From f598be30641485edaa531f42a867c3db5a09c430 Mon Sep 17 00:00:00 2001 From: Karthik110505 Date: Sun, 19 May 2024 10:56:44 +0530 Subject: [PATCH 1/6] adding .ipynb --- ...Pest_and_Disease_Detection_using_DL_.ipynb | 13083 ++++++++-------- 1 file changed, 6542 insertions(+), 6541 deletions(-) diff --git a/Crop Pest and Disease Detection using DL/Model/Crop_Pest_and_Disease_Detection_using_DL_.ipynb b/Crop Pest and Disease Detection using DL/Model/Crop_Pest_and_Disease_Detection_using_DL_.ipynb index 57e20d4c4..b2539c963 100644 --- a/Crop Pest and Disease Detection using DL/Model/Crop_Pest_and_Disease_Detection_using_DL_.ipynb +++ b/Crop Pest and Disease Detection using DL/Model/Crop_Pest_and_Disease_Detection_using_DL_.ipynb @@ -1,6604 +1,6605 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Note: This notebook is prepared in Google collab" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "IRYcu-UwzQAU" - }, - "source": [ - "# Loading Basic Libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "_ryJq3BVUI5F" - }, - "outputs": [], - "source": [ - "import os\n", - "import cv2\n", - "import tensorflow as tf\n", - "from tensorflow.keras import layers, models\n", - "import matplotlib.pyplot as plt\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "Mukt0YWFjy8U" - }, - "outputs": [], - "source": [ - "# Creaking dataset folder to download the dataset\n", - "mkdir dataset\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "OOTLURUXlxxL", - "outputId": "a34cf209-2f49-4270-978f-d51147037fe5" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/content/dataset\n" - ] - } - ], - "source": [ - "# changing current directory path\n", - "cd dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "QieK7HFzidPC", - "outputId": "3c9a5312-d30e-4950-eb6d-1913c3f56a79" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.6.14 / client 1.6.12)\n", - "Dataset URL: https://www.kaggle.com/datasets/nirmalsankalana/crop-pest-and-disease-detection\n", - "License(s): CC0-1.0\n", - "Downloading crop-pest-and-disease-detection.zip to /content/dataset\n", - "100% 1.24G/1.25G [00:46<00:00, 31.4MB/s]\n", - "100% 1.25G/1.25G [00:46<00:00, 29.0MB/s]\n" - ] - } - ], - "source": [ - "# Downloading the dataset\n", - "!kaggle datasets download -d nirmalsankalana/crop-pest-and-disease-detection" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "collapsed": true, - "id": "FhJ3tB5mjVe0", - "outputId": "8b6fff35-753b-441f-ecc9-4095b21eed32" - }, - "outputs": [ - { - "name": "stdout", - 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" inflating: Tomato verticulium wilt/verticulium wilt621_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt622_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt623_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt624_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt625_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt626_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt627_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt628_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt629_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt62_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt630_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt631_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt632_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt633_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt634_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt635_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt636_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt637_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt638_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt639_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt63_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt640_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt641_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt642_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt643_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt644_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt645_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt646_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt647_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt648_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt649_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt64_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt650_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt651_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt652_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt653_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt654_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt655_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt656_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt657_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt658_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt659_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt65_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt660_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt661_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt662_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt663_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt664_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt665_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt666_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt667_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt668_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt669_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt66_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt670_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt671_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt672_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt673_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt674_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt675_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt676_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt677_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt678_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt679_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt67_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt680_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt681_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt682_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt683_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt684_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt685_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt686_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt687_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt688_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt689_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt68_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt690_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt691_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt692_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt693_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt694_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt695_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt696_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt697_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt698_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt699_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt69_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt6_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt700_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt701_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt702_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt703_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt704_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt705_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt706_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt707_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt708_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt709_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt70_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt710_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt711_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt712_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt713_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt714_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt715_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt716_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt717_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt718_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt719_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt71_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt720_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt721_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt722_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt723_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt724_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt725_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt726_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt727_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt728_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt729_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt72_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt730_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt731_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt732_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt733_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt734_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt735_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt736_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt737_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt738_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt739_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt73_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt740_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt741_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt742_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt743_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt744_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt745_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt746_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt747_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt748_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt749_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt74_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt750_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt751_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt752_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt753_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt754_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt755_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt756_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt757_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt758_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt759_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt75_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt760_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt76_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt77_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt78_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt79_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt7_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt80_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt81_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt82_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt83_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt84_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt85_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt86_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt87_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt88_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt89_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt8_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt90_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt91_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt92_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt93_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt94_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt95_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt96_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt97_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt98_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt99_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt9_.jpg \n" - ] - } - ], - "source": [ - "# Unzipping the dataset\n", - "!unzip \\*.zip\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "4V88wjJT2_30" - }, - "outputs": [], - "source": [ - "# Navigating back to content directory\n", - "cd '../'" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WlsvA2X-UI5K" - }, - "source": [ - "# Some predifined functions" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "id": "27ZBrqgWUI5M" - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "def plot_graph(history):\n", - " fig, ax = plt.subplots(1, 2, figsize=(12, 5)) # Create a figure with 1 row and 2 columns\n", - "\n", - " # Plot training & validation accuracy values\n", - " ax[0].plot(history.history['accuracy'])\n", - " ax[0].plot(history.history['val_accuracy'])\n", - " ax[0].set_title('Model accuracy')\n", - " ax[0].set_ylabel('Accuracy')\n", - " ax[0].set_xlabel('Epoch')\n", - " ax[0].legend(['Train', 'Validation'], loc='upper left')\n", - "\n", - " # Plot training & validation loss values\n", - " ax[1].plot(history.history['loss'])\n", - " ax[1].plot(history.history['val_loss'])\n", - " ax[1].set_title('Model loss')\n", - " ax[1].set_ylabel('Loss')\n", - " ax[1].set_xlabel('Epoch')\n", - " ax[1].legend(['Train', 'Validation'], loc='upper left')\n", - "\n", - " plt.tight_layout()\n", - " plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "LOwqeXYpUI5N" - }, - "source": [ - "# Data Cleaning and filter valid image data" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Lc9naWsczqaA" - }, - "source": [ - "The given dataset contains some invalid images. The below code filters the dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "collapsed": true, - "id": "yj3Dw9mWUI5O", - "outputId": "ad848410-2b0e-4988-a53c-458d917fdfae" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 392 image files in dataset/Cashew gumosis\n", - "Copied 274 images to train/Cashew gumosis\n", - "Copied 78 images to test/Cashew gumosis\n", - "Copied 40 images to val/Cashew gumosis\n", - "Found 673 image files in dataset/Maize grasshoper\n", - "Copied 471 images to train/Maize grasshoper\n", - "Copied 134 images to test/Maize grasshoper\n", - "Copied 68 images to val/Maize grasshoper\n", - "Found 1368 image files in dataset/Cashew healthy\n", - "Copied 957 images to train/Cashew healthy\n", - "Copied 273 images to test/Cashew healthy\n", - "Copied 138 images to val/Cashew healthy\n", - "Found 1015 image files in dataset/Cassava green mite\n", - "Copied 710 images to train/Cassava green mite\n", - "Copied 203 images to test/Cassava green mite\n", - "Copied 102 images to val/Cassava green mite\n", - "Found 948 image files in dataset/Maize leaf beetle\n", - "Copied 663 images to train/Maize leaf beetle\n", - "Copied 189 images to test/Maize leaf beetle\n", - "Copied 96 images to val/Maize leaf beetle\n", - "Found 1205 image files in dataset/Cassava mosaic\n", - "Copied 843 images to train/Cassava mosaic\n", - "Copied 241 images to test/Cassava mosaic\n", - "Copied 121 images to val/Cassava mosaic\n", - "Found 1006 image files in dataset/Maize leaf blight\n", - "Copied 704 images to train/Maize leaf blight\n", - "Copied 201 images to test/Maize leaf blight\n", - "Copied 101 images to val/Maize leaf blight\n", - "Found 979 image files in dataset/Maize streak virus\n", - "Copied 685 images to train/Maize streak virus\n", - "Copied 195 images to test/Maize streak virus\n", - "Copied 99 images to val/Maize streak virus\n", - "Found 1259 image files in dataset/Maize leaf spot\n", - "Copied 881 images to train/Maize leaf spot\n", - "Copied 251 images to test/Maize leaf spot\n", - "Copied 127 images to val/Maize leaf spot\n", - "Found 285 image files in dataset/Maize fall armyworm\n", - "Copied 199 images to train/Maize fall armyworm\n", - "Copied 57 images to test/Maize fall armyworm\n", - "Copied 29 images to val/Maize fall armyworm\n", - "Found 470 image files in dataset/Tomato healthy\n", - "Copied 329 images to train/Tomato healthy\n", - "Copied 94 images to test/Tomato healthy\n", - "Copied 47 images to val/Tomato healthy\n", - "Found 1193 image files in dataset/Cassava healthy\n", - "Copied 835 images to train/Cassava healthy\n", - "Copied 238 images to test/Cassava healthy\n", - "Copied 120 images to val/Cassava healthy\n", - "Found 518 image files in dataset/Tomato leaf curl\n", - "Copied 362 images to train/Tomato leaf curl\n", - "Copied 103 images to test/Tomato leaf curl\n", - "Copied 53 images to val/Tomato leaf curl\n", - "Found 1301 image files in dataset/Tomato leaf blight\n", - "Copied 910 images to train/Tomato leaf blight\n", - "Copied 260 images to test/Tomato leaf blight\n", - "Copied 131 images to val/Tomato leaf blight\n", - "Found 1682 image files in dataset/Cashew red rust\n", - "Copied 1177 images to train/Cashew red rust\n", - "Copied 336 images to test/Cashew red rust\n", - "Copied 169 images to val/Cashew red rust\n", - "Found 1481 image files in dataset/Cassava brown spot\n", - "Copied 1036 images to train/Cassava brown spot\n", - "Copied 296 images to test/Cassava brown spot\n", - "Copied 149 images to val/Cassava brown spot\n", - "Found 773 image files in dataset/Tomato verticulium wilt\n", - "Copied 541 images to train/Tomato verticulium wilt\n", - "Copied 154 images to test/Tomato verticulium wilt\n", - "Copied 78 images to val/Tomato verticulium wilt\n", - "Found 1729 image files in dataset/Cashew anthracnose\n", - "Copied 1210 images to train/Cashew anthracnose\n", - "Copied 345 images to test/Cashew anthracnose\n", - "Copied 174 images to val/Cashew anthracnose\n", - "Found 2743 image files in dataset/Tomato septoria leaf spot\n", - "Copied 1920 images to train/Tomato septoria leaf spot\n", - "Copied 548 images to test/Tomato septoria leaf spot\n", - "Copied 275 images to val/Tomato septoria leaf spot\n", - "Found 1378 image files in dataset/Cashew leaf miner\n", - "Copied 964 images to train/Cashew leaf miner\n", - "Copied 275 images to test/Cashew leaf miner\n", - "Copied 139 images to val/Cashew leaf miner\n", - "Found 208 image files in dataset/Maize healthy\n", - "Copied 145 images to train/Maize healthy\n", - "Copied 41 images to test/Maize healthy\n", - "Copied 22 images to val/Maize healthy\n", - "Found 2614 image files in dataset/Cassava bacterial blight\n", - "Copied 1829 images to train/Cassava bacterial blight\n", - "Copied 522 images to test/Cassava bacterial blight\n", - "Copied 263 images to val/Cassava bacterial blight\n" - ] - } - ], - "source": [ - "import os\n", - "import random\n", - "import shutil\n", - "\n", - "# Set the source directory\n", - "src_dir = 'dataset'\n", - "\n", - "# Set the destination directories\n", - "train_dir = 'train'\n", - "test_dir = 'test'\n", - "val_dir = 'val'\n", - "\n", - "# Set the split ratios\n", - "train_ratio = 0.7\n", - "test_ratio = 0.2\n", - "val_ratio = 0.1\n", - "\n", - "# Create the destination directories if they don't exist\n", - "os.makedirs(train_dir, exist_ok=True)\n", - "os.makedirs(test_dir, exist_ok=True)\n", - "os.makedirs(val_dir, exist_ok=True)\n", - "\n", - "# Get a list of all subdirectories in the source directory\n", - "subdirs = [d for d in os.listdir(src_dir) if os.path.isdir(os.path.join(src_dir, d))]\n", - "\n", - "for subdir in subdirs:\n", - " subdir_path = os.path.join(src_dir, subdir)\n", - " image_files = [f for f in os.listdir(subdir_path) if f.endswith('.jpg') or f.endswith('.png')]\n", - "\n", - " print(f\"Found {len(image_files)} image files in {subdir_path}\")\n", - "\n", - " # If no image files found in the subdirectory, skip it\n", - " if not image_files:\n", - " print(f\"No image files found in {subdir_path}. Skipping...\")\n", - " continue\n", - "\n", - " # Shuffle the list of image files\n", - " random.shuffle(image_files)\n", - "\n", - " # Calculate the number of images for each set\n", - " num_images = len(image_files)\n", - " num_train = int(num_images * train_ratio)\n", - " num_test = int(num_images * test_ratio)\n", - " num_val = num_images - num_train - num_test\n", - "\n", - " # Create subdirectories for train, test, and val sets\n", - " train_subdir = os.path.join(train_dir, subdir)\n", - " test_subdir = os.path.join(test_dir, subdir)\n", - " val_subdir = os.path.join(val_dir, subdir)\n", - "\n", - " os.makedirs(train_subdir, exist_ok=True)\n", - " os.makedirs(test_subdir, exist_ok=True)\n", - " os.makedirs(val_subdir, exist_ok=True)\n", - "\n", - " # Copy the images to their respective sets\n", - " train_files = image_files[:num_train]\n", - " test_files = image_files[num_train:num_train+num_test]\n", - " val_files = image_files[num_train+num_test:]\n", - "\n", - " for file in train_files:\n", - " src_path = os.path.join(subdir_path, file)\n", - " dst_path = os.path.join(train_subdir, file)\n", - " shutil.copy(src_path, dst_path)\n", - "\n", - " for file in test_files:\n", - " src_path = os.path.join(subdir_path, file)\n", - " dst_path = os.path.join(test_subdir, file)\n", - " shutil.copy(src_path, dst_path)\n", - "\n", - " for file in val_files:\n", - " src_path = os.path.join(subdir_path, file)\n", - " dst_path = os.path.join(val_subdir, file)\n", - " shutil.copy(src_path, dst_path)\n", - "\n", - " print(f\"Copied {num_train} images to {train_subdir}\")\n", - " print(f\"Copied {num_test} images to {test_subdir}\")\n", - " print(f\"Copied {num_val} images to {val_subdir}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true, - "id": "KwzUAq8xUI5Q" - }, - "outputs": [], - "source": [ - "# Function to check if an image is valid using OpenCV\n", - "def is_image_valid(image_path):\n", - " try:\n", - " img = cv2.imread(image_path)\n", - " if img is None:\n", - " return False\n", - " return True\n", - " except:\n", - " return False\n", - "\n", - "# Function to filter out corrupted images\n", - "def filter_and_save_valid_images(source_dir, target_dir):\n", - " if not os.path.exists(target_dir):\n", - " os.makedirs(target_dir)\n", - "\n", - " for root, _, files in os.walk(source_dir):\n", - " for file in files:\n", - " file_path = os.path.join(root, file)\n", - " if is_image_valid(file_path):\n", - " relative_path = os.path.relpath(file_path, source_dir)\n", - " target_path = os.path.join(target_dir, relative_path)\n", - " target_subdir = os.path.dirname(target_path)\n", - " if not os.path.exists(target_subdir):\n", - " os.makedirs(target_subdir)\n", - " cv2.imwrite(target_path, cv2.imread(file_path))\n", - "\n", - "# Specify the source and target directories\n", - "train_source_dir = 'train'\n", - "train_target_dir = 'filtered_train'\n", - "val_source_dir = 'val'\n", - "val_target_dir = 'filtered_val'\n", - "test_source_dir = 'test'\n", - "test_target_dir = 'filtered_test'\n", - "\n", - "# Filter and save valid images\n", - "filter_and_save_valid_images(train_source_dir, train_target_dir)\n", - "filter_and_save_valid_images(val_source_dir, val_target_dir)\n", - "filter_and_save_valid_images(test_source_dir, test_target_dir)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "aKljc92wz7Es" - }, - "source": [ - "# Loading the dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "kIbRzkJ5UI5R", - "outputId": "a0820fa1-f15a-4f26-a72e-6c8350757de6" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 17605 files belonging to 22 classes.\n", - "Found 2538 files belonging to 22 classes.\n" - ] - } - ], - "source": [ - "\n", - "# Function to load and preprocess the dataset\n", - "def load_and_preprocess_dataset(directory, image_size=(150, 150), batch_size=32):\n", - " dataset = tf.keras.utils.image_dataset_from_directory(\n", - " directory=directory,\n", - " labels='inferred',\n", - " label_mode='categorical',\n", - " batch_size=batch_size,\n", - " image_size=image_size)\n", - "\n", - " class_names = dataset.class_names\n", - "\n", - " # Define a preprocessing function to scale the images\n", - " def preprocess(image, label):\n", - " # Rescale the image pixel values from [0, 255] to [0, 1]\n", - " image = tf.cast(image, tf.float32) / 255.0\n", - " return image, label\n", - "\n", - " # Apply the preprocessing function to the dataset\n", - " dataset = dataset.map(preprocess, num_parallel_calls=tf.data.AUTOTUNE)\n", - " return dataset, class_names\n", - "\n", - "# Load the datasets\n", - "train_ds, train_class_names = load_and_preprocess_dataset(train_target_dir)\n", - "val_ds, val_class_names = load_and_preprocess_dataset(val_target_dir)\n", - "\n", - "# Ensure the class names are consistent\n", - "assert train_class_names == val_class_names, \"Class names in training and validation datasets do not match.\"\n", - "\n", - "# Prefetching to improve performance\n", - "train_ds = train_ds.prefetch(buffer_size=tf.data.AUTOTUNE)\n", - "val_ds = val_ds.prefetch(buffer_size=tf.data.AUTOTUNE)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "3SStCzWbUI5S", - "outputId": "32ff7f88-d132-427a-d79a-40ae71c41ea9" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 5027 files belonging to 22 classes.\n" - ] - } - ], - "source": [ - "test_ds, test_class_names = load_and_preprocess_dataset(test_target_dir)\n", - "test_ds = test_ds.prefetch(buffer_size=tf.data.AUTOTUNE)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "GOC_HjrYUI5U" - }, - "source": [ - "# ANN model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "MvkyNR5CUI5V", - "outputId": "d44f1c8f-194e-40b6-ccd6-df8c1f68712c" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"sequential\"\n", - "_________________________________________________________________\n", - " Layer (type) Output Shape Param # \n", - "=================================================================\n", - " flatten (Flatten) (None, 67500) 0 \n", - " \n", - " dense (Dense) (None, 512) 34560512 \n", - " \n", - " dense_1 (Dense) (None, 256) 131328 \n", - " \n", - " dense_2 (Dense) (None, 22) 5654 \n", - " \n", - "=================================================================\n", - "Total params: 34697494 (132.36 MB)\n", - "Trainable params: 34697494 (132.36 MB)\n", - "Non-trainable params: 0 (0.00 Byte)\n", - "_________________________________________________________________\n" - ] - } - ], - "source": [ - "import tensorflow as tf\n", - "from tensorflow.keras import layers, models\n", - "ann_model = models.Sequential([\n", - " layers.Flatten(input_shape=(150,150,3)), # Input layer flattens the image\n", - " layers.Dense(512, activation='relu'), # Hidden layer with 512 units and ReLU activation\n", - " layers.Dense(256, activation='relu'), # Hidden layer with 256 units and ReLU activation\n", - " layers.Dense(22, activation='softmax') # Output layer with softmax activation for classification\n", - "])\n", - "\n", - "# Compile the model\n", - "ann_model.compile(optimizer='adam',\n", - " loss='categorical_crossentropy',\n", - " metrics=['accuracy'])\n", - "\n", - "ann_model.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "cWbWa40UUI5V", - "outputId": "f5c99b23-20d6-4298-e480-a68e572dad6c" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/10\n", - "551/551 [==============================] - 32s 56ms/step - loss: 2.5090 - accuracy: 0.2331 - val_loss: 2.4573 - val_accuracy: 0.2368\n", - "Epoch 2/10\n", - "551/551 [==============================] - 35s 62ms/step - loss: 2.2748 - accuracy: 0.2769 - val_loss: 2.1671 - val_accuracy: 0.3073\n", - "Epoch 3/10\n", - "551/551 [==============================] - 31s 55ms/step - loss: 2.1548 - accuracy: 0.3088 - val_loss: 2.1161 - val_accuracy: 0.3180\n", - "Epoch 4/10\n", - "551/551 [==============================] - 29s 51ms/step - loss: 2.0701 - accuracy: 0.3264 - val_loss: 2.0176 - val_accuracy: 0.3416\n", - "Epoch 5/10\n", - "551/551 [==============================] - 29s 52ms/step - loss: 2.0042 - accuracy: 0.3417 - val_loss: 2.0083 - val_accuracy: 0.3424\n", - "Epoch 6/10\n", - "551/551 [==============================] - 28s 51ms/step - loss: 1.9771 - accuracy: 0.3518 - val_loss: 1.9500 - val_accuracy: 0.3660\n", - "Epoch 7/10\n", - "551/551 [==============================] - 29s 51ms/step - loss: 1.9136 - accuracy: 0.3694 - val_loss: 1.8914 - val_accuracy: 0.3881\n", - "Epoch 8/10\n", - "551/551 [==============================] - 31s 56ms/step - loss: 1.8748 - accuracy: 0.3789 - val_loss: 2.0321 - val_accuracy: 0.3416\n", - "Epoch 9/10\n", - "551/551 [==============================] - 31s 56ms/step - loss: 1.8413 - accuracy: 0.3881 - val_loss: 1.9444 - val_accuracy: 0.3751\n", - "Epoch 10/10\n", - "551/551 [==============================] - 29s 51ms/step - loss: 1.8477 - accuracy: 0.3902 - val_loss: 1.8945 - val_accuracy: 0.3822\n" - ] - } - ], - "source": [ - "ann_history= ann_model.fit(train_ds,validation_data=val_ds,epochs=10)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 507 - }, - "id": "N1G8-LeCBU8d", - "outputId": "6d6717f7-e10d-4757-e619-71b63f3e2338" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_graph(ann_history)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "dlI2JELTUI5W" - }, - "source": [ - "# Basic CNN model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "WmkAduHdUI5W" - }, - "outputs": [], - "source": [ - "from keras.optimizers import Adam" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "O9R68g_lUI5W", - "outputId": "21d1cdb3-56ea-480d-e662-375c82003429" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"sequential_1\"\n", - "_________________________________________________________________\n", - " Layer (type) Output Shape Param # \n", - "=================================================================\n", - " conv2d (Conv2D) (None, 148, 148, 32) 896 \n", - " \n", - " batch_normalization (Batch (None, 148, 148, 32) 128 \n", - " Normalization) \n", - " \n", - " max_pooling2d (MaxPooling2 (None, 74, 74, 32) 0 \n", - " D) \n", - " \n", - " conv2d_1 (Conv2D) (None, 72, 72, 64) 18496 \n", - " \n", - " batch_normalization_1 (Bat (None, 72, 72, 64) 256 \n", - " chNormalization) \n", - " \n", - " conv2d_2 (Conv2D) (None, 70, 70, 64) 36928 \n", - " \n", - " batch_normalization_2 (Bat (None, 70, 70, 64) 256 \n", - " chNormalization) \n", - " \n", - " max_pooling2d_1 (MaxPoolin (None, 35, 35, 64) 0 \n", - " g2D) \n", - " \n", - " conv2d_3 (Conv2D) (None, 33, 33, 128) 73856 \n", - " \n", - " batch_normalization_3 (Bat (None, 33, 33, 128) 512 \n", - " chNormalization) \n", - " \n", - " max_pooling2d_2 (MaxPoolin (None, 16, 16, 128) 0 \n", - " g2D) \n", - " \n", - " conv2d_4 (Conv2D) (None, 14, 14, 128) 147584 \n", - " \n", - " batch_normalization_4 (Bat (None, 14, 14, 128) 512 \n", - " chNormalization) \n", - " \n", - " conv2d_5 (Conv2D) (None, 12, 12, 128) 147584 \n", - " \n", - " batch_normalization_5 (Bat (None, 12, 12, 128) 512 \n", - " chNormalization) \n", - " \n", - " max_pooling2d_3 (MaxPoolin (None, 6, 6, 128) 0 \n", - " g2D) \n", - " \n", - " flatten_1 (Flatten) (None, 4608) 0 \n", - " \n", - " dense_3 (Dense) (None, 1024) 4719616 \n", - " \n", - " batch_normalization_6 (Bat (None, 1024) 4096 \n", - " chNormalization) \n", - " \n", - " dropout (Dropout) (None, 1024) 0 \n", - " \n", - " dense_4 (Dense) (None, 22) 22550 \n", - " \n", - "=================================================================\n", - "Total params: 5173782 (19.74 MB)\n", - "Trainable params: 5170646 (19.72 MB)\n", - "Non-trainable params: 3136 (12.25 KB)\n", - "_________________________________________________________________\n" - ] - } - ], - "source": [ - "import tensorflow as tf\n", - "from tensorflow.keras import layers, models\n", - "\n", - "# Define the CNN model with batch normalization\n", - "cnn_model = models.Sequential([\n", - " layers.Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)),\n", - " layers.BatchNormalization(),\n", - " layers.MaxPooling2D((2, 2)),\n", - " layers.Conv2D(64, (3, 3), activation='relu'),\n", - " layers.BatchNormalization(),\n", - " layers.Conv2D(64, (3, 3), activation='relu'),\n", - " layers.BatchNormalization(),\n", - " layers.MaxPooling2D((2, 2)),\n", - " layers.Conv2D(128, (3, 3), activation='relu'),\n", - " layers.BatchNormalization(),\n", - " layers.MaxPooling2D((2, 2)),\n", - " layers.Conv2D(128, (3, 3), activation='relu'),\n", - " layers.BatchNormalization(),\n", - " layers.Conv2D(128, (3, 3), activation='relu'),\n", - " layers.BatchNormalization(),\n", - " layers.MaxPooling2D((2, 2)),\n", - " layers.Flatten(),\n", - " layers.Dense(1024, activation='relu'),\n", - " layers.BatchNormalization(),\n", - " layers.Dropout(0.75),\n", - " layers.Dense(22, activation='softmax')\n", - "])\n", - "\n", - "\n", - "# Compile the model\n", - "cnn_model.compile(optimizer='adam',\n", - " loss='categorical_crossentropy',\n", - " metrics=['accuracy'])\n", - "\n", - "# Display the model summary\n", - "cnn_model.summary()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "QjOA_sUCUI5W", - "outputId": "0b72dc7c-2df8-485f-8746-c999f31cb301" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/10\n", - "551/551 [==============================] - 46s 64ms/step - loss: 3.2072 - accuracy: 0.2810 - val_loss: 2.7506 - val_accuracy: 0.2116\n", - "Epoch 2/10\n", - "551/551 [==============================] - 35s 63ms/step - loss: 2.0666 - accuracy: 0.4187 - val_loss: 1.9861 - val_accuracy: 0.4283\n", - "Epoch 3/10\n", - "551/551 [==============================] - 36s 65ms/step - loss: 1.5801 - accuracy: 0.5168 - val_loss: 1.7026 - val_accuracy: 0.4803\n", - "Epoch 4/10\n", - "551/551 [==============================] - 37s 67ms/step - loss: 1.3267 - accuracy: 0.5757 - val_loss: 1.1292 - val_accuracy: 0.6340\n", - "Epoch 5/10\n", - "551/551 [==============================] - 38s 69ms/step - loss: 1.1433 - accuracy: 0.6254 - val_loss: 1.0762 - val_accuracy: 0.6466\n", - "Epoch 6/10\n", - "551/551 [==============================] - 35s 64ms/step - loss: 1.0291 - accuracy: 0.6573 - val_loss: 1.1473 - val_accuracy: 0.6399\n", - "Epoch 7/10\n", - "551/551 [==============================] - 35s 62ms/step - loss: 0.9183 - accuracy: 0.6904 - val_loss: 0.9613 - val_accuracy: 0.6848\n", - "Epoch 8/10\n", - "551/551 [==============================] - 36s 65ms/step - loss: 0.8195 - accuracy: 0.7247 - val_loss: 1.1618 - val_accuracy: 0.6584\n", - "Epoch 9/10\n", - "551/551 [==============================] - 36s 65ms/step - loss: 0.7272 - accuracy: 0.7481 - val_loss: 0.9426 - val_accuracy: 0.7104\n", - "Epoch 10/10\n", - "551/551 [==============================] - 36s 65ms/step - loss: 0.6606 - accuracy: 0.7719 - val_loss: 1.0112 - val_accuracy: 0.6899\n" - ] - } - ], - "source": [ - "cnn_history = cnn_model.fit(\n", - " train_ds,\n", - " validation_data=val_ds,\n", - "\n", - " epochs=10 # Adjust the number of epochs based on your requirements\n", - ")\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 507 - }, - "id": "09A0ihQVUI5X", - "outputId": "29a4f519-ff28-41f9-b2e6-6de784815841" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_graph(cnn_history)" - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Note: This notebook is prepared in Google collab" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IRYcu-UwzQAU" + }, + "source": [ + "# Loading Basic Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "_ryJq3BVUI5F" + }, + "outputs": [], + "source": [ + "import os\n", + "import cv2\n", + "import tensorflow as tf\n", + "from tensorflow.keras import layers, models\n", + "import matplotlib.pyplot as plt\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "Mukt0YWFjy8U" + }, + "outputs": [], + "source": [ + "# Creaking dataset folder to download the dataset\n", + "mkdir dataset\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "OOTLURUXlxxL", + "outputId": "a34cf209-2f49-4270-978f-d51147037fe5" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "edlmW0YVUI5b" - }, - "source": [ - "# Resnet(Basic)\n" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "/content/dataset\n" + ] + } + ], + "source": [ + "# changing current directory path\n", + "cd dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "QieK7HFzidPC", + "outputId": "3c9a5312-d30e-4950-eb6d-1913c3f56a79" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "id": "Ggk2xWRSsy-3" - }, - "outputs": [], - "source": [ - "import tensorflow as tf\n", - "from tensorflow.keras.applications import ResNet50\n", - "from tensorflow.keras.models import Model\n", - "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D , Dropout\n", - "from tensorflow.keras.optimizers import Adam\n" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.6.14 / client 1.6.12)\n", + "Dataset URL: https://www.kaggle.com/datasets/nirmalsankalana/crop-pest-and-disease-detection\n", + "License(s): CC0-1.0\n", + "Downloading crop-pest-and-disease-detection.zip to /content/dataset\n", + "100% 1.24G/1.25G [00:46<00:00, 31.4MB/s]\n", + "100% 1.25G/1.25G [00:46<00:00, 29.0MB/s]\n" + ] + } + ], + "source": [ + "# Downloading the dataset\n", + "!kaggle datasets download -d nirmalsankalana/crop-pest-and-disease-detection" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "collapsed": true, + "id": "FhJ3tB5mjVe0", + "outputId": "8b6fff35-753b-441f-ecc9-4095b21eed32" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "bYBGWOAPsL1k", - "outputId": "5ff7de24-5683-44dc-a959-e4b69a9b11eb" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n", - "94765736/94765736 [==============================] - 3s 0us/step\n" - ] - } - ], - "source": [ - "base_model = ResNet50(weights='imagenet', include_top=False)\n", - "\n", - "for layer in base_model.layers[-10:]:\n", - " layer.trainable = True\n", - "\n", - "# Add new top layers\n", - "x = base_model.output\n", - "x = GlobalAveragePooling2D()(x)\n", - "x = Dense(1024, activation='relu')(x)\n", - "predictions = Dense(22, activation='softmax')(x)\n", - "\n", - "res_model = Model(inputs=base_model.input, outputs=predictions)\n", - "\n", - "\n", - "# Re-compile the model\n", - "res_model.compile(optimizer=Adam(learning_rate=1e-5),\n", - " loss='categorical_crossentropy',\n", - " metrics=['accuracy'])\n" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n", + " inflating: Tomato leaf blight/leaf blight1302_.jpg \n", + " inflating: Tomato leaf blight/leaf blight1303_.jpg \n", + " inflating: Tomato leaf blight/leaf blight1304_.jpg \n", + " inflating: Tomato leaf blight/leaf blight1305_.jpg \n", + " inflating: Tomato leaf blight/leaf blight1306_.jpg \n", + " inflating: Tomato leaf blight/leaf blight1307_.jpg \n", + " inflating: Tomato leaf blight/leaf blight1308_.jpg \n", + " inflating: Tomato leaf blight/leaf blight1309_.jpg \n", + " inflating: Tomato leaf blight/leaf blight130_.jpg \n", + " inflating: Tomato leaf blight/leaf blight1310_.jpg \n", + " inflating: Tomato leaf blight/leaf blight1311_.jpg \n", + " inflating: Tomato leaf blight/leaf blight1312_.jpg \n", + " inflating: Tomato leaf blight/leaf blight1313_.jpg \n", + " 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wilt737_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt738_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt739_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt73_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt740_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt741_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt742_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt743_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt744_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt745_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt746_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt747_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt748_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt749_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt74_.jpg \n", + " inflating: 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wilt78_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt79_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt7_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt80_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt81_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt82_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt83_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt84_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt85_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt86_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt87_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt88_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt89_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt8_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt90_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt91_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt92_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt93_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt94_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt95_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt96_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt97_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt98_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt99_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt9_.jpg \n" + ] + } + ], + "source": [ + "# Unzipping the dataset\n", + "!unzip \\*.zip\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4V88wjJT2_30" + }, + "outputs": [], + "source": [ + "# Navigating back to content directory\n", + "cd '../'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WlsvA2X-UI5K" + }, + "source": [ + "# Some predifined functions" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "27ZBrqgWUI5M" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "def plot_graph(history):\n", + " fig, ax = plt.subplots(1, 2, figsize=(12, 5)) # Create a figure with 1 row and 2 columns\n", + "\n", + " # Plot training & validation accuracy values\n", + " ax[0].plot(history.history['accuracy'])\n", + " ax[0].plot(history.history['val_accuracy'])\n", + " ax[0].set_title('Model accuracy')\n", + " ax[0].set_ylabel('Accuracy')\n", + " ax[0].set_xlabel('Epoch')\n", + " ax[0].legend(['Train', 'Validation'], loc='upper left')\n", + "\n", + " # Plot training & validation loss values\n", + " ax[1].plot(history.history['loss'])\n", + " ax[1].plot(history.history['val_loss'])\n", + " ax[1].set_title('Model loss')\n", + " ax[1].set_ylabel('Loss')\n", + " ax[1].set_xlabel('Epoch')\n", + " ax[1].legend(['Train', 'Validation'], loc='upper left')\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LOwqeXYpUI5N" + }, + "source": [ + "# Data Cleaning and filter valid image data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Lc9naWsczqaA" + }, + "source": [ + "The given dataset contains some invalid images. The below code filters the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "collapsed": true, + "id": "yj3Dw9mWUI5O", + "outputId": "ad848410-2b0e-4988-a53c-458d917fdfae" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "6ANqeRP8tWV4", - "outputId": "5091ac68-6b19-4668-8584-3b046dd64b7e" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/10\n", - "551/551 [==============================] - 150s 201ms/step - loss: 1.3461 - accuracy: 0.6010 - val_loss: 3.0756 - val_accuracy: 0.0867\n", - "Epoch 2/10\n", - "551/551 [==============================] - 104s 188ms/step - loss: 0.5116 - accuracy: 0.8258 - val_loss: 0.5763 - val_accuracy: 0.8042\n", - "Epoch 3/10\n", - "551/551 [==============================] - 110s 200ms/step - loss: 0.3162 - accuracy: 0.8965 - val_loss: 0.4449 - val_accuracy: 0.8479\n", - "Epoch 4/10\n", - "551/551 [==============================] - 104s 189ms/step - loss: 0.2023 - accuracy: 0.9386 - val_loss: 0.4379 - val_accuracy: 0.8522\n", - "Epoch 5/10\n", - "551/551 [==============================] - 104s 187ms/step - loss: 0.1295 - accuracy: 0.9632 - val_loss: 0.4506 - val_accuracy: 0.8542\n", - "Epoch 6/10\n", - "551/551 [==============================] - 104s 187ms/step - loss: 0.0870 - accuracy: 0.9776 - val_loss: 0.4571 - val_accuracy: 0.8601\n", - "Epoch 7/10\n", - "551/551 [==============================] - 104s 188ms/step - loss: 0.0579 - accuracy: 0.9848 - val_loss: 0.4730 - val_accuracy: 0.8562\n", - "Epoch 8/10\n", - "551/551 [==============================] - 104s 189ms/step - loss: 0.0477 - accuracy: 0.9855 - val_loss: 0.5126 - val_accuracy: 0.8617\n", - "Epoch 9/10\n", - "551/551 [==============================] - 104s 188ms/step - loss: 0.0387 - accuracy: 0.9878 - val_loss: 0.5597 - val_accuracy: 0.8491\n", - "Epoch 10/10\n", - "551/551 [==============================] - 104s 188ms/step - loss: 0.0378 - accuracy: 0.9864 - val_loss: 0.5381 - val_accuracy: 0.8495\n" - ] - } - ], - "source": [ - "resnet_history= res_model.fit(train_ds,validation_data=val_ds,epochs=10)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 392 image files in dataset/Cashew gumosis\n", + "Copied 274 images to train/Cashew gumosis\n", + "Copied 78 images to test/Cashew gumosis\n", + "Copied 40 images to val/Cashew gumosis\n", + "Found 673 image files in dataset/Maize grasshoper\n", + "Copied 471 images to train/Maize grasshoper\n", + "Copied 134 images to test/Maize grasshoper\n", + "Copied 68 images to val/Maize grasshoper\n", + "Found 1368 image files in dataset/Cashew healthy\n", + "Copied 957 images to train/Cashew healthy\n", + "Copied 273 images to test/Cashew healthy\n", + "Copied 138 images to val/Cashew healthy\n", + "Found 1015 image files in dataset/Cassava green mite\n", + "Copied 710 images to train/Cassava green mite\n", + "Copied 203 images to test/Cassava green mite\n", + "Copied 102 images to val/Cassava green mite\n", + "Found 948 image files in dataset/Maize leaf beetle\n", + "Copied 663 images to train/Maize leaf beetle\n", + "Copied 189 images to test/Maize leaf beetle\n", + "Copied 96 images to val/Maize leaf beetle\n", + "Found 1205 image files in dataset/Cassava mosaic\n", + "Copied 843 images to train/Cassava mosaic\n", + "Copied 241 images to test/Cassava mosaic\n", + "Copied 121 images to val/Cassava mosaic\n", + "Found 1006 image files in dataset/Maize leaf blight\n", + "Copied 704 images to train/Maize leaf blight\n", + "Copied 201 images to test/Maize leaf blight\n", + "Copied 101 images to val/Maize leaf blight\n", + "Found 979 image files in dataset/Maize streak virus\n", + "Copied 685 images to train/Maize streak virus\n", + "Copied 195 images to test/Maize streak virus\n", + "Copied 99 images to val/Maize streak virus\n", + "Found 1259 image files in dataset/Maize leaf spot\n", + "Copied 881 images to train/Maize leaf spot\n", + "Copied 251 images to test/Maize leaf spot\n", + "Copied 127 images to val/Maize leaf spot\n", + "Found 285 image files in dataset/Maize fall armyworm\n", + "Copied 199 images to train/Maize fall armyworm\n", + "Copied 57 images to test/Maize fall armyworm\n", + "Copied 29 images to val/Maize fall armyworm\n", + "Found 470 image files in dataset/Tomato healthy\n", + "Copied 329 images to train/Tomato healthy\n", + "Copied 94 images to test/Tomato healthy\n", + "Copied 47 images to val/Tomato healthy\n", + "Found 1193 image files in dataset/Cassava healthy\n", + "Copied 835 images to train/Cassava healthy\n", + "Copied 238 images to test/Cassava healthy\n", + "Copied 120 images to val/Cassava healthy\n", + "Found 518 image files in dataset/Tomato leaf curl\n", + "Copied 362 images to train/Tomato leaf curl\n", + "Copied 103 images to test/Tomato leaf curl\n", + "Copied 53 images to val/Tomato leaf curl\n", + "Found 1301 image files in dataset/Tomato leaf blight\n", + "Copied 910 images to train/Tomato leaf blight\n", + "Copied 260 images to test/Tomato leaf blight\n", + "Copied 131 images to val/Tomato leaf blight\n", + "Found 1682 image files in dataset/Cashew red rust\n", + "Copied 1177 images to train/Cashew red rust\n", + "Copied 336 images to test/Cashew red rust\n", + "Copied 169 images to val/Cashew red rust\n", + "Found 1481 image files in dataset/Cassava brown spot\n", + "Copied 1036 images to train/Cassava brown spot\n", + "Copied 296 images to test/Cassava brown spot\n", + "Copied 149 images to val/Cassava brown spot\n", + "Found 773 image files in dataset/Tomato verticulium wilt\n", + "Copied 541 images to train/Tomato verticulium wilt\n", + "Copied 154 images to test/Tomato verticulium wilt\n", + "Copied 78 images to val/Tomato verticulium wilt\n", + "Found 1729 image files in dataset/Cashew anthracnose\n", + "Copied 1210 images to train/Cashew anthracnose\n", + "Copied 345 images to test/Cashew anthracnose\n", + "Copied 174 images to val/Cashew anthracnose\n", + "Found 2743 image files in dataset/Tomato septoria leaf spot\n", + "Copied 1920 images to train/Tomato septoria leaf spot\n", + "Copied 548 images to test/Tomato septoria leaf spot\n", + "Copied 275 images to val/Tomato septoria leaf spot\n", + "Found 1378 image files in dataset/Cashew leaf miner\n", + "Copied 964 images to train/Cashew leaf miner\n", + "Copied 275 images to test/Cashew leaf miner\n", + "Copied 139 images to val/Cashew leaf miner\n", + "Found 208 image files in dataset/Maize healthy\n", + "Copied 145 images to train/Maize healthy\n", + "Copied 41 images to test/Maize healthy\n", + "Copied 22 images to val/Maize healthy\n", + "Found 2614 image files in dataset/Cassava bacterial blight\n", + "Copied 1829 images to train/Cassava bacterial blight\n", + "Copied 522 images to test/Cassava bacterial blight\n", + "Copied 263 images to val/Cassava bacterial blight\n" + ] + } + ], + "source": [ + "import os\n", + "import random\n", + "import shutil\n", + "\n", + "# Set the source directory\n", + "src_dir = 'dataset'\n", + "\n", + "# Set the destination directories\n", + "train_dir = 'train'\n", + "test_dir = 'test'\n", + "val_dir = 'val'\n", + "\n", + "# Set the split ratios\n", + "train_ratio = 0.7\n", + "test_ratio = 0.2\n", + "val_ratio = 0.1\n", + "\n", + "# Create the destination directories if they don't exist\n", + "os.makedirs(train_dir, exist_ok=True)\n", + "os.makedirs(test_dir, exist_ok=True)\n", + "os.makedirs(val_dir, exist_ok=True)\n", + "\n", + "# Get a list of all subdirectories in the source directory\n", + "subdirs = [d for d in os.listdir(src_dir) if os.path.isdir(os.path.join(src_dir, d))]\n", + "\n", + "for subdir in subdirs:\n", + " subdir_path = os.path.join(src_dir, subdir)\n", + " image_files = [f for f in os.listdir(subdir_path) if f.endswith('.jpg') or f.endswith('.png')]\n", + "\n", + " print(f\"Found {len(image_files)} image files in {subdir_path}\")\n", + "\n", + " # If no image files found in the subdirectory, skip it\n", + " if not image_files:\n", + " print(f\"No image files found in {subdir_path}. Skipping...\")\n", + " continue\n", + "\n", + " # Shuffle the list of image files\n", + " random.shuffle(image_files)\n", + "\n", + " # Calculate the number of images for each set\n", + " num_images = len(image_files)\n", + " num_train = int(num_images * train_ratio)\n", + " num_test = int(num_images * test_ratio)\n", + " num_val = num_images - num_train - num_test\n", + "\n", + " # Create subdirectories for train, test, and val sets\n", + " train_subdir = os.path.join(train_dir, subdir)\n", + " test_subdir = os.path.join(test_dir, subdir)\n", + " val_subdir = os.path.join(val_dir, subdir)\n", + "\n", + " os.makedirs(train_subdir, exist_ok=True)\n", + " os.makedirs(test_subdir, exist_ok=True)\n", + " os.makedirs(val_subdir, exist_ok=True)\n", + "\n", + " # Copy the images to their respective sets\n", + " train_files = image_files[:num_train]\n", + " test_files = image_files[num_train:num_train+num_test]\n", + " val_files = image_files[num_train+num_test:]\n", + "\n", + " for file in train_files:\n", + " src_path = os.path.join(subdir_path, file)\n", + " dst_path = os.path.join(train_subdir, file)\n", + " shutil.copy(src_path, dst_path)\n", + "\n", + " for file in test_files:\n", + " src_path = os.path.join(subdir_path, file)\n", + " dst_path = os.path.join(test_subdir, file)\n", + " shutil.copy(src_path, dst_path)\n", + "\n", + " for file in val_files:\n", + " src_path = os.path.join(subdir_path, file)\n", + " dst_path = os.path.join(val_subdir, file)\n", + " shutil.copy(src_path, dst_path)\n", + "\n", + " print(f\"Copied {num_train} images to {train_subdir}\")\n", + " print(f\"Copied {num_test} images to {test_subdir}\")\n", + " print(f\"Copied {num_val} images to {val_subdir}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true, + "id": "KwzUAq8xUI5Q" + }, + "outputs": [], + "source": [ + "# Function to check if an image is valid using OpenCV\n", + "def is_image_valid(image_path):\n", + " try:\n", + " img = cv2.imread(image_path)\n", + " if img is None:\n", + " return False\n", + " return True\n", + " except:\n", + " return False\n", + "\n", + "# Function to filter out corrupted images\n", + "def filter_and_save_valid_images(source_dir, target_dir):\n", + " if not os.path.exists(target_dir):\n", + " os.makedirs(target_dir)\n", + "\n", + " for root, _, files in os.walk(source_dir):\n", + " for file in files:\n", + " file_path = os.path.join(root, file)\n", + " if is_image_valid(file_path):\n", + " relative_path = os.path.relpath(file_path, source_dir)\n", + " target_path = os.path.join(target_dir, relative_path)\n", + " target_subdir = os.path.dirname(target_path)\n", + " if not os.path.exists(target_subdir):\n", + " os.makedirs(target_subdir)\n", + " cv2.imwrite(target_path, cv2.imread(file_path))\n", + "\n", + "# Specify the source and target directories\n", + "train_source_dir = 'train'\n", + "train_target_dir = 'filtered_train'\n", + "val_source_dir = 'val'\n", + "val_target_dir = 'filtered_val'\n", + "test_source_dir = 'test'\n", + "test_target_dir = 'filtered_test'\n", + "\n", + "# Filter and save valid images\n", + "filter_and_save_valid_images(train_source_dir, train_target_dir)\n", + "filter_and_save_valid_images(val_source_dir, val_target_dir)\n", + "filter_and_save_valid_images(test_source_dir, test_target_dir)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aKljc92wz7Es" + }, + "source": [ + "# Loading the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "kIbRzkJ5UI5R", + "outputId": "a0820fa1-f15a-4f26-a72e-6c8350757de6" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 41, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 468 - }, - "id": "7QLWlY1dvfUU", - "outputId": "5d0f74ca-f30f-47c7-db63-ec68d0c1de13" - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_graph(resnet_history)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 17605 files belonging to 22 classes.\n", + "Found 2538 files belonging to 22 classes.\n" + ] + } + ], + "source": [ + "\n", + "# Function to load and preprocess the dataset\n", + "def load_and_preprocess_dataset(directory, image_size=(150, 150), batch_size=32):\n", + " dataset = tf.keras.utils.image_dataset_from_directory(\n", + " directory=directory,\n", + " labels='inferred',\n", + " label_mode='categorical',\n", + " batch_size=batch_size,\n", + " image_size=image_size)\n", + "\n", + " class_names = dataset.class_names\n", + "\n", + " # Define a preprocessing function to scale the images\n", + " def preprocess(image, label):\n", + " # Rescale the image pixel values from [0, 255] to [0, 1]\n", + " image = tf.cast(image, tf.float32) / 255.0\n", + " return image, label\n", + "\n", + " # Apply the preprocessing function to the dataset\n", + " dataset = dataset.map(preprocess, num_parallel_calls=tf.data.AUTOTUNE)\n", + " return dataset, class_names\n", + "\n", + "# Load the datasets\n", + "train_ds, train_class_names = load_and_preprocess_dataset(train_target_dir)\n", + "val_ds, val_class_names = load_and_preprocess_dataset(val_target_dir)\n", + "\n", + "# Ensure the class names are consistent\n", + "assert train_class_names == val_class_names, \"Class names in training and validation datasets do not match.\"\n", + "\n", + "# Prefetching to improve performance\n", + "train_ds = train_ds.prefetch(buffer_size=tf.data.AUTOTUNE)\n", + "val_ds = val_ds.prefetch(buffer_size=tf.data.AUTOTUNE)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "3SStCzWbUI5S", + "outputId": "32ff7f88-d132-427a-d79a-40ae71c41ea9" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "WGcdJoLHvjxd" - }, - "source": [ - "# ResNet(Perfect) with Data Augumentaion and Regularizations" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 5027 files belonging to 22 classes.\n" + ] + } + ], + "source": [ + "test_ds, test_class_names = load_and_preprocess_dataset(test_target_dir)\n", + "test_ds = test_ds.prefetch(buffer_size=tf.data.AUTOTUNE)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GOC_HjrYUI5U" + }, + "source": [ + "# ANN model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "MvkyNR5CUI5V", + "outputId": "d44f1c8f-194e-40b6-ccd6-df8c1f68712c" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "6kfaFjzVemsj", - "outputId": "6cff68f7-0fe9-460a-955f-97608240456a" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 17605 images belonging to 22 classes.\n", - "Found 2538 images belonging to 22 classes.\n" - ] - } - ], - "source": [ - "# Data augmentaion\n", - "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", - "\n", - "datagen = ImageDataGenerator(\n", - " rotation_range=20,\n", - " width_shift_range=0.2,\n", - " height_shift_range=0.2,\n", - " shear_range=0.2,\n", - " zoom_range=0.2,\n", - " horizontal_flip=True,\n", - " fill_mode='nearest'\n", - ")\n", - "\n", - "train_generator = datagen.flow_from_directory(\n", - " 'filtered_train',\n", - " target_size=(150,150),\n", - " batch_size=32,\n", - " class_mode='categorical'\n", - ")\n", - "\n", - "val_generator = datagen.flow_from_directory(\n", - " 'filtered_val',\n", - " target_size=(150, 150),\n", - " batch_size=32,\n", - " class_mode='categorical'\n", - ")\n" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " flatten (Flatten) (None, 67500) 0 \n", + " \n", + " dense (Dense) (None, 512) 34560512 \n", + " \n", + " dense_1 (Dense) (None, 256) 131328 \n", + " \n", + " dense_2 (Dense) (None, 22) 5654 \n", + " \n", + "=================================================================\n", + "Total params: 34697494 (132.36 MB)\n", + "Trainable params: 34697494 (132.36 MB)\n", + "Non-trainable params: 0 (0.00 Byte)\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "from tensorflow.keras import layers, models\n", + "ann_model = models.Sequential([\n", + " layers.Flatten(input_shape=(150,150,3)), # Input layer flattens the image\n", + " layers.Dense(512, activation='relu'), # Hidden layer with 512 units and ReLU activation\n", + " layers.Dense(256, activation='relu'), # Hidden layer with 256 units and ReLU activation\n", + " layers.Dense(22, activation='softmax') # Output layer with softmax activation for classification\n", + "])\n", + "\n", + "# Compile the model\n", + "ann_model.compile(optimizer='adam',\n", + " loss='categorical_crossentropy',\n", + " metrics=['accuracy'])\n", + "\n", + "ann_model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "cWbWa40UUI5V", + "outputId": "f5c99b23-20d6-4298-e480-a68e572dad6c" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "id": "AsmRa-MUfsCs" - }, - "outputs": [], - "source": [ - "from tensorflow.keras.applications import ResNet50\n", - "from tensorflow.keras.layers import GlobalAveragePooling2D, Dense, Dropout\n", - "from tensorflow.keras.models import Model\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.regularizers import l2\n", - "from tensorflow.keras.callbacks import EarlyStopping\n" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "551/551 [==============================] - 32s 56ms/step - loss: 2.5090 - accuracy: 0.2331 - val_loss: 2.4573 - val_accuracy: 0.2368\n", + "Epoch 2/10\n", + "551/551 [==============================] - 35s 62ms/step - loss: 2.2748 - accuracy: 0.2769 - val_loss: 2.1671 - val_accuracy: 0.3073\n", + "Epoch 3/10\n", + "551/551 [==============================] - 31s 55ms/step - loss: 2.1548 - accuracy: 0.3088 - val_loss: 2.1161 - val_accuracy: 0.3180\n", + "Epoch 4/10\n", + "551/551 [==============================] - 29s 51ms/step - loss: 2.0701 - accuracy: 0.3264 - val_loss: 2.0176 - val_accuracy: 0.3416\n", + "Epoch 5/10\n", + "551/551 [==============================] - 29s 52ms/step - loss: 2.0042 - accuracy: 0.3417 - val_loss: 2.0083 - val_accuracy: 0.3424\n", + "Epoch 6/10\n", + "551/551 [==============================] - 28s 51ms/step - loss: 1.9771 - accuracy: 0.3518 - val_loss: 1.9500 - val_accuracy: 0.3660\n", + "Epoch 7/10\n", + "551/551 [==============================] - 29s 51ms/step - loss: 1.9136 - accuracy: 0.3694 - val_loss: 1.8914 - val_accuracy: 0.3881\n", + "Epoch 8/10\n", + "551/551 [==============================] - 31s 56ms/step - loss: 1.8748 - accuracy: 0.3789 - val_loss: 2.0321 - val_accuracy: 0.3416\n", + "Epoch 9/10\n", + "551/551 [==============================] - 31s 56ms/step - loss: 1.8413 - accuracy: 0.3881 - val_loss: 1.9444 - val_accuracy: 0.3751\n", + "Epoch 10/10\n", + "551/551 [==============================] - 29s 51ms/step - loss: 1.8477 - accuracy: 0.3902 - val_loss: 1.8945 - val_accuracy: 0.3822\n" + ] + } + ], + "source": [ + "ann_history= ann_model.fit(train_ds,validation_data=val_ds,epochs=10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 507 }, + "id": "N1G8-LeCBU8d", + "outputId": "6d6717f7-e10d-4757-e619-71b63f3e2338" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "h7_q9fbffbgd", - "outputId": "065ed344-07fa-4bfe-de27-62018da4e819" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/5\n", - "551/551 [==============================] - 218s 342ms/step - loss: 16.7730 - accuracy: 0.2183 - val_loss: 15.0907 - val_accuracy: 0.5839\n", - "Epoch 2/5\n", - "551/551 [==============================] - 187s 339ms/step - loss: 15.0545 - accuracy: 0.5087 - val_loss: 14.0818 - val_accuracy: 0.7206\n", - "Epoch 3/5\n", - "551/551 [==============================] - 187s 339ms/step - loss: 14.0944 - accuracy: 0.6330 - val_loss: 13.3983 - val_accuracy: 0.7545\n", - "Epoch 4/5\n", - "551/551 [==============================] - 189s 343ms/step - loss: 13.3619 - accuracy: 0.6914 - val_loss: 12.7492 - val_accuracy: 0.7888\n", - "Epoch 5/5\n", - "551/551 [==============================] - 192s 349ms/step - loss: 12.6688 - accuracy: 0.7296 - val_loss: 12.1332 - val_accuracy: 0.8081\n" - ] - } - ], - "source": [ - "# Load the base model\n", - "base_model = ResNet50(weights='imagenet', include_top=False)\n", - "\n", - "# Make the last 10 layers trainable\n", - "for layer in base_model.layers[-10:]:\n", - " layer.trainable = True\n", - "\n", - "# Add new top layers\n", - "x = base_model.output\n", - "x = GlobalAveragePooling2D()(x)\n", - "x = Dropout(0.5)(x) # Adding dropout for regularization\n", - "x = Dense(1024, activation='relu', kernel_regularizer=l2(0.01))(x)\n", - "x = Dropout(0.5)(x) # Adding another dropout layer for regularization\n", - "predictions = Dense(22, activation='softmax', kernel_regularizer=l2(0.01))(x)\n", - "\n", - "res_model_perfect = Model(inputs=base_model.input, outputs=predictions)\n", - "\n", - "# Re-compile the model\n", - "res_model_perfect.compile(optimizer=Adam(learning_rate=1e-5),\n", - " loss='categorical_crossentropy',\n", - " metrics=['accuracy'])\n", - "\n", - "# Early stopping callback\n", - "early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n", - "\n", - "# Train the model\n", - "res_model_perfect_history = res_model_perfect.fit(train_generator, epochs=5, validation_data=val_generator, callbacks=[early_stopping])\n" + "data": { + "image/png": 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", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_graph(ann_history)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dlI2JELTUI5W" + }, + "source": [ + "# Basic CNN model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "WmkAduHdUI5W" + }, + "outputs": [], + "source": [ + "from keras.optimizers import Adam" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "O9R68g_lUI5W", + "outputId": "21d1cdb3-56ea-480d-e662-375c82003429" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "rottw-JQjOq4", - "outputId": "275497e0-d72c-400c-d5e7-b777665d7d68" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 6/10\n", - "551/551 [==============================] - 189s 344ms/step - loss: 11.9784 - accuracy: 0.7541 - val_loss: 11.4686 - val_accuracy: 0.8282\n", - "Epoch 7/10\n", - "551/551 [==============================] - 189s 342ms/step - loss: 11.3271 - accuracy: 0.7718 - val_loss: 10.8296 - val_accuracy: 0.8294\n", - "Epoch 8/10\n", - "551/551 [==============================] - 191s 347ms/step - loss: 10.6584 - accuracy: 0.7876 - val_loss: 10.2008 - val_accuracy: 0.8373\n", - "Epoch 9/10\n", - "551/551 [==============================] - 192s 347ms/step - loss: 10.0059 - accuracy: 0.8040 - val_loss: 9.6002 - val_accuracy: 0.8408\n", - "Epoch 10/10\n", - "551/551 [==============================] - 194s 352ms/step - loss: 9.3686 - accuracy: 0.8173 - val_loss: 8.9783 - val_accuracy: 0.8455\n" - ] - } - ], - "source": [ - "res_model_perfect_history1 = res_model_perfect.fit(train_generator, initial_epoch=5,epochs=10, validation_data=val_generator, callbacks=[early_stopping])" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_1\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " conv2d (Conv2D) (None, 148, 148, 32) 896 \n", + " \n", + " batch_normalization (Batch (None, 148, 148, 32) 128 \n", + " Normalization) \n", + " \n", + " max_pooling2d (MaxPooling2 (None, 74, 74, 32) 0 \n", + " D) \n", + " \n", + " conv2d_1 (Conv2D) (None, 72, 72, 64) 18496 \n", + " \n", + " batch_normalization_1 (Bat (None, 72, 72, 64) 256 \n", + " chNormalization) \n", + " \n", + " conv2d_2 (Conv2D) (None, 70, 70, 64) 36928 \n", + " \n", + " batch_normalization_2 (Bat (None, 70, 70, 64) 256 \n", + " chNormalization) \n", + " \n", + " max_pooling2d_1 (MaxPoolin (None, 35, 35, 64) 0 \n", + " g2D) \n", + " \n", + " conv2d_3 (Conv2D) (None, 33, 33, 128) 73856 \n", + " \n", + " batch_normalization_3 (Bat (None, 33, 33, 128) 512 \n", + " chNormalization) \n", + " \n", + " max_pooling2d_2 (MaxPoolin (None, 16, 16, 128) 0 \n", + " g2D) \n", + " \n", + " conv2d_4 (Conv2D) (None, 14, 14, 128) 147584 \n", + " \n", + " batch_normalization_4 (Bat (None, 14, 14, 128) 512 \n", + " chNormalization) \n", + " \n", + " conv2d_5 (Conv2D) (None, 12, 12, 128) 147584 \n", + " \n", + " batch_normalization_5 (Bat (None, 12, 12, 128) 512 \n", + " chNormalization) \n", + " \n", + " max_pooling2d_3 (MaxPoolin (None, 6, 6, 128) 0 \n", + " g2D) \n", + " \n", + " flatten_1 (Flatten) (None, 4608) 0 \n", + " \n", + " dense_3 (Dense) (None, 1024) 4719616 \n", + " \n", + " batch_normalization_6 (Bat (None, 1024) 4096 \n", + " chNormalization) \n", + " \n", + " dropout (Dropout) (None, 1024) 0 \n", + " \n", + " dense_4 (Dense) (None, 22) 22550 \n", + " \n", + "=================================================================\n", + "Total params: 5173782 (19.74 MB)\n", + "Trainable params: 5170646 (19.72 MB)\n", + "Non-trainable params: 3136 (12.25 KB)\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "from tensorflow.keras import layers, models\n", + "\n", + "# Define the CNN model with batch normalization\n", + "cnn_model = models.Sequential([\n", + " layers.Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)),\n", + " layers.BatchNormalization(),\n", + " layers.MaxPooling2D((2, 2)),\n", + " layers.Conv2D(64, (3, 3), activation='relu'),\n", + " layers.BatchNormalization(),\n", + " layers.Conv2D(64, (3, 3), activation='relu'),\n", + " layers.BatchNormalization(),\n", + " layers.MaxPooling2D((2, 2)),\n", + " layers.Conv2D(128, (3, 3), activation='relu'),\n", + " layers.BatchNormalization(),\n", + " layers.MaxPooling2D((2, 2)),\n", + " layers.Conv2D(128, (3, 3), activation='relu'),\n", + " layers.BatchNormalization(),\n", + " layers.Conv2D(128, (3, 3), activation='relu'),\n", + " layers.BatchNormalization(),\n", + " layers.MaxPooling2D((2, 2)),\n", + " layers.Flatten(),\n", + " layers.Dense(1024, activation='relu'),\n", + " layers.BatchNormalization(),\n", + " layers.Dropout(0.75),\n", + " layers.Dense(22, activation='softmax')\n", + "])\n", + "\n", + "\n", + "# Compile the model\n", + "cnn_model.compile(optimizer='adam',\n", + " loss='categorical_crossentropy',\n", + " metrics=['accuracy'])\n", + "\n", + "# Display the model summary\n", + "cnn_model.summary()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "QjOA_sUCUI5W", + "outputId": "0b72dc7c-2df8-485f-8746-c999f31cb301" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "lhkIRX0oEpTj" - }, - "source": [ - "# VGG16 Model" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "551/551 [==============================] - 46s 64ms/step - loss: 3.2072 - accuracy: 0.2810 - val_loss: 2.7506 - val_accuracy: 0.2116\n", + "Epoch 2/10\n", + "551/551 [==============================] - 35s 63ms/step - loss: 2.0666 - accuracy: 0.4187 - val_loss: 1.9861 - val_accuracy: 0.4283\n", + "Epoch 3/10\n", + "551/551 [==============================] - 36s 65ms/step - loss: 1.5801 - accuracy: 0.5168 - val_loss: 1.7026 - val_accuracy: 0.4803\n", + "Epoch 4/10\n", + "551/551 [==============================] - 37s 67ms/step - loss: 1.3267 - accuracy: 0.5757 - val_loss: 1.1292 - val_accuracy: 0.6340\n", + "Epoch 5/10\n", + "551/551 [==============================] - 38s 69ms/step - loss: 1.1433 - accuracy: 0.6254 - val_loss: 1.0762 - val_accuracy: 0.6466\n", + "Epoch 6/10\n", + "551/551 [==============================] - 35s 64ms/step - loss: 1.0291 - accuracy: 0.6573 - val_loss: 1.1473 - val_accuracy: 0.6399\n", + "Epoch 7/10\n", + "551/551 [==============================] - 35s 62ms/step - loss: 0.9183 - accuracy: 0.6904 - val_loss: 0.9613 - val_accuracy: 0.6848\n", + "Epoch 8/10\n", + "551/551 [==============================] - 36s 65ms/step - loss: 0.8195 - accuracy: 0.7247 - val_loss: 1.1618 - val_accuracy: 0.6584\n", + "Epoch 9/10\n", + "551/551 [==============================] - 36s 65ms/step - loss: 0.7272 - accuracy: 0.7481 - val_loss: 0.9426 - val_accuracy: 0.7104\n", + "Epoch 10/10\n", + "551/551 [==============================] - 36s 65ms/step - loss: 0.6606 - accuracy: 0.7719 - val_loss: 1.0112 - val_accuracy: 0.6899\n" + ] + } + ], + "source": [ + "cnn_history = cnn_model.fit(\n", + " train_ds,\n", + " validation_data=val_ds,\n", + "\n", + " epochs=10 # Adjust the number of epochs based on your requirements\n", + ")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 507 }, + "id": "09A0ihQVUI5X", + "outputId": "29a4f519-ff28-41f9-b2e6-6de784815841" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "id": "v93hTELS5Jzl" - }, - "outputs": [], - "source": [ - "from tensorflow.keras.applications import VGG16\n", - "from tensorflow.keras.layers import GlobalAveragePooling2D, Dense, Dropout\n", - "from tensorflow.keras.models import Model\n", - "from tensorflow.keras.optimizers import SGD\n", - "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n" + "data": { + "image/png": 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", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_graph(cnn_history)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "edlmW0YVUI5b" + }, + "source": [ + "# Resnet(Basic)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "Ggk2xWRSsy-3" + }, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "from tensorflow.keras.applications import ResNet50\n", + "from tensorflow.keras.models import Model\n", + "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D , Dropout\n", + "from tensorflow.keras.optimizers import Adam\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "bYBGWOAPsL1k", + "outputId": "5ff7de24-5683-44dc-a959-e4b69a9b11eb" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "TbOkFGjzEw5p", - "outputId": "d30519f5-efa9-465c-b5bb-f43e9f81e484" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n", - "58889256/58889256 [==============================] - 2s 0us/step\n", - "Model: \"model_2\"\n", - "_________________________________________________________________\n", - " Layer (type) Output Shape Param # \n", - "=================================================================\n", - " input_3 (InputLayer) [(None, None, None, 3)] 0 \n", - " \n", - " block1_conv1 (Conv2D) (None, None, None, 64) 1792 \n", - " \n", - " block1_conv2 (Conv2D) (None, None, None, 64) 36928 \n", - " \n", - " block1_pool (MaxPooling2D) (None, None, None, 64) 0 \n", - " \n", - " block2_conv1 (Conv2D) (None, None, None, 128) 73856 \n", - " \n", - " block2_conv2 (Conv2D) (None, None, None, 128) 147584 \n", - " \n", - " block2_pool (MaxPooling2D) (None, None, None, 128) 0 \n", - " \n", - " block3_conv1 (Conv2D) (None, None, None, 256) 295168 \n", - " \n", - " block3_conv2 (Conv2D) (None, None, None, 256) 590080 \n", - " \n", - " block3_conv3 (Conv2D) (None, None, None, 256) 590080 \n", - " \n", - " block3_pool (MaxPooling2D) (None, None, None, 256) 0 \n", - " \n", - " block4_conv1 (Conv2D) (None, None, None, 512) 1180160 \n", - " \n", - " block4_conv2 (Conv2D) (None, None, None, 512) 2359808 \n", - " \n", - " block4_conv3 (Conv2D) (None, None, None, 512) 2359808 \n", - " \n", - " block4_pool (MaxPooling2D) (None, None, None, 512) 0 \n", - " \n", - " block5_conv1 (Conv2D) (None, None, None, 512) 2359808 \n", - " \n", - " block5_conv2 (Conv2D) (None, None, None, 512) 2359808 \n", - " \n", - " block5_conv3 (Conv2D) (None, None, None, 512) 2359808 \n", - " \n", - " block5_pool (MaxPooling2D) (None, None, None, 512) 0 \n", - " \n", - " global_average_pooling2d_2 (None, 512) 0 \n", - " (GlobalAveragePooling2D) \n", - " \n", - " dense_4 (Dense) (None, 1024) 525312 \n", - " \n", - " dense_5 (Dense) (None, 22) 22550 \n", - " \n", - "=================================================================\n", - "Total params: 15262550 (58.22 MB)\n", - "Trainable params: 15262550 (58.22 MB)\n", - "Non-trainable params: 0 (0.00 Byte)\n", - "_________________________________________________________________\n" - ] - } - ], - "source": [ - "\n", - "# Load the base model\n", - "base_model = VGG16(weights='imagenet', include_top=False)\n", - "\n", - "# Make more layers trainable\n", - "for layer in base_model.layers[-10:]:\n", - " layer.trainable = True\n", - "\n", - "# Add new top layers\n", - "x = base_model.output\n", - "x = GlobalAveragePooling2D()(x)\n", - "x = Dense(1024, activation='relu')(x)\n", - "predictions = Dense(22, activation='softmax')(x)\n", - "\n", - "# Create the final model\n", - "vgg_model = Model(inputs=base_model.input, outputs=predictions)\n", - "\n", - "vgg_model.compile(optimizer=Adam(learning_rate=1e-5),\n", - " loss='categorical_crossentropy',\n", - " metrics=['accuracy'])\n", - "\n", - "vgg_model.summary()\n", - "\n" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n", + "94765736/94765736 [==============================] - 3s 0us/step\n" + ] + } + ], + "source": [ + "base_model = ResNet50(weights='imagenet', include_top=False)\n", + "\n", + "for layer in base_model.layers[-10:]:\n", + " layer.trainable = True\n", + "\n", + "# Add new top layers\n", + "x = base_model.output\n", + "x = GlobalAveragePooling2D()(x)\n", + "x = Dense(1024, activation='relu')(x)\n", + "predictions = Dense(22, activation='softmax')(x)\n", + "\n", + "res_model = Model(inputs=base_model.input, outputs=predictions)\n", + "\n", + "\n", + "# Re-compile the model\n", + "res_model.compile(optimizer=Adam(learning_rate=1e-5),\n", + " loss='categorical_crossentropy',\n", + " metrics=['accuracy'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "6ANqeRP8tWV4", + "outputId": "5091ac68-6b19-4668-8584-3b046dd64b7e" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Nc8DXxH8MvnG", - "outputId": "babb6905-0dab-4fc2-d615-6e08898524ca" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/10\n", - "551/551 [==============================] - 148s 239ms/step - loss: 1.2364 - accuracy: 0.5985 - val_loss: 0.7437 - val_accuracy: 0.7360\n", - "Epoch 2/10\n", - "551/551 [==============================] - 123s 223ms/step - loss: 0.5993 - accuracy: 0.7848 - val_loss: 0.6208 - val_accuracy: 0.7683\n", - "Epoch 3/10\n", - "551/551 [==============================] - 128s 232ms/step - loss: 0.4672 - accuracy: 0.8295 - val_loss: 0.5241 - val_accuracy: 0.8081\n", - "Epoch 4/10\n", - "551/551 [==============================] - 128s 232ms/step - loss: 0.3806 - accuracy: 0.8620 - val_loss: 0.4916 - val_accuracy: 0.8140\n", - "Epoch 5/10\n", - "551/551 [==============================] - 124s 224ms/step - loss: 0.3119 - accuracy: 0.8891 - val_loss: 0.5329 - val_accuracy: 0.8069\n", - "Epoch 6/10\n", - "551/551 [==============================] - 124s 224ms/step - loss: 0.2599 - accuracy: 0.9063 - val_loss: 0.5281 - val_accuracy: 0.8128\n", - "Epoch 7/10\n", - "551/551 [==============================] - 123s 223ms/step - loss: 0.2171 - accuracy: 0.9225 - val_loss: 0.5258 - val_accuracy: 0.8262\n", - "Epoch 8/10\n", - "551/551 [==============================] - 128s 232ms/step - loss: 0.1756 - accuracy: 0.9380 - val_loss: 0.5114 - val_accuracy: 0.8353\n", - "Epoch 9/10\n", - "551/551 [==============================] - 123s 223ms/step - loss: 0.1501 - accuracy: 0.9474 - val_loss: 0.5555 - val_accuracy: 0.8286\n", - "Epoch 10/10\n", - "551/551 [==============================] - 128s 232ms/step - loss: 0.1284 - accuracy: 0.9558 - val_loss: 0.5891 - val_accuracy: 0.8270\n" - ] - } - ], - "source": [ - "vgg_history= vgg_model.fit(train_ds,epochs=10,validation_data=val_ds)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "551/551 [==============================] - 150s 201ms/step - loss: 1.3461 - accuracy: 0.6010 - val_loss: 3.0756 - val_accuracy: 0.0867\n", + "Epoch 2/10\n", + "551/551 [==============================] - 104s 188ms/step - loss: 0.5116 - accuracy: 0.8258 - val_loss: 0.5763 - val_accuracy: 0.8042\n", + "Epoch 3/10\n", + "551/551 [==============================] - 110s 200ms/step - loss: 0.3162 - accuracy: 0.8965 - val_loss: 0.4449 - val_accuracy: 0.8479\n", + "Epoch 4/10\n", + "551/551 [==============================] - 104s 189ms/step - loss: 0.2023 - accuracy: 0.9386 - val_loss: 0.4379 - val_accuracy: 0.8522\n", + "Epoch 5/10\n", + "551/551 [==============================] - 104s 187ms/step - loss: 0.1295 - accuracy: 0.9632 - val_loss: 0.4506 - val_accuracy: 0.8542\n", + "Epoch 6/10\n", + "551/551 [==============================] - 104s 187ms/step - loss: 0.0870 - accuracy: 0.9776 - val_loss: 0.4571 - val_accuracy: 0.8601\n", + "Epoch 7/10\n", + "551/551 [==============================] - 104s 188ms/step - loss: 0.0579 - accuracy: 0.9848 - val_loss: 0.4730 - val_accuracy: 0.8562\n", + "Epoch 8/10\n", + "551/551 [==============================] - 104s 189ms/step - loss: 0.0477 - accuracy: 0.9855 - val_loss: 0.5126 - val_accuracy: 0.8617\n", + "Epoch 9/10\n", + "551/551 [==============================] - 104s 188ms/step - loss: 0.0387 - accuracy: 0.9878 - val_loss: 0.5597 - val_accuracy: 0.8491\n", + "Epoch 10/10\n", + "551/551 [==============================] - 104s 188ms/step - loss: 0.0378 - accuracy: 0.9864 - val_loss: 0.5381 - val_accuracy: 0.8495\n" + ] + } + ], + "source": [ + "resnet_history= res_model.fit(train_ds,validation_data=val_ds,epochs=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 468 }, + "id": "7QLWlY1dvfUU", + "outputId": "5d0f74ca-f30f-47c7-db63-ec68d0c1de13" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 468 - }, - "id": "5E-06ul8He9M", - "outputId": "210c701f-518b-4253-86ec-f5754d5798eb" - }, - "outputs": [ - { - "data": { - "image/png": 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+ "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_graph(resnet_history)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WGcdJoLHvjxd" + }, + "source": [ + "# ResNet(Perfect) with Data Augumentaion and Regularizations" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "6kfaFjzVemsj", + "outputId": "6cff68f7-0fe9-460a-955f-97608240456a" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "RcmcV-PLnCw_" - }, - "source": [ - "# Model Evaluation and Best model Testing" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 17605 images belonging to 22 classes.\n", + "Found 2538 images belonging to 22 classes.\n" + ] + } + ], + "source": [ + "# Data augmentaion\n", + "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", + "\n", + "datagen = ImageDataGenerator(\n", + " rotation_range=20,\n", + " width_shift_range=0.2,\n", + " height_shift_range=0.2,\n", + " shear_range=0.2,\n", + " zoom_range=0.2,\n", + " horizontal_flip=True,\n", + " fill_mode='nearest'\n", + ")\n", + "\n", + "train_generator = datagen.flow_from_directory(\n", + " 'filtered_train',\n", + " target_size=(150,150),\n", + " batch_size=32,\n", + " class_mode='categorical'\n", + ")\n", + "\n", + "val_generator = datagen.flow_from_directory(\n", + " 'filtered_val',\n", + " target_size=(150, 150),\n", + " batch_size=32,\n", + " class_mode='categorical'\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "id": "AsmRa-MUfsCs" + }, + "outputs": [], + "source": [ + "from tensorflow.keras.applications import ResNet50\n", + "from tensorflow.keras.layers import GlobalAveragePooling2D, Dense, Dropout\n", + "from tensorflow.keras.models import Model\n", + "from tensorflow.keras.optimizers import Adam\n", + "from tensorflow.keras.regularizers import l2\n", + "from tensorflow.keras.callbacks import EarlyStopping\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "h7_q9fbffbgd", + "outputId": "065ed344-07fa-4bfe-de27-62018da4e819" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "P6m-twEGm_AH", - "outputId": "9ecddb0d-3dc8-4cc5-ac29-4f7e77b5aeff" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "157/157 [==============================] - 7s 38ms/step - loss: 1.8495 - accuracy: 0.3898\n", - "ANN Test accuracy: 38.98%\n" - ] - } - ], - "source": [ - "loss_ann, accuracy_ann = ann_model.evaluate(test_ds)\n", - "print(f\"ANN Test accuracy: {accuracy_ann * 100:.2f}%\")\n" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/5\n", + "551/551 [==============================] - 218s 342ms/step - loss: 16.7730 - accuracy: 0.2183 - val_loss: 15.0907 - val_accuracy: 0.5839\n", + "Epoch 2/5\n", + "551/551 [==============================] - 187s 339ms/step - loss: 15.0545 - accuracy: 0.5087 - val_loss: 14.0818 - val_accuracy: 0.7206\n", + "Epoch 3/5\n", + "551/551 [==============================] - 187s 339ms/step - loss: 14.0944 - accuracy: 0.6330 - val_loss: 13.3983 - val_accuracy: 0.7545\n", + "Epoch 4/5\n", + "551/551 [==============================] - 189s 343ms/step - loss: 13.3619 - accuracy: 0.6914 - val_loss: 12.7492 - val_accuracy: 0.7888\n", + "Epoch 5/5\n", + "551/551 [==============================] - 192s 349ms/step - loss: 12.6688 - accuracy: 0.7296 - val_loss: 12.1332 - val_accuracy: 0.8081\n" + ] + } + ], + "source": [ + "# Load the base model\n", + "base_model = ResNet50(weights='imagenet', include_top=False)\n", + "\n", + "# Make the last 10 layers trainable\n", + "for layer in base_model.layers[-10:]:\n", + " layer.trainable = True\n", + "\n", + "# Add new top layers\n", + "x = base_model.output\n", + "x = GlobalAveragePooling2D()(x)\n", + "x = Dropout(0.5)(x) # Adding dropout for regularization\n", + "x = Dense(1024, activation='relu', kernel_regularizer=l2(0.01))(x)\n", + "x = Dropout(0.5)(x) # Adding another dropout layer for regularization\n", + "predictions = Dense(22, activation='softmax', kernel_regularizer=l2(0.01))(x)\n", + "\n", + "res_model_perfect = Model(inputs=base_model.input, outputs=predictions)\n", + "\n", + "# Re-compile the model\n", + "res_model_perfect.compile(optimizer=Adam(learning_rate=1e-5),\n", + " loss='categorical_crossentropy',\n", + " metrics=['accuracy'])\n", + "\n", + "# Early stopping callback\n", + "early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n", + "\n", + "# Train the model\n", + "res_model_perfect_history = res_model_perfect.fit(train_generator, epochs=5, validation_data=val_generator, callbacks=[early_stopping])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "rottw-JQjOq4", + "outputId": "275497e0-d72c-400c-d5e7-b777665d7d68" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "sAs7JCS1MQgL", - "outputId": "19d046a2-6e95-4542-8f60-3aa69acd6ef7" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "157/157 [==============================] - 9s 53ms/step - loss: 0.9678 - accuracy: 0.6992\n", - "CNN Test accuracy: 69.92%\n" - ] - } - ], - "source": [ - "loss_cnn, accuracy_cnn = cnn_model.evaluate(test_ds)\n", - "print(f\"CNN Test accuracy: {accuracy_cnn * 100:.2f}%\")\n" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 6/10\n", + "551/551 [==============================] - 189s 344ms/step - loss: 11.9784 - accuracy: 0.7541 - val_loss: 11.4686 - val_accuracy: 0.8282\n", + "Epoch 7/10\n", + "551/551 [==============================] - 189s 342ms/step - loss: 11.3271 - accuracy: 0.7718 - val_loss: 10.8296 - val_accuracy: 0.8294\n", + "Epoch 8/10\n", + "551/551 [==============================] - 191s 347ms/step - loss: 10.6584 - accuracy: 0.7876 - val_loss: 10.2008 - val_accuracy: 0.8373\n", + "Epoch 9/10\n", + "551/551 [==============================] - 192s 347ms/step - loss: 10.0059 - accuracy: 0.8040 - val_loss: 9.6002 - val_accuracy: 0.8408\n", + "Epoch 10/10\n", + "551/551 [==============================] - 194s 352ms/step - loss: 9.3686 - accuracy: 0.8173 - val_loss: 8.9783 - val_accuracy: 0.8455\n" + ] + } + ], + "source": [ + "res_model_perfect_history1 = res_model_perfect.fit(train_generator, initial_epoch=5,epochs=10, validation_data=val_generator, callbacks=[early_stopping])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lhkIRX0oEpTj" + }, + "source": [ + "# VGG16 Model" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "v93hTELS5Jzl" + }, + "outputs": [], + "source": [ + "from tensorflow.keras.applications import VGG16\n", + "from tensorflow.keras.layers import GlobalAveragePooling2D, Dense, Dropout\n", + "from tensorflow.keras.models import Model\n", + "from tensorflow.keras.optimizers import SGD\n", + "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "TbOkFGjzEw5p", + "outputId": "d30519f5-efa9-465c-b5bb-f43e9f81e484" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "K2zPnXNaMQP6", - "outputId": "ba65fa34-0993-443c-e39f-71bd03be6a0e" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "158/158 [==============================] - 9s 57ms/step - loss: 0.5548 - accuracy: 0.8562\n", - "ResNet Test accuracy: 85.62%\n" - ] - } - ], - "source": [ - "loss_res, accuracy_res = res_model.evaluate(test_ds)\n", - "print(f\"ResNet Test accuracy: {accuracy_res * 100:.2f}%\")\n", - "\n" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n", + "58889256/58889256 [==============================] - 2s 0us/step\n", + "Model: \"model_2\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " input_3 (InputLayer) [(None, None, None, 3)] 0 \n", + " \n", + " block1_conv1 (Conv2D) (None, None, None, 64) 1792 \n", + " \n", + " block1_conv2 (Conv2D) (None, None, None, 64) 36928 \n", + " \n", + " block1_pool (MaxPooling2D) (None, None, None, 64) 0 \n", + " \n", + " block2_conv1 (Conv2D) (None, None, None, 128) 73856 \n", + " \n", + " block2_conv2 (Conv2D) (None, None, None, 128) 147584 \n", + " \n", + " block2_pool (MaxPooling2D) (None, None, None, 128) 0 \n", + " \n", + " block3_conv1 (Conv2D) (None, None, None, 256) 295168 \n", + " \n", + " block3_conv2 (Conv2D) (None, None, None, 256) 590080 \n", + " \n", + " block3_conv3 (Conv2D) (None, None, None, 256) 590080 \n", + " \n", + " block3_pool (MaxPooling2D) (None, None, None, 256) 0 \n", + " \n", + " block4_conv1 (Conv2D) (None, None, None, 512) 1180160 \n", + " \n", + " block4_conv2 (Conv2D) (None, None, None, 512) 2359808 \n", + " \n", + " block4_conv3 (Conv2D) (None, None, None, 512) 2359808 \n", + " \n", + " block4_pool (MaxPooling2D) (None, None, None, 512) 0 \n", + " \n", + " block5_conv1 (Conv2D) (None, None, None, 512) 2359808 \n", + " \n", + " block5_conv2 (Conv2D) (None, None, None, 512) 2359808 \n", + " \n", + " block5_conv3 (Conv2D) (None, None, None, 512) 2359808 \n", + " \n", + " block5_pool (MaxPooling2D) (None, None, None, 512) 0 \n", + " \n", + " global_average_pooling2d_2 (None, 512) 0 \n", + " (GlobalAveragePooling2D) \n", + " \n", + " dense_4 (Dense) (None, 1024) 525312 \n", + " \n", + " dense_5 (Dense) (None, 22) 22550 \n", + " \n", + "=================================================================\n", + "Total params: 15262550 (58.22 MB)\n", + "Trainable params: 15262550 (58.22 MB)\n", + "Non-trainable params: 0 (0.00 Byte)\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "\n", + "# Load the base model\n", + "base_model = VGG16(weights='imagenet', include_top=False)\n", + "\n", + "# Make more layers trainable\n", + "for layer in base_model.layers[-10:]:\n", + " layer.trainable = True\n", + "\n", + "# Add new top layers\n", + "x = base_model.output\n", + "x = GlobalAveragePooling2D()(x)\n", + "x = Dense(1024, activation='relu')(x)\n", + "predictions = Dense(22, activation='softmax')(x)\n", + "\n", + "# Create the final model\n", + "vgg_model = Model(inputs=base_model.input, outputs=predictions)\n", + "\n", + "vgg_model.compile(optimizer=Adam(learning_rate=1e-5),\n", + " loss='categorical_crossentropy',\n", + " metrics=['accuracy'])\n", + "\n", + "vgg_model.summary()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "Nc8DXxH8MvnG", + "outputId": "babb6905-0dab-4fc2-d615-6e08898524ca" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "vkawaMLVMP6d", - "outputId": "46a0d654-b34a-47b9-bf61-819e60e6f08e" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "158/158 [==============================] - 11s 70ms/step - loss: 0.5903 - accuracy: 0.8303\n", - "VGG16 Test accuracy: 83.03%\n" - ] - } - ], - "source": [ - "\n", - "loss_vgg16, accuracy_vgg16 = vgg_model.evaluate(test_ds)\n", - "print(f\"VGG16 Test accuracy: {accuracy_vgg16 * 100:.2f}%\")\n" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "551/551 [==============================] - 148s 239ms/step - loss: 1.2364 - accuracy: 0.5985 - val_loss: 0.7437 - val_accuracy: 0.7360\n", + "Epoch 2/10\n", + "551/551 [==============================] - 123s 223ms/step - loss: 0.5993 - accuracy: 0.7848 - val_loss: 0.6208 - val_accuracy: 0.7683\n", + "Epoch 3/10\n", + "551/551 [==============================] - 128s 232ms/step - loss: 0.4672 - accuracy: 0.8295 - val_loss: 0.5241 - val_accuracy: 0.8081\n", + "Epoch 4/10\n", + "551/551 [==============================] - 128s 232ms/step - loss: 0.3806 - accuracy: 0.8620 - val_loss: 0.4916 - val_accuracy: 0.8140\n", + "Epoch 5/10\n", + "551/551 [==============================] - 124s 224ms/step - loss: 0.3119 - accuracy: 0.8891 - val_loss: 0.5329 - val_accuracy: 0.8069\n", + "Epoch 6/10\n", + "551/551 [==============================] - 124s 224ms/step - loss: 0.2599 - accuracy: 0.9063 - val_loss: 0.5281 - val_accuracy: 0.8128\n", + "Epoch 7/10\n", + "551/551 [==============================] - 123s 223ms/step - loss: 0.2171 - accuracy: 0.9225 - val_loss: 0.5258 - val_accuracy: 0.8262\n", + "Epoch 8/10\n", + "551/551 [==============================] - 128s 232ms/step - loss: 0.1756 - accuracy: 0.9380 - val_loss: 0.5114 - val_accuracy: 0.8353\n", + "Epoch 9/10\n", + "551/551 [==============================] - 123s 223ms/step - loss: 0.1501 - accuracy: 0.9474 - val_loss: 0.5555 - val_accuracy: 0.8286\n", + "Epoch 10/10\n", + "551/551 [==============================] - 128s 232ms/step - loss: 0.1284 - accuracy: 0.9558 - val_loss: 0.5891 - val_accuracy: 0.8270\n" + ] + } + ], + "source": [ + "vgg_history= vgg_model.fit(train_ds,epochs=10,validation_data=val_ds)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 468 }, + "id": "5E-06ul8He9M", + "outputId": "210c701f-518b-4253-86ec-f5754d5798eb" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 56, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "PLRg-hzH3xCv", - "outputId": "bd18867b-1445-40f0-d001-b63d62339591" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 5027 images belonging to 22 classes.\n" - ] - } - ], - "source": [ - "# Preparing test data for Renset Perfect model\n", - "test_generator = datagen.flow_from_directory(\n", - " 'filtered_test',\n", - " target_size=(150, 150),\n", - " batch_size=32,\n", - " class_mode='categorical'\n", - ")\n" + "data": { + "image/png": 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" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_graph(vgg_history)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RcmcV-PLnCw_" + }, + "source": [ + "# Model Evaluation and Best model Testing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "P6m-twEGm_AH", + "outputId": "9ecddb0d-3dc8-4cc5-ac29-4f7e77b5aeff" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 55, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "9z53Loe7uKVH", - "outputId": "63799035-a5cb-4279-89f5-69188aefb10e" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "158/158 [==============================] - 47s 299ms/step - loss: 8.9857 - accuracy: 0.8472\n", - "ResNet(Perfect) Test accuracy: 84.72%\n" - ] - } - ], - "source": [ - "loss_res_model_perfect, accuracy_res_model_perfect = res_model_perfect.evaluate(test_generator)\n", - "print(f\"ResNet(Perfect) Test accuracy: {accuracy_res_model_perfect * 100:.2f}%\")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "157/157 [==============================] - 7s 38ms/step - loss: 1.8495 - accuracy: 0.3898\n", + "ANN Test accuracy: 38.98%\n" + ] + } + ], + "source": [ + "loss_ann, accuracy_ann = ann_model.evaluate(test_ds)\n", + "print(f\"ANN Test accuracy: {accuracy_ann * 100:.2f}%\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "sAs7JCS1MQgL", + "outputId": "19d046a2-6e95-4542-8f60-3aa69acd6ef7" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "swE1qjVO30EQ" - }, - "source": [ - "# Tabulating the Model and it's Accuracy" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "157/157 [==============================] - 9s 53ms/step - loss: 0.9678 - accuracy: 0.6992\n", + "CNN Test accuracy: 69.92%\n" + ] + } + ], + "source": [ + "loss_cnn, accuracy_cnn = cnn_model.evaluate(test_ds)\n", + "print(f\"CNN Test accuracy: {accuracy_cnn * 100:.2f}%\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "K2zPnXNaMQP6", + "outputId": "ba65fa34-0993-443c-e39f-71bd03be6a0e" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "wG_5qUPadj7N" - }, - "source": [ - " | Model Name | Accuracy (%) |\n", - " |-----------------|--------------|\n", - " | ANN | 38.98 |\n", - " | CNN | 69.92 |\n", - " | VGG16 | 83.03 |\n", - " | ResNet | 85.62 |\n", - " | ResNet(Perfect) | 84.58 |" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "158/158 [==============================] - 9s 57ms/step - loss: 0.5548 - accuracy: 0.8562\n", + "ResNet Test accuracy: 85.62%\n" + ] + } + ], + "source": [ + "loss_res, accuracy_res = res_model.evaluate(test_ds)\n", + "print(f\"ResNet Test accuracy: {accuracy_res * 100:.2f}%\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "vkawaMLVMP6d", + "outputId": "46a0d654-b34a-47b9-bf61-819e60e6f08e" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "2F0_YBHIc1zt" - }, - "source": [ - "- The ResNet(Perfect) model is chosen as the optimal model due to its balance of high accuracy and reduced overfitting, making it the best candidate for deployment. While the standard ResNet model achieved the highest accuracy, its overfitting issues could compromise its performance on new, unseen data. Therefore, for practical applications where generalization is crucial, the ResNet(Perfect) model is the preferred choice\n" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "158/158 [==============================] - 11s 70ms/step - loss: 0.5903 - accuracy: 0.8303\n", + "VGG16 Test accuracy: 83.03%\n" + ] + } + ], + "source": [ + "\n", + "loss_vgg16, accuracy_vgg16 = vgg_model.evaluate(test_ds)\n", + "print(f\"VGG16 Test accuracy: {accuracy_vgg16 * 100:.2f}%\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "PLRg-hzH3xCv", + "outputId": "bd18867b-1445-40f0-d001-b63d62339591" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "_un1ZG_XDvzr" - }, - "outputs": [], - "source": [] + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 5027 images belonging to 22 classes.\n" + ] } - ], - "metadata": { - "accelerator": "GPU", + ], + "source": [ + "# Preparing test data for Renset Perfect model\n", + "test_generator = datagen.flow_from_directory(\n", + " 'filtered_test',\n", + " target_size=(150, 150),\n", + " batch_size=32,\n", + " class_mode='categorical'\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { "colab": { - "gpuType": "T4", - "provenance": [] - }, - "kaggle": { - "accelerator": "none", - "dataSources": [ - { - "datasetId": 4280832, - "sourceId": 7368427, - "sourceType": "datasetVersion" - } - ], - "dockerImageVersionId": 30699, - "isGpuEnabled": false, - "isInternetEnabled": true, - "language": "python", - "sourceType": "notebook" - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" + "base_uri": "https://localhost:8080/" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" + "id": "9z53Loe7uKVH", + "outputId": "63799035-a5cb-4279-89f5-69188aefb10e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "158/158 [==============================] - 47s 299ms/step - loss: 8.9857 - accuracy: 0.8472\n", + "ResNet(Perfect) Test accuracy: 84.72%\n" + ] + } + ], + "source": [ + "loss_res_model_perfect, accuracy_res_model_perfect = res_model_perfect.evaluate(test_generator)\n", + "print(f\"ResNet(Perfect) Test accuracy: {accuracy_res_model_perfect * 100:.2f}%\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "swE1qjVO30EQ" + }, + "source": [ + "# Tabulating the Model and it's Accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wG_5qUPadj7N" + }, + "source": [ + " | Model Name | Accuracy (%) |\n", + " |-----------------|--------------|\n", + " | ANN | 38.98 |\n", + " | CNN | 69.92 |\n", + " | VGG16 | 83.03 |\n", + " | ResNet | 85.62 |\n", + " | ResNet(Perfect) | 84.58 |" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2F0_YBHIc1zt" + }, + "source": [ + "- The ResNet(Perfect) model is chosen as the optimal model due to its balance of high accuracy and reduced overfitting, making it the best candidate for deployment. While the standard ResNet model achieved the highest accuracy, its overfitting issues could compromise its performance on new, unseen data. Therefore, for practical applications where generalization is crucial, the ResNet(Perfect) model is the preferred choice\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "_un1ZG_XDvzr" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kaggle": { + "accelerator": "none", + "dataSources": [ + { + "datasetId": 4280832, + "sourceId": 7368427, + "sourceType": "datasetVersion" } + ], + "dockerImageVersionId": 30699, + "isGpuEnabled": false, + "isInternetEnabled": true, + "language": "python", + "sourceType": "notebook" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 0 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 1 } From 1c321d4bbf86ce45dc92c9f2965a36100b0e5c46 Mon Sep 17 00:00:00 2001 From: Karthik110505 Date: Wed, 22 May 2024 18:06:20 +0530 Subject: [PATCH 2/6] Adding Waste Classification using DL project --- .../Dataset/README.md | 9 + .../Images/ANN_Performance.png | Bin 0 -> 48485 bytes .../Images/CNN_Performance.png | Bin 0 -> 63089 bytes .../Images/MobileNetV2_Performance.png | Bin 0 -> 51401 bytes .../Images/ResNet50_Performance.png | Bin 0 -> 57845 bytes .../Images/VGG16_Performance.png | Bin 0 -> 54034 bytes Waste Classification using DL/Model/README.md | 92 + .../Model/Waste_Classification_using_DL.ipynb | 6567 +++++++++++++++++ .../requirements.txt | 5 + 9 files changed, 6673 insertions(+) create mode 100644 Waste Classification using DL/Dataset/README.md create mode 100644 Waste Classification using DL/Images/ANN_Performance.png create mode 100644 Waste Classification using DL/Images/CNN_Performance.png create mode 100644 Waste Classification using DL/Images/MobileNetV2_Performance.png create mode 100644 Waste Classification using DL/Images/ResNet50_Performance.png create mode 100644 Waste Classification using DL/Images/VGG16_Performance.png create mode 100644 Waste Classification using DL/Model/README.md create mode 100644 Waste Classification using DL/Model/Waste_Classification_using_DL.ipynb create mode 100644 Waste Classification using DL/requirements.txt diff --git a/Waste Classification using DL/Dataset/README.md b/Waste Classification using DL/Dataset/README.md new file mode 100644 index 000000000..5260a2380 --- /dev/null +++ b/Waste Classification using DL/Dataset/README.md @@ -0,0 +1,9 @@ +# Waste Classification Using Deep Learning + +The dataset used in this project is taken from the Kaggle website. + +**Dataset Link:** [Waste Classification Data](https://www.kaggle.com/datasets/techsash/waste-classification-data) + +This dataset consists of images of organic (O) and recyclable (R) waste. The images are labeled to provide a comprehensive dataset for training and evaluating machine learning models. + +The task is to classify each waste image into one of the two predefined categories indicating either organic or recyclable waste. diff --git a/Waste Classification using DL/Images/ANN_Performance.png b/Waste Classification using DL/Images/ANN_Performance.png new file mode 100644 index 0000000000000000000000000000000000000000..b6e5f1c2a6ee7619969c09d098e8fe721cea4e60 GIT binary patch literal 48485 zcmbrmWmr{hv;~T!(xFI7D4?`-x2T{9NOyO4rwB?a0@4T)(jC&EARr}dkrwF^q?z1Di)8Doq&-u+5h=^+6w6)qYY8o}d7a;j)(82xByR}!yc!*^ti zj91`)n65I9)vm&q-&L~+_&25711-1bjuvj7#xCY)mJW{g<{Yl3F6QPAu2zn2TNtep z@FEW6MY1mD#%|V*4)ki)_U34s=4SLfJoHbEz3F+ldHCqL`9-*eMDBCbD?g=w{9LW_ z2c0(>8a>)$IcYVow2f&`uXpMPZU46RlJ_#Cs2JYG4N3bXlokYqRN~z&&JJ0BNm2_hfglgosIbvtr()6BnIPIdYX8*d@WV~rs)n0 zyRH8C8ci&7mEi^Qhf+?-CiegS{ROdm0%rgJ{p1VfqapVHUITxm1^<8k;6l&ySGD!? zr`59m_cjFr`i&kXqw{8Y+W+@0A&hG&VgGy42;R7?|Gz&dF!pFnude1Jyd@UVZy~z* 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The task is to classify these images using deep learning architectures. + +**WHAT I DID** + +First, I imported all the required libraries and the dataset for this project. I split the dataset into training, validation, and testing sets. Then I proceeded to build and evaluate the models. + +I developed several deep learning models to classify the images. Initially, I used an Artificial Neural Network (ANN), followed by a Convolutional Neural Network (CNN). Both models did not yield satisfactory accuracy. I then used VGG16, ResNet, and MobileNetV2 architectures, with the latter providing better performance. Finally, I evaluated the performance of all the models in order to pick the optimal model. + +**MODELS USED** + +The models are: + +1. Artificial Neural Network (ANN) +2. Basic Convolutional Neural Network (CNN) +3. VGG16 Model +4. ResNet Model +5. MobileNetV2 Model + +**LIBRARIES NEEDED** + +- tensorflow +- matplotlib +- opencv-python +- numpy +- random +- shutil + +**VISUALIZATION** + +### Model 1 (ANN Model) Performance Graphs +![Model 1 (ANN Model) performance graphs](../Images/ANN_Performance.png) + +### Model 2 (Basic CNN Model) Performance Graphs +![Model 2 (Basic CNN Model) performance graphs](../Images/CNN_Performance.png) + +### Model 3 (VGG16 Model) Performance Graphs +![Model 3 (VGG16 Model) performance graphs](../Images/VGG16_Performance.png) + +### Model 4 (ResNet Model) Performance Graphs +![Model 4 (ResNet Model) performance graphs](../Images/ResNet50_Performance.png) + +### Model 5 (MobileNetV2 Model) Performance Graphs +![Model 5 (MobileNetV2 Model) performance graphs](../Images/MobileNetV2_Performance.png) + +**ACCURACIES** + +| Model | Architecture | Accuracy in % (on testing data) | +|--------------------|:---------------------------:|:------------------------------:| +| Model 1 | ANN Model | 81.73 | +| Model 2 | Basic CNN Model | 87.31 | +| Model 3 | VGG16 Model | 90.65 | +| Model 4 | ResNet Model | 76.88 | +| Model 5 | MobileNetV2 Model | 90.85 | + +**CONCLUSION** + +After training and evaluating various deep learning models on the waste classification dataset, the results are as follows: + +**Key Observations:** + +1. **Artificial Neural Network (ANN) Model**: Achieved an accuracy of 81.73%. While it provided a decent baseline, it was outperformed by the other convolutional neural network-based models. + +2. **Basic CNN Model**: Improved accuracy significantly to 87.31%, demonstrating the power of convolutional layers in image classification tasks. + +3. **VGG16 Model**: Achieved an impressive accuracy of 90.65%. This pre-trained model showed strong performance, indicating the benefits of transfer learning. + +4. **ResNet Model**: Obtained an accuracy of 76.88%, which was lower than expected. This might be due to overfitting or insufficient fine-tuning. + +5. **MobileNetV2 Model**: Achieved the highest accuracy of 90.85%. This lightweight model is particularly suitable for deployment in resource-constrained environments. + +**Conclusion:** + +The MobileNetV2 model emerged as the best-performing model for the waste classification task, with an accuracy of 90.85%. Its balance of high accuracy and computational efficiency makes it an excellent choice for practical applications. The VGG16 model also performed very well and could be a suitable alternative depending on the specific use case requirements. + +Further improvements can be made by fine-tuning the models, exploring additional data augmentation techniques, and experimenting with different hyperparameters. + +**Connect with Me** + +- [LinkedIn](https://www.linkedin.com/in/barrenkala-veera-venkata-karthik-b58b9a285/) +- [GitHub](https://github.com/Karthik110505) diff --git a/Waste Classification using DL/Model/Waste_Classification_using_DL.ipynb b/Waste Classification using DL/Model/Waste_Classification_using_DL.ipynb new file mode 100644 index 000000000..39600d5c9 --- /dev/null +++ b/Waste Classification using DL/Model/Waste_Classification_using_DL.ipynb @@ -0,0 +1,6567 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "JETjvUs37mHR" + }, + "source": [ + "# Loading Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "kpJYybr5-xwz" + }, + "outputs": [], + "source": [ + "import os\n", + "import cv2\n", + "import tensorflow as tf\n", + "from tensorflow.keras import layers, models\n", + "import matplotlib.pyplot as plt\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pYY3EFz_7rPm" + }, + "source": [ + "# Downloading the Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qs1YiHJ__IKV", + "outputId": "57d8e412-abaa-4b06-f45f-8c21ef7cd1a8" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset URL: https://www.kaggle.com/datasets/techsash/waste-classification-data\n", + "License(s): CC-BY-SA-4.0\n", + "Downloading waste-classification-data.zip to /content\n", + " 97% 414M/427M [00:04<00:00, 159MB/s]\n", + "100% 427M/427M [00:04<00:00, 103MB/s]\n" + ] + } + ], + "source": [ + "!kaggle datasets download -d techsash/waste-classification-data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 36 + }, + "id": "b3oDVrCsBDmz", + "outputId": "a1536458-01f1-4b94-b460-872a417c7183" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + }, + "text/plain": [ + "'/content'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Checking the current directory\n", + "pwd" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "collapsed": true, + "id": "aaFXmlUv_gIe", + "outputId": "32de020c-c2e9-4040-c26e-98ed3acd6bd6" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n", + " inflating: dataset/DATASET/TRAIN/R/R_5499.jpg \n", + " inflating: 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dataset/DATASET/TRAIN/R/R_9988.jpg \n", + " inflating: dataset/DATASET/TRAIN/R/R_9989.jpg \n", + " inflating: dataset/DATASET/TRAIN/R/R_999.jpg \n", + " inflating: dataset/DATASET/TRAIN/R/R_9990.jpg \n", + " inflating: dataset/DATASET/TRAIN/R/R_9991.jpg \n", + " inflating: dataset/DATASET/TRAIN/R/R_9992.jpg \n", + " inflating: dataset/DATASET/TRAIN/R/R_9993.jpg \n", + " inflating: dataset/DATASET/TRAIN/R/R_9994.jpg \n", + " inflating: dataset/DATASET/TRAIN/R/R_9995.jpg \n", + " inflating: dataset/DATASET/TRAIN/R/R_9996.jpg \n", + " inflating: dataset/DATASET/TRAIN/R/R_9997.jpg \n", + " inflating: dataset/DATASET/TRAIN/R/R_9998.jpg \n", + " inflating: dataset/DATASET/TRAIN/R/R_9999.jpg \n" + ] + } + ], + "source": [ + "# Unzipping the dataset\n", + "!unzip \\*.zip\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "O4vA8DhG8IS_" + }, + "source": [ + "# Delete the unnecessary files and folders created" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3F9TEnR__ni0", + "outputId": "f251895d-5a97-44d6-b45c-6dddbea68b34" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The directory \"/content/dataset\" has been deleted.\n" + ] + } + ], + "source": [ + "import shutil\n", + "\n", + "# Specify the directory path\n", + "directory_path = '/content/dataset'\n", + "\n", + "# Delete the directory and all its contents\n", + "shutil.rmtree(directory_path)\n", + "\n", + "print(f'The directory \"{directory_path}\" has been deleted.')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "11FnsUk18XJv" + }, + "source": [ + "# Check the number of files present" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "d7kktIit_vlX", + "outputId": "f8bf3fbc-65a2-4292-eefc-e4272130bf14" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The number of files in \"/content/DATASET/TEST/O\" is: 1401\n", + "The number of files in \"/content/DATASET/TEST/R\" is: 1112\n", + "The number of files in \"/content/DATASET/TRAIN/O\" is: 12565\n", + "The number of files in \"/content/DATASET/TRAIN/R\" is: 9999\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "def check_files():\n", + " # Specify the directory path\n", + " directory_path = '/content/DATASET'\n", + " sub_dir= ['TEST/O','TEST/R','TRAIN/O','TRAIN/R','VALIDATION/O','VALIDATION/R']\n", + "\n", + " for dir in sub_dir:\n", + " path = os.path.join(directory_path,dir)\n", + " if os.path.exists(path):\n", + " num_files = len([f for f in os.listdir(path) if os.path.isfile(os.path.join(path, f))])\n", + " print(f'The number of files in \"{path}\" is: {num_files}')\n", + "\n", + "check_files()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "08hJrIFd8kx2" + }, + "source": [ + "# Creating Validation Dataset\n", + "- The Dataset we have, contains only train and test. There is no validation data. The below code creates a validation data from the training dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pAXwf-H4Aj89", + "outputId": "af232f96-793a-4f63-d7ae-7f1c44108073" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Moved 1500 images from \"/content/DATASET/TRAIN/O\" to \"/content/DATASET/VALIDATION/O\".\n", + "Moved 1500 images from \"/content/DATASET/TRAIN/R\" to \"/content/DATASET/VALIDATION/R\".\n" + ] + } + ], + "source": [ + "import random\n", + "\n", + "def move_images(source_dir, target_dir, num_images):\n", + " # Create the target directory if it doesn't exist\n", + " if not os.path.exists(target_dir):\n", + " os.makedirs(target_dir)\n", + "\n", + " # List all files in the source directory\n", + " files = [f for f in os.listdir(source_dir) if os.path.isfile(os.path.join(source_dir, f))]\n", + "\n", + " # Randomly select the specified number of files\n", + " selected_files = random.sample(files, num_images)\n", + "\n", + " # Move the selected files to the target directory\n", + " for file in selected_files:\n", + " shutil.move(os.path.join(source_dir, file), os.path.join(target_dir, file))\n", + "\n", + "# Define the source and target directories\n", + "source_dir_O = '/content/DATASET/TRAIN/O'\n", + "source_dir_R = '/content/DATASET/TRAIN/R'\n", + "target_dir_O = '/content/DATASET/VALIDATION/O'\n", + "target_dir_R = '/content/DATASET/VALIDATION/R'\n", + "\n", + "# Number of images to move\n", + "num_images_to_move = 1500\n", + "\n", + "# Move images\n", + "move_images(source_dir_O, target_dir_O, num_images_to_move)\n", + "move_images(source_dir_R, target_dir_R, num_images_to_move)\n", + "\n", + "print(f'Moved {num_images_to_move} images from \"{source_dir_O}\" to \"{target_dir_O}\".')\n", + "print(f'Moved {num_images_to_move} images from \"{source_dir_R}\" to \"{target_dir_R}\".')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NmMucovbUg6H" + }, + "source": [ + "# Function for plotting the Graphs" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "VaRBrH8EUgfo" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "def plot_graph(history):\n", + " fig, ax = plt.subplots(1, 2, figsize=(12, 5)) # Create a figure with 1 row and 2 columns\n", + "\n", + " # Plot training & validation accuracy values\n", + " ax[0].plot(history.history['accuracy'])\n", + " ax[0].plot(history.history['val_accuracy'])\n", + " ax[0].set_title('Model accuracy')\n", + " ax[0].set_ylabel('Accuracy')\n", + " ax[0].set_xlabel('Epoch')\n", + " ax[0].legend(['Train', 'Validation'], loc='upper left')\n", + "\n", + " # Plot training & validation loss values\n", + " ax[1].plot(history.history['loss'])\n", + " ax[1].plot(history.history['val_loss'])\n", + " ax[1].set_title('Model loss')\n", + " ax[1].set_ylabel('Loss')\n", + " ax[1].set_xlabel('Epoch')\n", + " ax[1].legend(['Train', 'Validation'], loc='upper left')\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "P6KmzQyK9L4s" + }, + "source": [ + "# Loading the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "O1RVpWcgEF-v", + "outputId": "3a1fae23-6524-44bd-90ae-ad975f93f369" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 19564 files belonging to 2 classes.\n", + "Found 3000 files belonging to 2 classes.\n", + "Found 2513 files belonging to 2 classes.\n", + "['O', 'R']\n" + ] + } + ], + "source": [ + "\n", + "# Define paths\n", + "train_dir = '/content/DATASET/TRAIN'\n", + "validation_dir = '/content/DATASET/VALIDATION'\n", + "test_dir = '/content/DATASET/TEST'\n", + "\n", + "# Parameters\n", + "batch_size = 32\n", + "img_height = 224\n", + "img_width = 224\n", + "\n", + "# Load training dataset\n", + "train_ds = tf.keras.preprocessing.image_dataset_from_directory(\n", + " train_dir,\n", + " seed=123,\n", + " image_size=(img_height, img_width),\n", + " batch_size=batch_size\n", + ")\n", + "\n", + "# Load validation dataset\n", + "val_ds = tf.keras.preprocessing.image_dataset_from_directory(\n", + " validation_dir,\n", + " seed=123,\n", + " image_size=(img_height, img_width),\n", + " batch_size=batch_size\n", + ")\n", + "\n", + "\n", + "# Load test dataset\n", + "test_ds = tf.keras.preprocessing.image_dataset_from_directory(\n", + " test_dir,\n", + " seed=123,\n", + " image_size=(img_height, img_width),\n", + " batch_size=batch_size\n", + ")\n", + "# Print class names to verify\n", + "class_names = train_ds.class_names\n", + "print(class_names)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "b-yA8ZJnGOsP" + }, + "source": [ + "# Checking the data loaded perfectly or not" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 241 + }, + "id": "y-tGELjgHWCG", + "outputId": "13168c4c-1b67-424d-973b-7ed167fb1335" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import cv2\n", + "import numpy as np\n", + "from google.colab.patches import cv2_imshow\n", + "\n", + "\n", + "# Get a batch of images and labels\n", + "for images, labels in train_ds.take(1):\n", + " first_image = images[3].numpy().astype(\"uint8\")\n", + " first_label = labels[3].numpy()\n", + "\n", + "# Convert the image from RGB to BGR (OpenCV uses BGR by default)\n", + "first_image_bgr = cv2.cvtColor(first_image, cv2.COLOR_RGB2BGR)\n", + "\n", + "# Display the image using OpenCV\n", + "cv2_imshow( first_image_bgr)\n", + "cv2.waitKey(0)\n", + "cv2.destroyAllWindows()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "8RZ4-cNBHXM8", + "outputId": "2da81b6e-f2d6-44ca-9634-16482f89ce95" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "first_label" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "KsZm5zhgNuy7" + }, + "outputs": [], + "source": [ + "# Normalize the datasets\n", + "normalization_layer = layers.Rescaling(1./255)\n", + "\n", + "normalized_train_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))\n", + "normalized_val_ds = val_ds.map(lambda x, y: (normalization_layer(x), y))\n", + "normalized_test_ds = test_ds.map(lambda x, y: (normalization_layer(x), y))\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0PkPAS609nI8" + }, + "source": [ + "# Model Building" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cr4qVvZGJslK" + }, + "source": [ + "# ANN" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "id": "FVkUYWThJM80" + }, + "outputs": [], + "source": [ + "from tensorflow.keras.layers import Flatten, Dense, Dropout\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.optimizers import Adam" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "C6YN8kDyN5Jd", + "outputId": "aa70ad7c-27e8-457a-eb13-ed8f6e682a49" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_8\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " flatten (Flatten) (None, 150528) 0 \n", + " \n", + " dense_16 (Dense) (None, 512) 77070848 \n", + " \n", + " dropout_8 (Dropout) (None, 512) 0 \n", + " \n", + " dense_17 (Dense) (None, 256) 131328 \n", + " \n", + " dropout_9 (Dropout) (None, 256) 0 \n", + " \n", + " dense_18 (Dense) (None, 128) 32896 \n", + " \n", + " dense_19 (Dense) (None, 1) 129 \n", + " \n", + "=================================================================\n", + "Total params: 77235201 (294.63 MB)\n", + "Trainable params: 77235201 (294.63 MB)\n", + "Non-trainable params: 0 (0.00 Byte)\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "ann_model = Sequential([\n", + " Flatten(input_shape=(img_width, img_height, 3)), # Flatten the input image\n", + " Dense(512, activation='relu'),\n", + " Dropout(0.5),\n", + " Dense(256, activation='relu'),\n", + " Dropout(0.5),\n", + " Dense(128, activation='relu'),\n", + " Dense(1, activation='sigmoid') # Output layer with sigmoid activation for binary classification\n", + "])\n", + "\n", + "# Compile the model\n", + "ann_model.compile(optimizer=Adam(learning_rate=0.0001), loss='binary_crossentropy', metrics=['accuracy'])\n", + "\n", + "ann_model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "WKsng-7xQfMS", + "outputId": "cbb29387-f4df-4416-b988-31d6dab60c67" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "612/612 [==============================] - 32s 48ms/step - loss: 0.9849 - accuracy: 0.5710 - val_loss: 0.5917 - val_accuracy: 0.6157\n", + "Epoch 2/10\n", + "612/612 [==============================] - 32s 52ms/step - loss: 0.6151 - accuracy: 0.6390 - val_loss: 0.5857 - val_accuracy: 0.7810\n", + "Epoch 3/10\n", + "612/612 [==============================] - 30s 48ms/step - loss: 0.6175 - accuracy: 0.6530 - val_loss: 0.6019 - val_accuracy: 0.7447\n", + "Epoch 4/10\n", + "612/612 [==============================] - 36s 58ms/step - loss: 0.6071 - accuracy: 0.6631 - val_loss: 0.5790 - val_accuracy: 0.7893\n", + "Epoch 5/10\n", + "612/612 [==============================] - 32s 53ms/step - loss: 0.6051 - accuracy: 0.6784 - val_loss: 0.5710 - val_accuracy: 0.7793\n", + "Epoch 6/10\n", + "612/612 [==============================] - 30s 49ms/step - loss: 0.5971 - accuracy: 0.6857 - val_loss: 0.5644 - val_accuracy: 0.7920\n", + "Epoch 7/10\n", + "612/612 [==============================] - 31s 51ms/step - loss: 0.5918 - accuracy: 0.6892 - val_loss: 0.5665 - val_accuracy: 0.7780\n", + "Epoch 8/10\n", + "612/612 [==============================] - 31s 50ms/step - loss: 0.5895 - accuracy: 0.6979 - val_loss: 0.5605 - val_accuracy: 0.7813\n", + "Epoch 9/10\n", + "612/612 [==============================] - 32s 52ms/step - loss: 0.5724 - accuracy: 0.7201 - val_loss: 0.5583 - val_accuracy: 0.7820\n", + "Epoch 10/10\n", + "612/612 [==============================] - 36s 58ms/step - loss: 0.5727 - accuracy: 0.7090 - val_loss: 0.5577 - val_accuracy: 0.7660\n" + ] + } + ], + "source": [ + "ann_history = ann_model.fit(normalized_train_ds,validation_data=normalized_val_ds,epochs=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 507 + }, + "id": "CjoUn4YhVNQd", + "outputId": "f4adb878-1fb2-4654-cdd8-eac693614caf" + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_graph(ann_history)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Z46ZvloTRRY5" + }, + "source": [ + "# CNN" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "id": "NTzwG7WDT0NW" + }, + "outputs": [], + "source": [ + "from tensorflow.keras.layers import Conv2D , BatchNormalization, MaxPooling2D" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Sv_B-Gd0QvO1", + "outputId": "c260f2d5-c73d-4c30-be03-9e83418a1f2b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_9\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " conv2d (Conv2D) (None, 222, 222, 32) 896 \n", + " \n", + " batch_normalization (Batch (None, 222, 222, 32) 128 \n", + " Normalization) \n", + " \n", + " max_pooling2d (MaxPooling2 (None, 111, 111, 32) 0 \n", + " D) \n", + " \n", + " dropout_10 (Dropout) (None, 111, 111, 32) 0 \n", + " \n", + " conv2d_1 (Conv2D) (None, 109, 109, 64) 18496 \n", + " \n", + " batch_normalization_1 (Bat (None, 109, 109, 64) 256 \n", + " chNormalization) \n", + " \n", + " max_pooling2d_1 (MaxPoolin (None, 54, 54, 64) 0 \n", + " g2D) \n", + " \n", + " dropout_11 (Dropout) (None, 54, 54, 64) 0 \n", + " \n", + " conv2d_2 (Conv2D) (None, 52, 52, 128) 73856 \n", + " \n", + " batch_normalization_2 (Bat (None, 52, 52, 128) 512 \n", + " chNormalization) \n", + " \n", + " max_pooling2d_2 (MaxPoolin (None, 26, 26, 128) 0 \n", + " g2D) \n", + " \n", + " dropout_12 (Dropout) (None, 26, 26, 128) 0 \n", + " \n", + " flatten_1 (Flatten) (None, 86528) 0 \n", + " \n", + " dense_20 (Dense) (None, 512) 44302848 \n", + " \n", + " batch_normalization_3 (Bat (None, 512) 2048 \n", + " chNormalization) \n", + " \n", + " dropout_13 (Dropout) (None, 512) 0 \n", + " \n", + " dense_21 (Dense) (None, 1) 513 \n", + " \n", + "=================================================================\n", + "Total params: 44399553 (169.37 MB)\n", + "Trainable params: 44398081 (169.37 MB)\n", + "Non-trainable params: 1472 (5.75 KB)\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "cnn_model = Sequential([\n", + " Conv2D(32, (3, 3), activation='relu', input_shape=(img_width, img_height, 3)),\n", + " BatchNormalization(),\n", + " MaxPooling2D((2, 2)),\n", + " Dropout(0.25),\n", + "\n", + " Conv2D(64, (3, 3), activation='relu'),\n", + " BatchNormalization(),\n", + " MaxPooling2D((2, 2)),\n", + " Dropout(0.25),\n", + "\n", + " Conv2D(128, (3, 3), activation='relu'),\n", + " BatchNormalization(),\n", + " MaxPooling2D((2, 2)),\n", + " Dropout(0.25),\n", + "\n", + " Flatten(),\n", + " Dense(512, activation='relu'),\n", + " BatchNormalization(),\n", + " Dropout(0.5),\n", + " Dense(1, activation='sigmoid') # Output layer with sigmoid activation for binary classification\n", + "])\n", + "\n", + "# Compile the model\n", + "cnn_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n", + "cnn_model.summary()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "gBVmJ10ITxCi", + "outputId": "cbb564b7-3dcd-443f-c404-97d4444a0288" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "612/612 [==============================] - 96s 148ms/step - loss: 0.4969 - accuracy: 0.7967 - val_loss: 0.4461 - val_accuracy: 0.7940\n", + "Epoch 2/10\n", + "612/612 [==============================] - 89s 145ms/step - loss: 0.3802 - accuracy: 0.8381 - val_loss: 0.4573 - val_accuracy: 0.8133\n", + "Epoch 3/10\n", + "612/612 [==============================] - 89s 144ms/step - loss: 0.3394 - accuracy: 0.8571 - val_loss: 0.4897 - val_accuracy: 0.8253\n", + "Epoch 4/10\n", + "612/612 [==============================] - 89s 144ms/step - loss: 0.3150 - accuracy: 0.8690 - val_loss: 0.3033 - val_accuracy: 0.8763\n", + "Epoch 5/10\n", + "612/612 [==============================] - 87s 142ms/step - loss: 0.2923 - accuracy: 0.8819 - val_loss: 0.5597 - val_accuracy: 0.7943\n", + "Epoch 6/10\n", + "612/612 [==============================] - 88s 143ms/step - loss: 0.2711 - accuracy: 0.8914 - val_loss: 0.3267 - val_accuracy: 0.8790\n", + "Epoch 7/10\n", + "612/612 [==============================] - 86s 140ms/step - loss: 0.2371 - accuracy: 0.9034 - val_loss: 0.3712 - val_accuracy: 0.8687\n", + "Epoch 8/10\n", + "612/612 [==============================] - 86s 140ms/step - loss: 0.1997 - accuracy: 0.9218 - val_loss: 0.3451 - val_accuracy: 0.8667\n", + "Epoch 9/10\n", + "612/612 [==============================] - 88s 144ms/step - loss: 0.1590 - accuracy: 0.9390 - val_loss: 0.5838 - val_accuracy: 0.8477\n", + "Epoch 10/10\n", + "612/612 [==============================] - 86s 140ms/step - loss: 0.1329 - accuracy: 0.9505 - val_loss: 0.4911 - val_accuracy: 0.8297\n" + ] + } + ], + "source": [ + "cnn_history = cnn_model.fit(normalized_train_ds,validation_data = normalized_val_ds,epochs=10)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 507 + }, + "id": "DZRt__JPZ4me", + "outputId": "39412fef-b36e-46ab-89d3-492dc0ef76ae" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_graph(cnn_history)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2YAQRXa1RTTg" + }, + "source": [ + "# VGG16" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "id": "s0NQ199WaCk2" + }, + "outputs": [], + "source": [ + "from tensorflow.keras.applications import VGG16\n", + "from tensorflow.keras.models import Model\n" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "2juV65DraChp", + "outputId": "b90795c2-af03-4bbe-ac18-2478852ef676" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n", + "58889256/58889256 [==============================] - 0s 0us/step\n", + "Model: \"model\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " input_9 (InputLayer) [(None, 224, 224, 3)] 0 \n", + " \n", + " block1_conv1 (Conv2D) (None, 224, 224, 64) 1792 \n", + " \n", + " block1_conv2 (Conv2D) (None, 224, 224, 64) 36928 \n", + " \n", + " block1_pool (MaxPooling2D) (None, 112, 112, 64) 0 \n", + " \n", + " block2_conv1 (Conv2D) (None, 112, 112, 128) 73856 \n", + " \n", + " block2_conv2 (Conv2D) (None, 112, 112, 128) 147584 \n", + " \n", + " block2_pool (MaxPooling2D) (None, 56, 56, 128) 0 \n", + " \n", + " block3_conv1 (Conv2D) (None, 56, 56, 256) 295168 \n", + " \n", + " block3_conv2 (Conv2D) (None, 56, 56, 256) 590080 \n", + " \n", + " block3_conv3 (Conv2D) (None, 56, 56, 256) 590080 \n", + " \n", + " block3_pool (MaxPooling2D) (None, 28, 28, 256) 0 \n", + " \n", + " block4_conv1 (Conv2D) (None, 28, 28, 512) 1180160 \n", + " \n", + " block4_conv2 (Conv2D) (None, 28, 28, 512) 2359808 \n", + " \n", + " block4_conv3 (Conv2D) (None, 28, 28, 512) 2359808 \n", + " \n", + " block4_pool (MaxPooling2D) (None, 14, 14, 512) 0 \n", + " \n", + " block5_conv1 (Conv2D) (None, 14, 14, 512) 2359808 \n", + " \n", + " block5_conv2 (Conv2D) (None, 14, 14, 512) 2359808 \n", + " \n", + " block5_conv3 (Conv2D) (None, 14, 14, 512) 2359808 \n", + " \n", + " block5_pool (MaxPooling2D) (None, 7, 7, 512) 0 \n", + " \n", + " flatten_2 (Flatten) (None, 25088) 0 \n", + " \n", + " dense_22 (Dense) (None, 1024) 25691136 \n", + " \n", + " dense_23 (Dense) (None, 1) 1025 \n", + " \n", + "=================================================================\n", + "Total params: 40406849 (154.14 MB)\n", + "Trainable params: 32771585 (125.01 MB)\n", + "Non-trainable params: 7635264 (29.13 MB)\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "# Load the pre-trained VGG16 model without the top classification layer\n", + "base_model = VGG16(weights='imagenet', include_top=False, input_shape=(img_width, img_height, 3))\n", + "\n", + "# Freeze the layers of the pre-trained model\n", + "base_model.trainable=False\n", + "\n", + "# Unfreezing some layers for model fine tuning\n", + "for layer in base_model.layers[-4:]:\n", + " layer.trainable = True\n", + "\n", + "# Add layers on top\n", + "x = Flatten()(base_model.output)\n", + "x = Dense(1024, activation='relu')(x)\n", + "predictions = Dense(1, activation='sigmoid')(x)\n", + "\n", + "# Create the final model\n", + "vgg_model = Model(inputs=base_model.input, outputs=predictions)\n", + "\n", + "# Compile the model\n", + "vgg_model.compile(optimizer=Adam(learning_rate=1e-6), loss='binary_crossentropy', metrics=['accuracy'])\n", + "\n", + "vgg_model.summary()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DlymoD6wRVkw", + "outputId": "82e99459-55da-4a4e-820d-f1a4678cba06" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "612/612 [==============================] - 128s 192ms/step - loss: 0.4220 - accuracy: 0.8149 - val_loss: 0.3227 - val_accuracy: 0.8730\n", + "Epoch 2/10\n", + "612/612 [==============================] - 117s 190ms/step - loss: 0.2745 - accuracy: 0.8928 - val_loss: 0.2583 - val_accuracy: 0.8993\n", + "Epoch 3/10\n", + "612/612 [==============================] - 117s 190ms/step - loss: 0.2269 - accuracy: 0.9121 - val_loss: 0.2281 - val_accuracy: 0.9123\n", + "Epoch 4/10\n", + "612/612 [==============================] - 108s 176ms/step - loss: 0.1967 - accuracy: 0.9251 - val_loss: 0.2167 - val_accuracy: 0.9173\n", + "Epoch 5/10\n", + "612/612 [==============================] - 108s 176ms/step - loss: 0.1744 - accuracy: 0.9329 - val_loss: 0.1946 - val_accuracy: 0.9237\n", + "Epoch 6/10\n", + "612/612 [==============================] - 108s 176ms/step - loss: 0.1560 - accuracy: 0.9416 - val_loss: 0.1855 - val_accuracy: 0.9273\n", + "Epoch 7/10\n", + "612/612 [==============================] - 117s 190ms/step - loss: 0.1410 - accuracy: 0.9481 - val_loss: 0.1782 - val_accuracy: 0.9333\n", + "Epoch 8/10\n", + "612/612 [==============================] - 117s 190ms/step - loss: 0.1281 - accuracy: 0.9532 - val_loss: 0.1750 - val_accuracy: 0.9337\n", + "Epoch 9/10\n", + "612/612 [==============================] - 117s 190ms/step - loss: 0.1160 - accuracy: 0.9613 - val_loss: 0.1711 - val_accuracy: 0.9363\n", + "Epoch 10/10\n", + "612/612 [==============================] - 109s 178ms/step - loss: 0.1053 - accuracy: 0.9649 - val_loss: 0.1689 - val_accuracy: 0.9370\n" + ] + } + ], + "source": [ + "vgg_history = vgg_model.fit(normalized_train_ds,validation_data=normalized_val_ds,epochs=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 507 + }, + "id": "BgXs8MkJBm8h", + "outputId": "6c3dd9c0-ecd7-409f-9909-6e74f16b2246" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_graph(vgg_history)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nK8VEibARWVr" + }, + "source": [ + "# ResNet" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "id": "7XPBAmioRX_y" + }, + "outputs": [], + "source": [ + "from tensorflow.keras.applications import ResNet50\n", + "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D , Dropout\n" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nhA0Cxf_j6bQ", + "outputId": "fce4db1c-9c26-4475-fd1c-9ee1eb5e6f34" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n", + "94765736/94765736 [==============================] - 1s 0us/step\n", + "Model: \"sequential_10\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " resnet50 (Functional) (None, 7, 7, 2048) 23587712 \n", + " \n", + " global_average_pooling2d_8 (None, 2048) 0 \n", + " (GlobalAveragePooling2D) \n", + " \n", + " dense_24 (Dense) (None, 256) 524544 \n", + " \n", + " dropout_14 (Dropout) (None, 256) 0 \n", + " \n", + " dense_25 (Dense) (None, 1) 257 \n", + " \n", + "=================================================================\n", + "Total params: 24112513 (91.98 MB)\n", + "Trainable params: 524801 (2.00 MB)\n", + "Non-trainable params: 23587712 (89.98 MB)\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "# Load a pre-trained ResNet50 model without the top layer\n", + "base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(img_width,img_width, 3))\n", + "\n", + "# Freeze the base model\n", + "base_model.trainable = False\n", + "\n", + "for layer in base_model.layers[-10:]:\n", + " layer.trainable = True\n", + "\n", + "# Add new top layers for binary classification\n", + "resnet_model = Sequential([\n", + " base_model,\n", + " GlobalAveragePooling2D(),\n", + " Dense(256,activation='relu'),\n", + " Dropout(0.25),\n", + " Dense(1, activation='sigmoid')\n", + "])\n", + "\n", + "# Compile the model\n", + "resnet_model.compile(optimizer='adam',\n", + " loss='binary_crossentropy',\n", + " metrics=['accuracy'])\n", + "resnet_model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "KBiDATRtRdGg", + "outputId": "134d7d2e-62b1-4bbd-a94a-1418d0682508" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "612/612 [==============================] - 79s 120ms/step - loss: 0.5743 - accuracy: 0.7083 - val_loss: 0.5280 - val_accuracy: 0.7490\n", + "Epoch 2/10\n", + "612/612 [==============================] - 71s 116ms/step - loss: 0.5329 - accuracy: 0.7405 - val_loss: 0.5133 - val_accuracy: 0.7587\n", + "Epoch 3/10\n", + "612/612 [==============================] - 70s 113ms/step - loss: 0.5084 - accuracy: 0.7575 - val_loss: 0.5264 - val_accuracy: 0.7393\n", + "Epoch 4/10\n", + "612/612 [==============================] - 69s 112ms/step - loss: 0.5053 - accuracy: 0.7561 - val_loss: 0.5558 - val_accuracy: 0.7273\n", + "Epoch 5/10\n", + "612/612 [==============================] - 70s 115ms/step - loss: 0.4909 - accuracy: 0.7696 - val_loss: 0.4865 - val_accuracy: 0.7730\n", + "Epoch 6/10\n", + "612/612 [==============================] - 70s 114ms/step - loss: 0.4783 - accuracy: 0.7749 - val_loss: 0.4829 - val_accuracy: 0.7777\n", + "Epoch 7/10\n", + "612/612 [==============================] - 70s 114ms/step - loss: 0.4819 - accuracy: 0.7725 - val_loss: 0.4864 - val_accuracy: 0.7703\n", + "Epoch 8/10\n", + "612/612 [==============================] - 69s 112ms/step - loss: 0.4699 - accuracy: 0.7814 - val_loss: 0.4682 - val_accuracy: 0.7797\n", + "Epoch 9/10\n", + "612/612 [==============================] - 70s 114ms/step - loss: 0.4616 - accuracy: 0.7865 - val_loss: 0.4671 - val_accuracy: 0.7837\n", + "Epoch 10/10\n", + "612/612 [==============================] - 69s 112ms/step - loss: 0.4557 - accuracy: 0.7909 - val_loss: 0.4808 - val_accuracy: 0.7773\n" + ] + } + ], + "source": [ + "resnet_history = resnet_model.fit(normalized_train_ds,validation_data=normalized_val_ds,epochs=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 507 + }, + "id": "-vxIpiuI0lgy", + "outputId": "7305d3a0-a65c-4187-a84c-ec8264aceebe" + }, + "outputs": [ + { + "data": { + "image/png": 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Gr6+v0aZNG2P27NnZx2zfvt0YNmyYUbVqVcPNzc2oWLGi0bt3b2Pr1q3XjElECp7FMP4xjlFEREqlZs2aUb58eZYvX+7oUEREREREpBRQTSkREWHr1q3s3LmTkSNHOjoUEREREREpJTRSSkSkFNu7dy/btm3jnXfeITY2luPHj+Pu7u7osEREREREpBTQSCkRkVJs7ty5jB49mszMTH744QclpEREREREpNAoKSUiUoq9/PLL2O12Dhw4QOfOnR0djojkwccff0xoaCju7u60adOGzZs3X/XYGTNmYLFYcjz+mYROSkrioYceonLlynh4eFC/fn0+++yzgv4YIiIiUoo5OzoAEREREcmbWbNmMWHCBD777DPatGnD+++/T/fu3Tl06BAVK1a84jk+Pj4cOnQo+7XFYsnx/oQJE/jzzz/57rvvCA0NZcmSJTzwwAMEBwfTt2/fAv08IiIiUjpppJSIiIhIMfPuu+8ybtw4Ro8enT2iydPTk2nTpl31HIvFQmBgYPYjICAgx/vr16/n7rvvpkuXLoSGhjJ+/HiaNGlyzRFYIiIiIjdCI6WuwG63c/r0aby9vS+7iygiIiKlh2EYJCYmEhwcjNVaNO7lZWRksG3bNiZOnJi9z2q10q1bNzZs2HDV85KSkggJCcFut9O8eXNef/11GjRokP1++/bt+fnnnxkzZgzBwcGsXLmSw4cP89577+U6NvWhREREBHLfh1JS6gpOnz5NlSpVHB2GiIiIFBGnTp2icuXKjg4DgNjYWGw222UjnQICAjh48OAVz6lTpw7Tpk2jcePGxMfHM2XKFNq3b8++ffuyP9f//vc/xo8fT+XKlXF2dsZqtTJ16lQ6dep01VjS09NJT0/Pfh0ZGUn9+vXz4VOKiIhISfBvfSglpa7A29sbML88Hx8fB0cjIiIijpKQkECVKlWy+wbFVbt27WjXrl326/bt21OvXj0+//xzXn31VcBMSm3cuJGff/6ZkJAQVq9ezYMPPkhwcDDdunW7YruTJ0/mlVdeuWy/+lAiIiKlW277UEpKXcGl4eY+Pj7qUImIiEiRmorm5+eHk5MT0dHROfZHR0cTGBiYqzZcXFxo1qwZR48eBSA1NZXnnnuOBQsW0KtXLwAaN27Mzp07mTJlylWTUhMnTmTChAnZry91QNWHEhEREfj3PlTRKI4gIiIiIrni6upKixYtWL58efY+u93O8uXLc4yGuhabzcaePXsICgoCIDMzk8zMzMtqPjg5OWG326/ajpubW3YCSokoERERySuNlBIREREpZiZMmMDdd99Ny5Ytad26Ne+//z7JycmMHj0agJEjR1KpUiUmT54MwKRJk2jbti01a9YkLi6Ot99+m7CwMO655x7AHB3euXNnnnrqKTw8PAgJCWHVqlV88803vPvuuw77nCIiIlKyKSklIiIiUswMHTqUs2fP8uKLLxIVFUXTpk1ZtGhRdvHz8PDwHKOeLly4wLhx44iKiqJcuXK0aNGC9evX5yhK/uOPPzJx4kSGDx/O+fPnCQkJ4bXXXuO+++4r9M8nIiIipYPFMAzD0UEUNQkJCfj6+hIfH3/NYeg2m43MzMxCjEwKiouLC05OTo4OQ0REipjc9gnElJvvS/2nksXV1fWaS32LiEjplNs+lEZKXQfDMIiKiiIuLs7RoUg+Klu2LIGBgUWqmK2IiEhJof5TyWS1WqlWrRqurq6ODkVERIohJaWuw6UOVcWKFfH09FQSo5gzDIOUlBRiYmIAsou+ioiISP5R/6nksdvtnD59mjNnzlC1alX9m4qISJ4pKZVHNpstu0NVoUIFR4cj+cTDwwOAmJgYKlasqKl8IiIi+Uj9p5LL39+f06dPk5WVhYuLi6PDERGRYkYTwPPoUg0ET09PB0ci+e3Sv6nqXIiIiOQv9Z9KrkvT9mw2m4MjERGR4khJqeuk4cklj/5NRURECpZ+a0se/ZuKiMiNUFJKREREREREREQKnZJSct1CQ0N5//33HR2GiIiISLGiPpSIiIhJSalSwGKxXPPx8ssvX1e7W7ZsYfz48fkbrIiIiEgRoT6UiIhIwdLqe6XAmTNnsrdnzZrFiy++yKFDh7L3lSlTJnvbMAxsNhvOzv/+n4a/v3/+BioiIiJShKgPJSIiUrA0UqoUCAwMzH74+vpisViyXx88eBBvb2/++OMPWrRogZubG2vXruXYsWP069ePgIAAypQpQ6tWrVi2bFmOdv859NxisfDll18yYMAAPD09qVWrFj///HMhf1oRERGR/KE+lIiISMFSUiofGIZBSkZWoT8Mw8i3z/Dss8/yxhtvcODAARo3bkxSUhK33XYby5cvZ8eOHfTo0YM+ffoQHh5+zXZeeeUVhgwZwu7du7ntttsYPnw458+fz7c4RURE/s4wDLaFXWD21lOODkXyKK/9p3NJaUScT1EfSkREpATR9L18kJppo/6Liwv9uvsndcfTNX/+CSdNmsStt96a/bp8+fI0adIk+/Wrr77KggUL+Pnnn3nooYeu2s6oUaMYNmwYAK+//joffvghmzdvpkePHvkSp4iICEDEhRQWbI9k/o5ITsQm4+5i5bZGQZRxU9emuHBU/wnUhxIRESkq1HMTAFq2bJnjdVJSEi+//DK//fYbZ86cISsri9TU1H+9y9e4cePsbS8vL3x8fIiJiSmQmEVEpHRJSs/ijz1nmLc9go3H/xpB4uHiRM+GgSSlZSkpJYVOfSgREZHrp55bPvBwcWL/pO4OuW5+8fLyyvH6ySefZOnSpUyZMoWaNWvi4eHB7bffTkZGxjXbcXFxyfHaYrFgt9vzLU4RESldbHaDDcfOMW97BIv2RpGaact+r131CgxqUZkeDQOVjCqG8tp/ik/J5NSFFFycrNQOKIPFYrmha+cX9aFERESun3pw+cBiseTbEPCiYt26dYwaNYoBAwYA5l2/kydPOjYoEREpNY7GJDF/ewQLdkRyJj4te381Py8GNa9E/2aVqFzO04ERyo3Ka//J3cWJCymZZNntZNnB16No9r3UhxIREcm9ovlrLg5Xq1Yt5s+fT58+fbBYLLzwwgu6WyciIgUqLiWDX3adZu72SHadisve7+PuTO8mwQxqXpnmVcve0AgZKb6sFgvlvFw4m5jO+eQMfD1c/v0kB1AfSkREJPeUlJIrevfddxkzZgzt27fHz8+PZ555hoSEBEeHJSIiJUymzc7KQ2eZty2CPw/GkGEz/3h3slroXNufQc0rc0u9irjn43QrKb7Ke7lyNjGdxLRM0rNsuDkXvf8u1IcSERHJPYuRn2vilhAJCQn4+voSHx+Pj49PjvfS0tI4ceIE1apVw93d3UERSkHQv62ISOEwDIN9pxOYtz2Cn3ee5lzyX7V26gX5MKh5Jfo1rYS/t5sDozRdq08gl7va95Wfv7EnYpNJTMvE39uNIF+PGw1ZbpD6TyIiciW57UNppJSIiIgUipiENBbujGTetkgORSdm7/cr40b/psEMbF6Z+sFK/Mi1lfdyJTEtkwvJmQT4uGPVdE4REZFiS0kpERERKTBpmTaW7I9m3rYI1hw5i/3i+GxXZyu31g9gUPNKdKrlj7OT1bGBSrHh4+6Mi5OVTJudhNRMynq6OjokERERuU5KSomIiEi+MgyDrWEXmLctgt92nyExPSv7vRYh5RjYvBK9GwXj61k0C1VL0WaxWCjv5Up0QhrnkjKUlBIRESnGlJQSERGRfHHqfArztkcwf3sk4edTsvdXKuvBwOaVGNi8MtX8vBwYoZQU5T1diUlIJzkji7RMmwrhi4iIFFNKSomIiMh1S0zL5Pc9Z5i3PZLNJ85n7/dydaJnoyAGNa9Mm2rlsVpV90fyj4uzFR8PZ+JTMzmXnEGlsip4LiIiUhwpKSUiIiJ5YrMbrDsay7ztESzeF0Vaph0AiwU61PBjUItKdG8QiKeruhlScMp7uRKfmklccgaBPu44KfEpIiJS7Ki3KCIiIrlyJDqRudsjWLgjkuiE9Oz9Nfy9GNSiMv2bViJYI1akkJRxc8bV2UpGlp341AzKe7k5OiQRERHJIyWlRERE5KrOJ2fw885I5m2PZE9kfPZ+Xw8X+jYJZlCLyjSp7IvFolEqUrgsFgsVvFw5E28WPFdSSkREpPhRUkpERERyyMiys+JQDPO2RbDiUAyZNgMAZ6uFLnUqcnuLSnStWxE3ZxWXFscq5+lKVEI6qZk2UjKyNGVURESkmLE6OgApPrp06cJjjz2W/To0NJT333//mudYLBYWLlx4w9fOr3ZEROTKDMNgd0QcL/20lzavL+Peb7exZH80mTaDhpV8eKlPfTY9dwtf3t2SHg2DlJCSIsHZyYqvhwsA55MyHBzNlan/JCIicnW6nVRK9OnTh8zMTBYtWnTZe2vWrKFTp07s2rWLxo0b57rNLVu24OWVv0t7v/zyyyxcuJCdO3fm2H/mzBnKlSuXr9cSERGIik9jwY5I5m+P4EhMUvb+it5uDGhWiYHNK1Mn0NuBEYpcWwUvV+JSMohLzSTQbsfZmn/3XNV/EhERKVhKSpUSY8eOZdCgQURERFC5cuUc702fPp2WLVvmqUMF4O/vn58hXlNgYGChXUtEpKRLzbCxeF8U87ZHsO5oLHZzdh5uzla6NwhkYPNKdKzph7OTBlRL0efp6oS7ixNpmTbiUjLxK5N/taXUfxIRESlY6m2WEr1798bf358ZM2bk2J+UlMScOXPo378/w4YNo1KlSnh6etKoUSN++OGHa7b5z+HnR44coVOnTri7u1O/fn2WLl162TnPPPMMtWvXxtPTk+rVq/PCCy+QmZkJwIwZM3jllVfYtWsXFosFi8WSHe8/h5/v2bOHm2++GQ8PDypUqMD48eNJSvrrDv+oUaPo378/U6ZMISgoiAoVKvDggw9mX0tEpDQKP5fCpF/20/q1ZTw2aydrjpgJqdah5XljYCO2/LcbHw5rRpc6FZWQkmLDYrFQ3ssVgHNJGRiGkW9tq/+k/pOIiBQsjZTKD4YBmSmFf10XT8jlakfOzs6MHDmSGTNm8Pzzz2evkjRnzhxsNhsjRoxgzpw5PPPMM/j4+PDbb79x1113UaNGDVq3bv2v7dvtdgYOHEhAQACbNm0iPj4+R/2ES7y9vZkxYwbBwcHs2bOHcePG4e3tzdNPP83QoUPZu3cvixYtYtmyZQD4+vpe1kZycjLdu3enXbt2bNmyhZiYGO655x4eeuihHJ3GFStWEBQUxIoVKzh69ChDhw6ladOmjBs3LlffmYhISWAYBhuOnWPaupMsPxjNpb/Xq5T3YGCzygxsXomQCvk7lUgkV/Kx/1TO2U50VioZmQbJSXbKuP1LFzeXfSj1n9R/EhGRgqWkVH7ITIHXgwv/us+dBtfc/yExZswY3n77bVatWkWXLl0Ac+j5oEGDCAkJ4cknn8w+9uGHH2bx4sXMnj07V52qZcuWcfDgQRYvXkxwsPldvP766/Ts2TPHcf/973+zt0NDQ3nyySf58ccfefrpp/Hw8KBMmTI4Oztfc7j5999/T1paGt988012TYaPPvqIPn368OabbxIQEABAuXLl+Oijj3BycqJu3br06tWL5cuXq1MlIqVCWqaNn3ZGMn3dSQ5GJWbv71zbnzEdq3FTTT+s1tzd2BApEPnYf3ICGuTlhDz0odR/Uv9JREQKjpJSpUjdunVp374906ZNo0uXLhw9epQ1a9YwadIkbDYbr7/+OrNnzyYyMpKMjAzS09Px9PTMVdsHDhygSpUq2R0qgHbt2l123KxZs/jwww85duwYSUlJZGVl4ePjk6fPceDAAZo0aZKjSGiHDh2w2+0cOnQou1PVoEEDnJz+Wh0qKCiIPXv25OlaIiLFTXRCGt9uCOP7zeGcTzZXI/NwcWJQi0qMal+NmhXLODhCkeJF/Sf1n0REpOAoKZUfXDzNO26OuG4ejR07locffpiPP/6Y6dOnU6NGDTp37sybb77JBx98wPvvv0+jRo3w8vLiscceIyMj/5ZX3rBhA8OHD+eVV16he/fu+Pr68uOPP/LOO+/k2zX+zsXFJcdri8WC3W4vkGuJiDjazlNxTF93gt92nyHrYuXySmU9uLt9CENbVsXX0+VfWhApZAXQfzp2NpmUjCwCfN2oWMb92tfOA/Wf1H8SEZGCoaRUfrBY8jSNzpGGDBnCo48+yvfff88333zD/fffj8ViYd26dfTr148RI0YAZo2Dw4cPU79+/Vy1W69ePU6dOsWZM2cICgoCYOPGjTmOWb9+PSEhITz//PPZ+8LCwnIc4+rqis1m+9drzZgxg+Tk5Oy7fevWrcNqtVKnTp1cxSsiUhJk2uws2hvF9HUn2B4el72/dWh5RncI5db6ASpYLkVXAfSfypV1IflCCuczrPi7eGbXgLpR6j+JiIgUDPVUS5kyZcowdOhQJk6cyJkzZxg1ahQAtWrVYunSpaxfv54DBw5w7733Eh0dnet2u3XrRu3atbn77rvZtWsXa9asydF5unSN8PBwfvzxR44dO8aHH37IggULchwTGhrKiRMn2LlzJ7GxsaSnp192reHDh+Pu7s7dd9/N3r17WbFiBQ8//DB33XVX9tBzEZGS7EJyBp+sPEqnt1bw8A872B4eh4uThYHNK/Hrwx2ZfV87ejYKUkJKSp2yHi44WS1kZNlJSs/Kt3bVfxIRESkY6q2WQmPHjuXChQt07949u4bBf//7X5o3b0737t3p0qULgYGB9O/fP9dtWq1WFixYQGpqKq1bt+aee+7htddey3FM3759efzxx3nooYdo2rQp69ev54UXXshxzKBBg+jRowddu3bF39//issqe3p6snjxYs6fP0+rVq24/fbbueWWW/joo4/y/mWIiBQjh6MTmTh/D+3eWM5biw5xJj4NvzKuPHpLLdY9ezPvDmlKw0qXr7olUlpYrRbKeboCcC4p/6bQgfpPIiIiBcFiGJcWh5ZLEhIS8PX1JT4+/rIikmlpaZw4cYJq1arh7n6NWgVS7OjfVkSKIrvdYOXhGKavO8maI7HZ++sH+TCmYzX6NAnCzdnpGi3IjbhWn0Aud7XvqzB/Y9MybRyOTsQC1An0wdVZ92ALkvpPIiJyJbntQ6mmlIiISBGUlJ7FvG0RzFh/khOxyQBYLfCf+oGM7hBK62rl861ejkhJ4u7ihJebM8npWZxPySDQR4kSERGRokpJKRERkSLk1PkUvl5/kllbTpF4sSaOt7szd7Sqwsh2oVQpn/eVV0VKmwperiSnZ3EhOYOK3m5YlcAVEREpkpSUEhERcTDDMNh04jzT151g6f5o7Bcn1lf382JUh1AGNa+Ml5t+skVyy8fDBWerlUybncS0THw9XB0dkoiIiFyBergiIiIOkpZp45ddp5m27iQHziRk77+plh9jOlSjc21/rNYCGOGRfA7cfcFJ3QApmawWC+W9XIhJTOdcUoaSUiIiIkWUeqMiIiKFLCYhje82hjFzUzjnks0VwtxdrAxsXpnR7UOpFeCd/xc9dwz2zod98yFmP5QNgVtehAYDwapC0FLylPdyJSYxnaT0LNIzbbi5aEEAERGRokZJqetkt9sdHYLkM/2bikhB2xMRz7R1J/h192kybeYcvSBfd0a2C2VY6yqU9czn0RwXwmDfAtg7D6J253wvLgzmjYV1H8Ctr0CNm/P32iJXUJi/ta7OTni7u5CYlsn5lAyCfD0K7dqliRbyFhGRG6GkVB65urpitVo5ffo0/v7+uLq6avWjYs4wDDIyMjh79ixWqxVXVw3xF5H8k2Wzs2R/NNPWnmBr2IXs/S1CyjG6QyjdGwTi4pSPI5USTl9MRM2HyK1/7bc4QfXO5sioGjfDru9h7QdmsurbAVC9C3R7GYKb5V8sIhc5qv/k7WwnISuDc/GZ+LpQMNNhSzHDMDh79iwWiwUXFxdHhyMiIsWQxdDtjcskJCTg6+tLfHw8Pj4+l72fkZHBmTNnSElJcUB0UlA8PT0JCgpSUkpE8kVcSgY/bjnFtxvCiIxLBcDZaqF34yBGd6hGkypl8+9iSTGw/yczERW+Abj0026B0I7QcCDU6wtefjnPS46FNe/A5qlgzzT3NRwEN/8XylfPv/iKsX/rE0hO1/q+HNF/MgyITkgjy25Q3ssFT1fdj81vFouFypUrU6ZMGUeHIiIiRUhu+1BKSl1Bbr48wzDIysrCZrMVcnRSEJycnHB2dtaoNxG5YUdjEpm+7iTzt0eSmmn+RpT3cmV4m6qMaBtCgI97/lwo5Twc+NmcmndyLRh/mxZVpa2ZiKrfD7wD/72tCydhxeuwezZggNUZWo6BTk9DGf/8ibeYUlIqb/7t+3JE/+m7DSeZvv4kDYJ9+XCYRgLmNxcXF5ycVK9LRERyym0fSreLrtOlYcoaqiwiIna7waojZ5m+7iSrD5/N3l830JsxHarRt2kw7vlRZDk1Dg7+ZhYrP74S7Fl/vRfc3ExENRgAvpXz1m65UBj4BbR7CJa/AkeXweYvYOf30P5haPcguBVA8XUpdRzRf+rbIpQ3lh4n8tB5TlzIoF6QkosiIiJFhZJSIiIi1yk5PYv52yOYvv4kx88mA2CxQLd6AYzpUI221cvf+AjM9EQ4tMhMRB1dBraMv94LbGTWiGowAMpXu7HrAAQ1hhHz4PgqWPoinNkJKyfDli+h8zPQ/G5w1hRnKV4q+rjznwYB/L4niu83hfNq/4aODklEREQuUlJKREQkjyIupPDNhjB+2BxOYpo5WqmMmzNDWlZhVPtQqlbwvLELZKTAkcVmjagjSyAr7a/3/OuaiaiGA8Gv1o1d52qqd4ZxK2D/Qlg+CS6cgN+fhA0fwy0vQP0BYM3H4uwiBWx4mxB+3xPFgh2RPNuzLl5u6gKLiIgUBepRioiI5IJhGGw+cZ77v9tGp7dW8MXq4ySmZRFSwZOX+9Rn43O38GKf+tefkMpKN6fmzR0Lb9eEOaPMmlFZaVC+BnR6Cu7fAA9ugi7PFFxC6hKr1Ux8PbQFbpsCXv5mcmruGJja1Zw+KA718ccfExoairu7O23atGHz5s1XPXbGjBlYLJYcD3f3y+ubHThwgL59++Lr64uXlxetWrUiPDy8ID9GoWhfowLV/bxISs/ip52nHR2OiIiIXKTbRCIiIteQabPz887TTF9/gr2RCdn7O9SswJgO1ehap+L1LzNvyzSTO3vnmQmp9L/ax7cqNBxgjooKamLOC3QEJxdoPQ6aDDNHSq3/0JzW900/qHEzdHvZjE8K1axZs5gwYQKfffYZbdq04f3336d79+4cOnSIihUrXvEcHx8fDh06lP36n1NLjx07RseOHRk7diyvvPIKPj4+7Nu374rJq+LGYrFwZ5uq/N9vB/huYxjDWlfR4iYiIiJFgFbfuwKttCMiIgCRcak8OHM7O0/FAeDmbGVAs0qM6hBK3cDr/H2wZcHJNWaNqAO/QOqFv97zDjbrQzUcCJVaOC4RdS1JZ2H127B1GtgzzX2NhsDNz5sF00uYotonaNOmDa1ateKjjz4CwG63U6VKFR5++GGeffbZy46fMWMGjz32GHFxcVdt84477sDFxYVvv/32uuMqqt8XwIXkDNpMXk5Glp0FD7SnWdVyjg5JRESkxMptn0DT90RERK7gz4PR9PpwDTtPxeHj7sxT3euwYeItvDGocd4TUnY7nFwHvz0B79aFb/vD9m/MhJSXP7QaB6P/gMf3QY/XoXLLopmQAijjD7e9BQ9thoa3m/v2zIb/tYQ/noXkWMfGVwpkZGSwbds2unXrlr3ParXSrVs3NmzYcNXzkpKSCAkJoUqVKvTr1499+/Zlv2e32/ntt9+oXbs23bt3p2LFirRp04aFCxcW5EcpVOW8XOndOAiAmZuK/5REERGRkkBJKRERkb/Jstl5c9FBxszYSlxKJo0r+/LbIzfxYNealPfKw8pzhgGnNpuJmvfqw4zbzFXsks+CRzloMQpG/gxPHIJeUyCkffEqHl6+Otz+FYxfBdW7mqOmNn0KHzSFVW9DRrKjIyyxYmNjsdlsBAQE5NgfEBBAVFTUFc+pU6cO06ZN46effuK7777DbrfTvn17IiIiAIiJiSEpKYk33niDHj16sGTJEgYMGMDAgQNZtWrVVWNJT08nISEhx6MoG94mBIBfdp0mLiXjX44WERGRgubw3m9einR26dLlsiKdFouFXr16ZR+TlJTEQw89ROXKlfHw8KB+/fp89tlnhfFRRESkmItOSOPOqZv4dOUxAEa1D2XOfe2oUj6XxcsNA07vgCUvwPuN4KtbzURN4hlw84Wmw2H4PHjyCPT5wFzlzupUgJ+oEAQ3hZEL4a4FZm2pjERY8X9mcmrLV2bdLHG4du3aMXLkSJo2bUrnzp2ZP38+/v7+fP7554A5UgqgX79+PP744zRt2pRnn32W3r17X7MfNXnyZHx9fbMfVapUKZTPc72aVy1LvSAf0rPszNse6ehwRERESj2HFjrPa5HO+fPnk5Hx112tc+fO0aRJEwYPHpy9b8KECfz555989913hIaGsmTJEh544AGCg4Pp27dvoXwuEREpftYcOctjP+7kXHIGZdyceXNQY3pdnOpzTYYB0fvMGlH7FsD543+951oG6vQ0i5XXvAWc3QruAzhajZuhWhfze/jzVbhwEn6bYBZHv+VFqN+v6E5JLGb8/PxwcnIiOjo6x/7o6GgCAwNz1YaLiwvNmjXj6NGj2W06OztTv379HMfVq1ePtWvXXrWdiRMnMmHChOzXCQkJRToxZbFYGN6mKv9duJeZm8IY0yFUBc9FREQcyKEjpd59913GjRvH6NGjs0c0eXp6Mm3atCseX758eQIDA7MfS5cuxdPTM0dSav369dx999106dKF0NBQxo8fT5MmTa45AktEREovm93gvaWHGTltM+eSM6gX5MMvD3f894TU2cOw8g34uA181gHWvGMmpJw9oH5/GPINPHUUBn0JdW8r2QmpS6xWaHQ7PLgFer4Nnn5w/hjMuRu+vAVOrHF0hCWCq6srLVq0YPny5dn77HY7y5cvp127drlqw2azsWfPHoKCgrLbbNWqVY7V+QAOHz5MSEjIVdtxc3PDx8cnx6Oo69+sEl6uThw/m8yG4+ccHY6IiEip5rCRUpeKdE6cODF7X26KdP7dV199xR133IGXl1f2vvbt2/Pzzz8zZswYgoODWblyJYcPH+a99967ajvp6emkp6dnvy7q9RBERCR/nE1M57FZO1h31PzDdFjrKrzUpwHuLleZUnf+hDkSaO98iN77134nV6h5q7lqXu0e4FamEKIvwpxdoc14aDoM1n8E6/8Hkdvg697m99TtJQhs5Ogoi7UJEyZw991307JlS1q3bs37779PcnIyo0ePBmDkyJFUqlSJyZMnAzBp0iTatm1LzZo1iYuL4+233yYsLIx77rknu82nnnqKoUOH0qlTJ7p27cqiRYv45ZdfWLlypSM+YoEp4+ZM/2aVmLkpnJmbwmlfw8/RIYmIiJRaDktKXatI58GDB//1/M2bN7N3716++uqrHPv/97//MX78eCpXroyzszNWq5WpU6fSqVOnq7Y1efJkXnnllev7ICIiUixtOn6Oh3/YQUxiOh4uTrw2oCEDm1e+/MC0eHOlvL3zzHpRl1idzSlrDQaaI6HcfQsv+OLCzRu6ToRWY2HVW7BtOhxdCkeXQeOh0PU5KHf1UThydUOHDuXs2bO8+OKLREVF0bRpUxYtWpTdrwoPD8f6t8L5Fy5cYNy4cURFRVGuXDlatGjB+vXrc0zXGzBgAJ999hmTJ0/mkUceoU6dOsybN4+OHTsW+ucraMPbhDBzUziL90YRk5hGRW93R4ckIiJSKlkMwzAcceHTp09TqVIl1q9fn2Oo+dNPP82qVavYtGnTNc+/99572bBhA7t3786xf8qUKUydOpUpU6YQEhLC6tWrmThxIgsWLMixdPLfXWmkVJUqVYiPjy8Ww9BFRCT37HaDz1YfY8riQ9gNqFmxDJ8Ob06tAO/LD47eD7OG/1UnymKFap3MRFS9PuBZvnCDL+7OHYM//88cbQbmCLNW4+CmJ8CrgmNju4qEhAR8fX3VJ8il4vR9DfxkHdvD43iqex0e7FrT0eGIiIiUKLntEzhspNSNFOlMTk7mxx9/ZNKkSTn2p6am8txzz7FgwYLsFfkaN27Mzp07mTJlylWTUm5ubri5lYJaHyIipdyF5AwmzN7JikNnARjYrBL/N6Ahnq5X+DnctwAWPgiZyeBbBTo8ahbrLnP5QhySSxVqwODp0P5hWPYSnFgNGz+GHd+a32/b+8HV69/bEckHw9uEsD08ju83hXNf5xo4WVXwXEREpLA5rND5jRTpnDNnDunp6YwYMSLH/szMTDIzM3MMVwdwcnLKXupYRERKp+3hF+j14RpWHDqLm7OVNwY24p0hTS5PSNltsPRFmDPKTEhV6wzjV0HrcUpI5ZdKzWHkzzBiHgQ0gvQEc8W+D5vD1ulgy3J0hFIK9GochK+HC5Fxqaw6HOPocEREREolh66+N2HCBKZOncrXX3/NgQMHuP/++y8r0vn3QuiXfPXVV/Tv358KFXIO9ffx8aFz58489dRTrFy5khMnTjBjxgy++eYbBgwYUCifSUREihbDMPhyzXGGfLaB0/FpVPPzYsEDHbijddXLl4JPOQ/fDYR1H5iv2z8MI+YX2allxZrFAjW7wb2rYeBUKFsVkqLg18fgk7aw/2dwTIUBKSXcXZwY3MKsIzdzY7iDoxERESmdHDZ9D/JepBPg0KFDrF27liVLllyxzR9//JGJEycyfPhwzp8/T0hICK+99hr33XdfgX8eEREpWuJTM3l67i4W7zOnivdqFMQbgxrh7e5y+cFndsGsERAXDi6e0Pd/0Oj2Qo64FLJaofEQc2rk1mlmQfRzR2D2XVC5FXR7BUI7ODpKKaHubFOVL9ee4M9DMURcSKFyOU9HhyQiIlKqOKzQeVFWnIp0iojIle2JiOeB77dx6nwqLk4WXuhdn7vahlw+Ogpg1yz45RHISoNyoTB0JgQ2LPSYBUhLgPUfwoaPITPF3Fe7B9zyEgTUv/a5BUB9grwpjt/X8C83su7oOR7qWpMnu9dxdDgiIiIlQm77BA6dviciIpLfDMPg2w0nGfTpek6dT6VyOQ/m3teeke1CL09I2TLhj2dhwXgzIVWzG4xboYSUI7n7wM3/hUd2QMsxYHGCw4vg0/aw4H6IO+XoCKWEGd4mBIAft5wiI0s1SOVvbJmaRiwiUsCUlBIRkRIjKT2Lh3/YwQs/7SPDZqdbvQB+e/gmmlQpe4WDz8I3/WHTp+brm56EO2eDZ/nCDFmuxjsQer8HD242p/ZhwK7v4X8tYPHzZv0vkXxwa/0A/L3diE1KZ+n+6H8/QUqHC2HwXgP4YZijIxERKdGUlBIRcaT0JNj1I5xY4+hIir0DZxLo+7+1/Lr7DE5WC8/fVo+pI1vg63mF+lGR2+CLzhC2FlzLwNDv4JYXwOpU+IHLtfnVhCHfwD3LIaQj2NJhw0fwQVNY+x5kpjo6QinmXJys3NGqCgAzN4U5OBopMpa+CEnRcPgPJcFFRAqQklIiIo5wegf88ii8UwcW3Atf94a5YyBRd+nzyjAMZm85Rf+P13E8NpkgX3dm39uWcZ2qX7l+1PZvYVpPSIiECrVg3J9Qr0/hBy55U7kljPoVhs+Fig0gPR6WvQwfNoft34Aty9ERSjF2R+uqWC2w/tg5jp1NcnQ44mhh62H/wr9eh29wWCgiIiWdklIiIoUlLQG2fAWfd4IvusC2GZCRBGWrgsUKe+fBR63MY+yqa5IbKRlZPDlnN0/P2016lp3Otf357ZGbaBFyhSl4WRnw6wT4+SFztE2d22DccvBXYeNiw2KBWrfCfWtgwOfgWwUST8PPD5s1p2KPOjpCKaYqlfXg5roVAfh+U7iDoxGHstth0bPmtpOr+Ry23nHxiIiUcEpKiYgUJMOAiG3w00PmqKjfJsCZXeDkBo0Gw6jf4NHdZnHt4Gbm6I/fJsC07hC9z9HRF2lHYxLp//E65m2PwGqBp7rXYfqoVpT3cr384MQoczTa1q8AC3R93lxhz9230OOWfGB1giZ3wENbofvr4FEO0hPBt5KjI5Ni7FLB87nbIkjLtDk4GnGYXd+bv9NuPnDrJHNf2DrHxiQiUoI5OzoAEZESKTUO9swxR0NF7/1rv18daDHK/IP67wW1g5uaNXO2fAnLX4WIzeaIqnYPQednwNWzcOMv4hbuiOS5BXtIybDh7+3Gh3c0o12NClc+OHwTzB4JSVHg5guDpkLt7oUbsBQMF3do9yA0GwHnjoKLh6MjkmKsU21/KpX1IDIulV93n+H2FpUdHZIUtvREWH4xEdX5aajX1xw1dWaX+Z6bt2PjExEpgZSUEhHJL4YBpzabiah9CyDrYgFmZ3doMACa3w1V25pTkK7E6gRt7oW6vWHRM3DgF1j3PuybD73eNactlXJpmTZe+WU/P2w2p9e0r1GBD+5ohr+32+UHG4Y5MuqPZ8GeCf714I6ZUKFGIUctBc7dFyq1cHQUUsw5WS3c2aYqby8+xMxNYUpKlUZr3jWLm5evDq3vBWdXKBcKF06aNzhqdXN0hCIiJY6SUiIiNyrlPOyebSajzh74a3/F+uaoqMZDzOlFueVbyVwN7tAf8PtTEBcOM283E1s93gDvwPz+BMXCydhkHpi5nf1nErBY4OGba/HoLbVwsl4hyZeZBr8/ATu+M1/X7wf9PgG3MoUbtIgUK0NaVuH9ZYfZER7HvtPxNAjWFN9S48JJ2PCxuf2f18yEFEBIB/O9sHVKSomIFAAlpURErodhmKvxbJsB+xaahbMBnD2g4SAzGVW55dVHReVGnZ4QehOsnAwbPzVHXx1dDt1eghZjwFp6ygL+vucMT8/dTVJ6FuW9XHl/aFM61fa/8sHxETDrLji93Swgf8tL0OHRG/u3EJFSwd/bje4NAvl19xlmbgrn9QGNHB2SFJalL5q/5dU6m7+/l4S0h50zVexcRKSAKCklIpIXyedg1w+w/WuIPfzX/oBG0OJuc1RUfhbPdisD3V8z2/3lMTPR8tsTsOtH6P0+BDbMv2sVQRlZdl7//QAz1p8EoFVoOf43rDmBvu5XPuHkWph9N6TEmqPTbp8GNW4uvIBFpNgb3iaEX3efYeGOSCb2rIu3u4ujQ5KCdnIt7P/JvJHRY3LOmxghHcznyG2QmaradSIi+UxJKRGRf2MYcHINbPsaDvwMtgxzv4sXNLo4Kiq4ecGOxAlqAvcsgy1fmUVYI7aYhdDbXyqE7lVw13aQU+dTeOiHHew6FQfAvZ2r8+R/6uDidIURYoYBmz6Dxc+DYTOThHd8Z9YCERHJg7bVy1PD34tjZ5NZuPM0d7UNcXRIUpDsNrOYOZi/5wENcr5fLhS8gyHxtPnbW61TYUcoIlKilZ65HyIieZV0FtZ9AP9rAV/3gb1zzYRUUFNzlNKTh6Dv/8wCy4UxNczqBG3Gw0ObzRpJhs2M75O2cHhJwV+/EC3bH03v/61l16k4fD1c+OrulkzsWe/KCamMFJg/3vyjwrBBoyEwdokSUiJyXSwWC8PbmImomRvDMAzDwRFJgdo5E6L2mKuzdn3+8vctFnMKH2gKn4hIAdBIKRGRv7Pb4cQqs1bUwd/MVdsAXL2h8WBzBb3gpo6MEHyCYcg3cGgR/P6kWQj9+8FQv79ZCN0nyLHx3YBMm50piw/x+erjADSpUpaP72xG5XKeVz7hQhjMGm7+QWFxgv/8H7S9X/WjROSGDGpembcWH+RgVCLbwy/QIqS8o0OSgpCWYI4+Buj8NHj5Xfm4kPbmjamwdYUXm4hIKaGklIgIQGI07PwOtn9jrrJzSaUW5nD+BgMLZOU2wzDYHn6BjCyDhpV88la7pE4PqHaxEPqGT2D/Qjj2J9zyIrQcY46sKkbOxKfy8Pc72Bp2AYDRHUKZ2LMers5XGdR77E+YOwZSL4CnHwyeYX4fIiI3yNfThT6Ng5mzLYKZG8OVlCqp1rwDyWehQk1oPf7qx4V2NJ9PbYGsjL9W5hMRkRumpJSIlF52Oxz/0xwVdegPsGeZ+918oPFQs3B5YMGsvJSRZefnXaf5cs1xDkYlAubgnup+XjSuXJZGlXxpUsWX+kG+eLheI7nk6mWODmo0BH59zCzE+vuTZiH0Pu8XWPz5bfXhszw2ayfnkzPwdnPmrdsb07PRVUZ8GYY5bXH5K2DYIbgZDP0OfCsXbtAiUqINbxvCnG0R/LrnDC/0rk85LyUiSpTzx2HjJ+b2f167dqLJrzZ4VoCUc3B6B1RtUzgxioiUAkpKiUjpk3AGdlwcFRUf/tf+Km3M6XkN+hdY4fD41Ey+3xTOjPUniE5IB8DT1YmyHi6cjk/j2Nlkjp1NZsGOSACsFqgd4E3jyr40qlyWJpV9qRPojZvzPxJVQY1h7FLYOs2cihC5FT7vDO0egC4Ti2whdJvd4INlh/nfiqMYBtQP8uGT4c0J9btKvOlJ8NOD5qgwgKYjoNc74HKV1fhERK5Tk8q+NKzkw97IBOZui2Bcp+qODkny09IXzTqR1btC7e7XPvZSXakDv5hT+JSUEhHJN0pKiUjpYLfB0WXmqKjDi82C2ADuvtBkmJmMCqhfYJc/dT6F6etOMmtLOMkZ5rUrersxukM17mxdFV9PF2KT0tkTEc/uiHj2RMaxKyKes4npHIxK5GBUIrO3RgDg4mShbqAPjSv7msmqSmWpHVAGZycnaD0O6vY2i37vXwjr/wf7FsJtU8zpfkVITGIaj/6wkw3HzwFwZ5uqvNi7Pu4uVxkZdu4Y/Dgczh4Aqwv0fANajlX9KBEpEJcKnk+cv4fvN4cztmM1rFb9/02JcGKNmWCyWKHH5Nz9joR0uJiUWg83TSj4GEVESgklpUSkZIuPuDgq6ltIiPhrf9X2Zq2o+n3BxaPALr87Io4vVh/nj71R2OzmCk51ArwZ16k6fZsE56iX5FfGja51K9K1bkXArDcVnZDOrog4M1kVGc/uiDjiUjLZExnPnsh4Zm4yz3V3sVI/yIfGlcuayaouH1G98TCsfzxljgb7YSjU6ws93zQLpTvYhmPneOTHHZxNTMfT1YnXBzSif7NKVz/h8GKYNw7S46FMgFnovWrbwgtYREqlvk2Cee23A5yITWb9sXN0rHWVQthSfNhtsGiiud1yDFSsl7vzQjqYz+EbwZYFTvozSkQkP+j/TUWk5LFlwZEl5qioo0vNukMAHuWgyZ1mrSj/OgV2ebvdYMWhGL5YfZxNJ85n7+9Y049xnarTqZYfllzclbVYLAT6uhPoG0j3BoGAmaiKuJDK7ggzQbU7Ip69kfEkpmexPTyO7eFx2ed7uTrRIuhD7veYQ5voH7Ee+Bnj2Aost7wIrcY6pBC63W7w6apjvLPkEHYDageU4ZPhLahZ8SpF5O12WDMFVrwOGFC5tZmQKsYrDIpI8eHl5szA5pX4ZkMYMzeFKSlVEuz4FqL3mCOluzyX+/MCGoCbr3lzJHqPWc9QRERumJJSIlJyXAgzO5s7voPEM3/tD73JHBVVt3eB1h5Ky7SxcEckU9cc59jZZACcrRb6Nglm7E3VaBDse8PXsFgsVCnvSZXynvRqbCZm7HaDE+eS2RMRnz2qau/peJIzbKwOS2E1vahnacjrLl/RLOMo/PEUZ1ZP53i716nWsC1Bvu65SpLdqPPJGTw+ayerDp8F4PYWlZnUrwGerlf5KUpLgAX3waHfzNctx0CPN7XqkYgUquFtQvhmQxhL9kcTnZBGgI9q2BVbafGw/FVzu/Oz4FUh9+dancwRukcWm1P4lJQSEckXSkqJSPFmyzRXztv+NRxdDphT5PCsAE2Hm7Wi/GoWaAgXkjP4bmMYX284SWxSBgDebs7c2aYqozqEEuRbcNMDAaxWCzX8y1DDv0z2FLgsm52jZ5PM+lQR8eyO8GXYmUncblvK084/EpS8H/+lA5m2qCffut1BrSpBNKrke7FOVVn8vd3yNcZtYed56PsdnIlPw93FyqR+DRnSssrVTzh7GH68E84dASdX6PUuNL8rX2MSEcmNOoHetAotx5aTF5i15RSP3FLL0SHJ9Vo9BVJioUItswZjXoW0/ysp1e7B/I9PRKQUUlJKRIqn8yfM1fN2fAfJMX/tr97FHBVVp1eBj6g5GZvMV2tPMGfbKdIyzSmCwb7ujOlYjaGtquDt7lKg178WZycrdQN9qBvok538yciyczi6I0uPjqDm9tdoEv8n451/o1fWRl48PIoPDrbIPj/I1z07QXUpWVXWM+/fp2EYfLnmBG8uOkiW3aC6vxefDG9O3UCfq5904FdzhFRGIvhUgiHfQuUWVz9eRKSADW8TwpaTF/hhczgPdKmBs5P130+SouXcMdj4qbnd/XVwuo7f6NCO5nPYOnN6uVX/HYiI3CglpUSkeEmMhoX3w7Hlf+3zqgjNhkPzkVC+4Jfs3hZ2gamrj7N4fxTGxYFZDYJ9GN+pOrc1CsKliP6x4upspWElXxpWagmdF8CRpdh/nUCl+HC+cn2H3d6deN0YzaZzbpyJT+NMfBqL90Vnn1+1vCeNKvvS5OKKfw0r+Vwz8RafksmTc3exdL/ZRp8mwUwe2Igyblf56bHbYOVkWP22+TqkIwyeAWX88+srEBG5Lj0aBlLuFxfOxKex4tBZbq0f4OiQJK+Wvgj2TKhxC9S69fraCGoCLp6QegHOHizQVXtFREoLJaVEpHjZ/vXFhJQFatx8cVRUz+u745kHNrvB0v3RTF1znG1hF7L3d63jz7ibqtOuRoVCqcuUr2rdivXBTbDqTVj/PxonruZH1x2k936OnQG3s+dMErsi4tkTEcfJcymEnzcfv+0263VZLFDdz+uvFf8q+1I/yBcPVyd2R8TxwMztRFxIxdXJyot96jO8TdWrf0epF8zV9Y4uNV+3uR/+82qB/7uKiOSGu4sTQ1pW4fPVx5m5KUxJqeLm+Co4+CtYnMxRUtf7e+3kAlVaw/GV5mgpJaVERG6YklIiUrycWG0+93wL2owv8MulZtiYuz2Cr9Yc5+S5FABcnaz0bxbMPTdVp3aAd4HHUKBcPeHWV6DRYPj1MYjYgtvSibQJnk2bPh/ATWYh1/iUTPZExrM7Mo7dp+LZExlPZFwqx84mc+xsMgt2RALgZLVQq2IZjp9NJsNmp2p5Tz4Z3pyGla5R5D16H/w4HC6cAGd36PMhNBlaCB9eRCT3hrWuyuerj7Pq8FlOnU+hSnlPR4ckuWG3waKJ5narsVCx7o21F9LhYlJq/fXVpRIRkRyUlBKR4iMzFU5tMrdr3FyglzqbmM63G07y7cYwLqRkAuDr4cKItlW5u10oFUva6kuBDWHMEtg2HZa9Aqd3wBddzBFLXZ/D17MMHWv55VgOPTYp/WIR9Xh2R8SxKyKe2KR0DkYlAtC9QQBv3d4EX49rjHbaOx9+ehAyU8C3KtzxnTk9QkSkiAn18+KmWn6sORLL95vDeabHDSY3pHBs/xpi9oF7Wegy8cbbC+lgPoetA8O4/lFXIiICKCklIsXJqU1gywDvYKhQo0AucTQmia/WHmfe9kgysszi5VXKezC2QzUGt6yC19XqIZUEVqt5F7luL/Ou8r75sPFj2P8T3PY21L0tx+F+ZdzoWrciXetWBMyi5tEJ6eyKiMPDxYmbavldfbqeLQuWvwLrPzRfV+8Cg6blbXluEZFCNrxNCGuOxDJ7yyke71YbV+eiWUNQLkqLhz//z9zuMhE8y994m5VamKvCJkXD+eMF1h8RESktSvBfVyJS4pxYYz5X65SvdyYNw2DzifNMXXOcZQf+WsmvSZWy3NupOt0bBOJkLUV3Qr0DYfB0aDocfpsAcWHw4zCo29ucNulb6YqnWSwWAn3dCfQNvHb7yedg7mg4scp83eFRuPlFcNJPkogUbd3qVSTAx43ohHQW7Yuib5NgR4ck17LqLUg5B361zZsu+cHFHSq1hPD15mgpJaVERG6Ibu+ISPFxqZ5UtU750lyWzc4vu07T/+N1DP1iI8sOxGCxwK31A5hzXzsWPtCe2xoFla6E1N/V6gYPbISOj4PV2SwS+3Fr2PiZWaPjepzZZU4LPLEKXLzg9ulw6yQlpESkWHB2snJHq6oAzNwY5uBo5JrOHYNNn5vb3V/P34UzQtqbz2Hr869NEZFSSn8FiEjxkJ4IkdvM7Wo33VBTyelZzNpyiq/WniAyLhUAN2crg1pUZmzHatTwL3Oj0ZYcrp7Q7WWzEPovj0HEZlj0DOz6Afq8D8HNct/Wrh/hl0chKw3KVYM7vtfKRSJS7NzRugofrTjKphPnORKdSK3ivuBFSbXkv2DPhJq3Qq1b87ft0A6wZgqcXJe/7YqIlEJKSolI8RC2AQybmcwoW/W6mohOSGPG+pPM3BhGQloWAOW9XLmrbQh3tQvBr4xbfkZcsgQ0gDGLYfsMWPoynNkJU2+GNvdB1+fA7Rp/lNkyzT8ONn1mvq71Hxj4BXiUK4TARUTyV5CvB7fUrciS/dHM3BTOy30bODok+adjK+DQ72BxMkdJ5bfKrc2248MhLvy6+yUiIqKklIgUF5fqD13HKKlDUYlMXXOcn3ZGkmkzzGb8vLjnpmoMal4Zdxen/Iy05LJaoeUYqNMLFj8He+fCxk/+Vgi91+XnJMXAnFFm3Q2ATk+bxWatmj0uIsXX8LYhLNkfzbztETzToy4ervodKTJsWeZvFEDrceBfO/+v4VYGgpuaI7jDNigpJSJyA5SUEpHiIbueVOdcHW4YBuuOnmPqmuOsOnw2e3+r0HKMu6k63eoFYC2ttaJulHcA3P4VNB0Gv14qhH7nxULob4JvZfO4iG0wawQkngZXbxjwGdTr7djYRUTywU01/aha3pPw8yn8sus0Q1pVcXRIcsn2GRCz3xyN2/mZgrtOSPuLSal10GRowV1HRKSEU1JKRIq+lPMQtcfcDr32SKlMm51fd5/mi9UnOHAmAQCrBXo0DOSem6rTvKqmjOWbmhcLoa9+G9Z/aBZCP74Suj5v1qL6/SmwZUCFWmb9qIK4Wy0i4gBWq4U721TljT8OMnNTmJJSRUVqHPz5mrnd5TnwLF9w1wrpCOv/99dIYBERuS5KSolI0Re2DjDAv645SucKEtIy+XFzONPXneRMfBoAHi5ODG1VhTEdqlG1gmchBlyKuHpCt5fMQui/PganNsHiiX+9X7c39P8U3H0cFqKISEEY3KIy7y45zK6IePZExNOosq+jQ5JVb0HqebO/0HJMwV6rahvAAueOQmL0VfsnIiJybUpKiUjRlz11r9Nlb52OS2X6uhP8sPkUSelm8XK/Mm6M7hDK8DZVKevpWpiRll4B9WH0ItjxDSx9EdIS4ObnoeMTqh8lIiVShTJu9GwUyE87TzNzUxhvVG7s6JBKt9ijsPlzc7v7a+BUwH/meJSDgIYQvQfC10ODAQV7PRGREkpJKREp+q6QlNobGc+Xa47z6+4zZNnN4uU1K5Zh/E3V6dcsGDdnFZ0tdFYrtBgF9ftD6gUoX83REYmIFKjhbUL4aedpftp5mud61cPH3cXRIZVeS54HexbU6m5OLy8MIe3NpFSYklIiItdLSSkRKdoSo+HsQcACIR04E5/KU3N2s/ZobPYh7apXYHyn6nSu7a/i5UWBR1nzISJSwrUKLUftgDIcjk5iwfZI7m4f6uiQSqejy+HwIrA6m6OkCktoB3N01knVlRIRuV6aUyEiRdvJNeZzYCMSrN6MmraFtUdjcbJa6Nc0mF8f7sgP49vStW5FJaRERKRQWSwWhrcJAWDmpjAMw3BwRKWQLQsWP2dutx4PfrUK79pV25vPMfvMRVlERCTPlJQSkaLt4tQ9W2gnHvhuO4eiE6no7cayCZ354I5mNKykwrIiIuI4A5pXwsPFicPRSWw5ecHR4ZQ+26abI6o9ykPnpwv32mX8we/iyrLhGwv32iIiJYSSUiJStF1MSn19pgprj8bi6erEtFGtqObn5eDAREREwMfdhX5NgwFztJQUotQLsOLidL2uz5nFxwtbyMXRUmGawicicj2UlBKRoisuHC6cwI4T7xzyw2qBj+7U6CgRESlaLk3h+2NPFOeS0h0cTSmy8k0zMeVfD1qMdkwMIR3NZyWlRESui5JSIlJ0nTDrSe2wVycZD17p24Cb6wY4OCgREZGcGlX2pXFlXzJsduZsi3B0OKXD2cOwZaq53eN1cHLQ+k0h7cznM7sgPdExMYiIFGNKSolIkXV2z1IA1tsbML5Tde5qF+rYgERERK5ixMXRUt9vCsduV8HzArfkebBnQe2eUONmx8XhWxnKhoBhh1ObHBeHiEgxpaSUiBRJR6MTsR9fBYCtakee7VHXwRGJiIhcXe8mQXi7OxN+PoU1R2MdHU7JdmQZHFkCVhf4z/85OhoI6WA+h613bBwiIsWQklIiUuTEJqXzwvSfCOA8mThz34hhWK0WR4clIlKkfPzxx4SGhuLu7k6bNm3YvHnzVY+dMWMGFoslx8Pd3f2qx993331YLBbef//9Aoi8ZPJ0dWZQ88oAzNyogucFxpYJi58zt9vcC341HRsPQOjFpNRJ1ZUSEckrJaVEpEhJzbBxz9dbqZa43dxRpQ3unmUcG5SISBEza9YsJkyYwEsvvcT27dtp0qQJ3bt3JyYm5qrn+Pj4cObMmexHWNiVEycLFixg48aNBAcHF1T4JdbwNlUBWHYgmjPxqQ6OpoTaOh1iD4FnBej0lKOjMV1agS9yG2Tq311EJC+UlBKRIsNmN3hs1g52noqjs8t+AFxqdnFsUCIiRdC7777LuHHjGD16NPXr1+ezzz7D09OTadOmXfUci8VCYGBg9iMg4PKFIyIjI3n44YeZOXMmLi4uBfkRSqRaAd60rlYeuwE/bj7l6HBKnpTzsPJ1c7vr8+BR1qHhZCtXDbyDwJ4JEVsdHY2ISLGipJSIFBmTfz/A4n3RuDnBze6HzZ3VOjk2KBGRIiYjI4Nt27bRrVu37H1Wq5Vu3bqxYcOGq56XlJRESEgIVapUoV+/fuzbty/H+3a7nbvuuounnnqKBg0aFFj8Jd2ItmbB8x+3hJNlszs4mhJm5RuQegEqNoDmdzs6mr9YLH+NllJdKRGRPFFSSkSKhK/Xn+TLtScA+PQ/nriknQMXLwhu7uDIRESKltjYWGw222UjnQICAoiKirriOXXq1GHatGn89NNPfPfdd9jtdtq3b09ERET2MW+++SbOzs488sgjuY4lPT2dhISEHI/SrnuDACp4uRKdkM6yA1efTil5dPYQbPnS3O7xOjg5Ozaef8oudr7WsXGIiBQzSkqJiMMt2x/NK7+Yd+yf6l6Hm90Omm9UbQvOrg6MTESkZGjXrh0jR46kadOmdO7cmfnz5+Pv78/nn38OwLZt2/jggw+yC6Ln1uTJk/H19c1+VKlSpaA+QrHh5uzEkFbm9zBzkwqe55vFz4Fhgzq9oHoXR0dzuUtJqVNbICvDsbGIiBQjSkqJiEPtiYjn4R92YDfgjlZVeKBLDTixxnxTU/dERC7j5+eHk5MT0dHROfZHR0cTGBiYqzZcXFxo1qwZR48eBWDNmjXExMRQtWpVnJ2dcXZ2JiwsjCeeeILQ0NCrtjNx4kTi4+OzH6dOqY4SwLBWVbFYYM2RWE7GJjs6nOLvyFI4ugysLvCfVx0dzZX51zGLr2elwpmdjo5GRKTYUFJKRBwm4kIKY77eQmqmjZtq+fFq/4ZYDDucvDj0XUkpEZHLuLq60qJFC5YvX569z263s3z5ctq1a5erNmw2G3v27CEoKAiAu+66i927d7Nz587sR3BwME899RSLFy++ajtubm74+PjkeAhUreBJp1r+APywOdzB0RRztkxzlBRA2/ugQg3HxnM1FgtUvfi/v7B1jo1FRKQYUVJKRBwiPjWTMTO2cDYxnbqB3nwyvDkuTlY4swvS48HNF4KaODpMEZEiacKECUydOpWvv/6aAwcOcP/995OcnMzo0aMBGDlyJBMnTsw+ftKkSSxZsoTjx4+zfft2RowYQVhYGPfccw8AFSpUoGHDhjkeLi4uBAYGUqdOHYd8xuLuUsHz2VtPkZ5lc3A0xdiWryD2MHj6QaenHB3NtYV2NJ9PKiklIpJbRaxCoIiUBhlZdu7/bhuHo5MI8HFj2qhWeLtfXHr8xGrzObQjWJ0cF6SISBE2dOhQzp49y4svvkhUVBRNmzZl0aJF2cXPw8PDsVr/uvd44cIFxo0bR1RUFOXKlaNFixasX7+e+vXrO+ojlHhd6/gT5OvOmfg0/tgTRf9mlRwdUvGTch5WTja3b/4vuPs6Np5/c2kFvvCNYLepHyMikgsWwzAMRwdR1CQkJODr60t8fLyGoYvkM8MweGrubuZui8DL1YnZ97WjQfDfOpnfDoRjy6HHm+YwfRERB1KfIG/0feX04fIjvLv0MK1CyzHnvvaODqf4+f0p2PwFBDSEe1cX/SSP3QZvhkJ6AoxfBcFNHR2RiIjD5LZPoOl7IlKo/vfnUeZui8DJauGj4c1zJqSyMiB8g7ld7SbHBCgiIpJPhraqgpPVwpaTFzgYleDocIqXmAPm1D2AHpOLfkIKzBirtjW3w9Y7NhYRkWJCSSkRKTQLdkTw7tLDAEzq14CudSrmPCByG2SmmHUj/Os5IEIREZH8E+Djzq31zCmV329SwfNcMwyzuLlhg7q9i9fCJyEdzGcVOxcRyRUlpUSkUGw4do6n5+4G4N7O1RneJuTyg06uMZ+r3QRW/d+TiIgUf5cKns/fHklyepaDoykmjiyBY3+Ckyv851VHR5M32Ump9WC3OzYWEZFiQH/1iUiBOxqTyL3fbiXTZtCrURDPdK975QMvFTkvTndERURErqF9jQqEVvAkKT2Ln3eddnQ4RV9WhjlKCqDt/VC+umPjyaugJuDiCannIfaQo6MRESnylJQSkQJ1NjGdUdO3kJCWRYuQcrwzpAlWq+XyAzNT4dQmc7ta58INUkREpIBYrZbs0cHfbQxDawz9iy1fwrmj4OUPNz3p6GjyztkVKrcytzWFT0TkXykpJSIFJjXDxj1fbyHiQiqhFTyZOrIl7i5XKVR6ahPYMsCnUvG7KyoiInINt7eojKuzlX2nE9gVEe/ocIqu5HOw6g1z++YXwL2YruAY2tF8PqmklIjIv1FSSkQKhM1u8OiPO9gVEU85Txemj25NeS/Xq59waepe6E1gucJIKhERkWKqnJcrvRsFATBzY5iDoynCVr4OafEQ0AiajXB0NNcvpL35HLbeLNouIiJXpaSUiBSI1347wJL90bg6W5k6siXV/LyufYLqSYmISAk2vG1VAH7ZfZr4lEwHR1MERe+HrdPM7Z5vgPUqI6uLg0otzCLtSVFw/rijoxERKdKUlBKRfDd93QmmrTsBwDuDm9AytPy1T0hPhMjt5na1mwo4OhERkcLXvGo56gZ6k5ZpZ972CEeHU7QYBiyeCIYd6vX9a/pbceXiYSamwBwtJSIiV6WklIjkq6X7o5n0634AnulRlz5Ngv/9pLANYNigXDUoW7WAIxQRESl8FouF4W3NguczN6ngeQ6HF8HxleboolsnOTqa/BHSwXxWsXMRkWtSUkpE8s3uiDge+WEHhgHDWlflvs65LFh+YpX5rKl7IiJSgvVvGoynqxPHziaz8fh5R4dTNGRlwOLnze12D0L5ao6NJ79k15VSUkpE5FqUlBKRfBFxIYUxM7aSmmmjc21/Xu3XAEtuC5arnpSIiJQC3u4u9G9WCTBHSwmw+Qs4fwy8KsJNTzg6mvxTpTVYnCAuHOJOOToaEZEiS0kpEblh8amZjJ6+hdikdOoGevPRnc1wdsrl/72knIeoPeZ2qOpJiYhIyXZna3Oa+uJ9UZxNTHdwNA6WHAur3jK3b3kR3LwdG09+cvOGoCbmdvgGx8YiIlKEKSklIjckI8vO/d9t40hMEgE+bkwf3Qpvd5fcN3ByLWCAf13wDiiwOEVERIqChpV8aVqlLJk2g9lbS/kImhWvQXo8BDaGpnc6Opr8F3qxrtTJtY6NQ0SkCFNSSkSum2EYTJy/h/XHzuHl6sS0Ua0I8vXIWyMn15jPmronIiKlxIiLBc9/2ByOzV5KC55H7YVtM8ztnm+C1cmh4RSI7GLnWoFPRORqlJQSkev24fKjzNsegZPVwkfDm9Mg2DfvjaielIiIlDK9Gwfh6+FCxIVUft9zxtHhFD7DgMXPgWGH+v3/Kgpe0lRtC1jg3BFIinF0NCIiRZKSUiJyXeZti+C9ZYcBeLVfQ7rWqZj3RhKj4exBwPLX3UQREZESzt3FiVHtQwF46ed9xCSmOTagwnbod3PlXSc3uHWSo6MpOB7lIKCBua3RUiIiV6SklIjk2fpjsTw7fzcA93WuwZ1tql5fQ5em7gU1Bs/y+RSdiIhI0fdg15rUC/LhfHIGz83fg2GUkml8Wemw+Hlzu/1DUC7EsfEUtOwpfOscG4eISBGlpJSI5MmR6ETu/XYbmTaDXo2DeLp7netv7MQq81mr7omISCnj6mzlvaFNcHWysuxADHO2Rjg6pMKx6XO4cALKBEDHxx0dTcG7NDVRI6VERK5ISSkRybWYxDRGTd9CYloWLULK8c7gJlitlutvMLueVOf8CVBERKQYqRvowxP/qQ3AK7/s49T5FAdHVMCSzsLqt83tW14CN2/HxlMYLiWlovdBynnHxiIiUgQpKSUiuZKSkcU9X28lMi6V0AqeTB3ZEneXG1gpJy4cLpwEixOEtMu3OEVERIqTe26qTqvQciRn2Hhizi7sJXk1vhX/B+kJENQUmgxzdDSFo0xFqFALMODUJkdHIyJS5Dg8KfXxxx8TGhqKu7s7bdq0YfPmzVc9tkuXLlgslssevXr1ynHcgQMH6Nu3L76+vnh5edGqVSvCw8ML+qOIlFg2u8GjP+5kd0Q85TxdmD66NeW9XG+s0RMX60lValE67pSKiIhcgZPVwjuDm+Lp6sTmE+eZtu6Eo0MqGFF7YPs35naPN8Dq8D9DCk/oxbpSJ9c6Ng4RkSLIob8Gs2bNYsKECbz00kts376dJk2a0L17d2Jirrxk6vz58zlz5kz2Y+/evTg5OTF48ODsY44dO0bHjh2pW7cuK1euZPfu3bzwwgu4u7sX1scSKXH+77f9LN0fjauzlakjW1LNz+vGG82eutfpxtsSEREpxqpW8OSF3vUBeGvxIQ5HJzo4onxmGLBoIhh2aDCg9I2Qzi52rrpSIiL/5NCk1Lvvvsu4ceMYPXo09evX57PPPsPT05Np06Zd8fjy5csTGBiY/Vi6dCmenp45klLPP/88t912G2+99RbNmjWjRo0a9O3bl4oVr2O5ehFh2toTTF93EoB3hzShZWg+rJJnGEpKiYiI/M0drarQtY4/GVl2Hp+1k4wsu6NDyj8HfzVX3HV2h1snOTqawneprtSZXZBewhKOIiI3yGFJqYyMDLZt20a3bt3+CsZqpVu3bmzYsCFXbXz11VfccccdeHmZozbsdju//fYbtWvXpnv37lSsWJE2bdqwcOHCa7aTnp5OQkJCjoeIwJJ9Ubz6234Anu1Zl96Ng/On4XPHIPE0OLlCldb506aIiEgxZrFYeHNQY8p6urDvdAIf/XnE0SHlj6x0WPJfc7v9w1C2qmPjcQTfyubnNmxw6uqlSkRESiOHJaViY2Ox2WwEBATk2B8QEEBUVNS/nr9582b27t3LPffck70vJiaGpKQk3njjDXr06MGSJUsYMGAAAwcOZNWqVVdta/Lkyfj6+mY/qlSpcv0fTKSE2HUqjkd+3IFhwJ1tqnJvp+r51/iJi/97rNIGXDzyr10REZFirKKPO6/1bwTAxyuPsSP8goMjygcbPzUXNikTCB0ec3Q0jhPS0XwOW+fYOEREiphiW2Hwq6++olGjRrRu/dcoC7vdHObcr18/Hn/8cZo2bcqzzz5L7969+eyzz67a1sSJE4mPj89+nDp1qsDjFynKTp1PYezXW0jLtNO5tj+T+jbAYrHk3wVOXixyrql7IiIiOfRqHES/psHY7AZPzN5FaobN0SFdv6QYWD3F3O72MriVcWg4DnVpCp/qSomI5OCwpJSfnx9OTk5ER0fn2B8dHU1gYOA1z01OTubHH39k7Nixl7Xp7OxM/fr1c+yvV6/eNVffc3Nzw8fHJ8dDpLSKT8lk9IwtxCZlUC/Ih4+HN8fZKR//r8Ju/2vlPSWlRERELjOpb0MCfNw4HpvMG38ccHQ41+/PVyEjEYKbQ+Ohjo7GsS4lpSK3QWaqY2MRESlCHJaUcnV1pUWLFixfvjx7n91uZ/ny5bRrd+0VOebMmUN6ejojRoy4rM1WrVpx6NChHPsPHz5MSEhI/gUvUkJlZNm597utHI1JItDHnemjWlHGzTl/L3L2AKTEgouX2UkVERGRHHw9XXj79iYAfL0hjDVHzjo4outwZjds/9bc7vEGWIvtBI38Ub66OYXRlmEmpkREBHDw9L0JEyYwdepUvv76aw4cOMD9999PcnIyo0ePBmDkyJFMnDjxsvO++uor+vfvT4UKFS5776mnnmLWrFlMnTqVo0eP8tFHH/HLL7/wwAMPFPjnESnODMPg2Xm72Xj8PGXcnJk+uhWBvu75f6FLq+6FtANn1/xvX0RESqbMVLBlOjqKQtOptj93tTVvqj41ZzfxKcXosxsGLJoIGNBwEFRt4+iIHM9igdAO5vZJ1ZUSEbnEoUmpoUOHMmXKFF588UWaNm3Kzp07WbRoUXbx8/DwcM6cOZPjnEOHDrF27drLpu5dMmDAAD777DPeeustGjVqxJdffsm8efPo2LFjgX+eUilqL0y/Df78P0dHIjfo/WVHmL8jEierhY+HN6deUAFNY72UlAq9qWDaFxGRksdug3n3wMzBkFZ6VkmeeFtdqvl5EZWQxsu/7HN0OLl34GcIWwvO7tDtFUdHU3Rk15VSUkpE5BKLYRiGo4MoahISEvD19SU+Pl71pa7lwC8w/17ITDZfj/wJqndxaEhyfeZui+DJObsAmDywEcNaF9ByzXYbvFkN0uNh3AqopOl7IlK0qU+QNwX2fUXvhy9vgcwUqNgAhs8B30r5134Rtj38Ard/uh67AZ8Mb85tjYIcHdK1ZabBx60hLgw6PQ03P+/oiIqOmAPwSVtw9oBnwzViXERKtNz2CUr55G65LoYBq96GWSPMhJRHeXP/b09CVrpjY5M8W3c0lmfn7QbggS41Ci4hBXBml5mQcvOFoCYFdx0RESlZAurD6N+hTADE7DMTVGd2OzqqQtG8ajke6FITgOcX7CEmMc3BEf2LjZ+YCSnvYOj4mKOjKVr86pj95qxUs08kIiJKSkkeZaTA3NGw4uJ0vdb3wsPbwKsinDsC6//n2PgkTw5HJ3Lfd9vIshv0aRLMk/+pU7AXzJ661xGsTgV7LRERKVmCm8E9y8C/LiSegek94cgyR0dVKB65pRb1g3y4kJLJxHl7KLITHRKjYc075na3l8HVy6HhFDlW69+m8K11bCwiIkWEklKSe/ERML0H7FsAVhfo8wHc9hZ4lofur5nHrJ4CF8IcG6fkSkxiGqOnbyExLYtWoeV4+/bGWK2Wgr3opaRUtU4Fex0RESmZylaFMYvNuoQZSfD9ENg2w9FRFThXZyvvDW2Kq5OV5QdjmL31lKNDurI/J5n/LpVaQKPBjo6maAq5WOw8bL1j4xARKSKUlJLcObUZvuhqDjX2rAB3/wwtRv31fqPBZgcxKxUWPeuwMPPD+8sOc9dXm3h67i4+WHaEedsi2Hj8HKfOp5Blszs6vHyRkpHF2BlbiYxLpZqfF1/c1RJ3lwIeuZSVAeEbzG0lpURE5Hp5lIUR86HxHWDY4JdHYdkrYC8Zv9FXUyfQmye71wZg0i/7OXU+xcER/cOxFbBjprnd401zVJBc7tJIqfCNZq1NEZFSztnRAUgxsPN7s8Nny4CAhnDH91AuJOcxFgvcNgU+6wCHfodDf0Cdno6J9wb8vOs07y87ctX3rRYI9HGncjlPKpXzoFJZj8ueCzy5c4NsdoNHftjBnsh4ynu5Mn1UK8p5FUKhzchtZoFaTz+oWK/gryciIiWXsysM+Mzsj6x6E9a+C3Hh0P8TcHZzdHQFZmzH6izbH8Pmk+d5YvYufhjfFqeCHuX8bwzDLN+w7GXAgMZDoUorx8ZUlAU2AjcfSE+A6L2qsSkipZ6SUnJ1dhssfRE2fGS+rtsbBnwObmWufHzFutDuQVj3AfzxNFTrDK6ehRfvDYqKT+O/C/YAMLhFZaqW9yTiQiqRcX89MrLsnI5P43R8Gpy8cjt+ZdyoVM6DyhcTVZX/kbTydncpvA/1D4ZhMOmXfSw7EIOrs5WpI1sS6ldI9R6yp+7dZCYxRUREboTFAl2fM6f0/fIo7J1r1poa+p1ZWqAEcrJamDK4CT0/WM3mk+eZtvYE4zpVd1xAafHw04Pmisxgjl7r/Z7j4ikOrE5QtS0cWQIn1ykpJSKlnpJScmWpcTBvLBy9WEC009PQZeK/D8Xu9DTsmWferVz7Ltz83wIPNT8YhsFTc3eRkJZF48q+vD6wES5OOT+r3W4Qm5RORFwqkZeSVRdSibiQkr2dnGEjNimd2KR0dp2Ku+K1fNydc4y0qpz9bO4r5+mCpYCSNtPWneTrDWbNr/eHNqVFSLkCuc4VnVxjPmvqnoiI5KdmI8CnEsweCWHr4Kv/wPA5UL6aoyMrEFUrePJC7/o8O38Pby8+RKfa/tQJ9C78QKL3mysxnz8GTq7Q4w1oOUY3nnIjpL2ZlApbB+0ecHQ0IiIOpaSUXC72KPxwh7manrOHORS+4cDcnetWBnpMhtl3mSOmGt8BfjULNt588O3GMNYcicXdxSwk+s+EFIDVaqGijzsVfdxpXvXyZI5hGMSnZhJxIfWvEVYXUomMS8l+HZeSSUJaFvvPJLD/TMIVY/FwccoxJbByjuSVJxW93a6rIPmivVH832/7AXjutrrc1igoz21ct8xUOLXJ3K7WufCuKyIipUONrjBmEcwcYvZfvroVhs2Cyi0cHVmBGNqqCkv2R/PnwRgen7WThQ92wNW5EGs47Z5tjk7LTAHfKjD46xL7XReIvxc7Nwwl8kSkVFNSSnI6uhzmjjaHY/tUMutHBTfNWxv1+kDNW+HoUvj9CbhrYZH+sT12NonXfz8AwMSe9ajhf5Xpif/CYrFQ1tOVsp6uNKzke8VjktOzcoyw+ueoq5jEdFIzbRyNSeJoTNIV23BxshBc9mLS6h81raqU8yTQ1/2ypNqO8As8NmsHhgHD21Rl3E2FPNT/1CazJplPJSjvwGkGIiJScgU0gHuWwfeDIWoPzOgFg76Eer0dHVm+s1gsvDGoEd3fW83+Mwl8uPwIT3avU/AXzkqHxc/Bli/N1zVuhoFfgleFgr92SRLU1Lzxm3oezh4yS2CIiJRSSkqJyTBg02dmR8OwQ+XWZk0G74C8t2WxwG1vwcdt4fhK2Lcg9yOtClmmzc6EWTtJy7RzUy0/7mob8u8n3QAvN2dqB3hTO+DKw+zTMm2ciU/LHmEVeXHU1aXkVVRCGpk2g7BzKYSdu/KqO1YLBPi450hYzd56irRMO13q+PNK3wYFNj3wqrLrSXUq0glKEREp5nyCYPQfMGe0eXNs1ghzBHfb+x0dWb6r6O3O6wMacf/M7Xyy8ig316t4xZHc+SY+AmbfDZFbzdednzEf1qK9wEuR5OwKVVrDiVUQtlZJKREp1fKclAoNDWXMmDGMGjWKqlWrFkRMUtiy0uG3J2DHt+brJndCn/dvbPWa8tXhpgmwcrKZ6Kp1K7g5oN7Bv/h4xVF2RcTj4+7M27c3ua5pcfnJ3cWJan5eVLtK8fEsm53oxHQizqf8bXpgao6C7BlZds7Ep3EmPo2tYReyz60f5MNHdzbH+QpTEwvcpaRU6E2Ff20RESld3Lxh2I/w+5OwbTosehYuhEH310pcAqVnoyAGNKvEgh2RPDF7F7890hFP1wK453zsT5g71hzZ414WBk6F2v/J/+uUJiEdLial1kOrexwdjYiIw+T5V+uxxx5jxowZTJo0ia5duzJ27FgGDBiAm1vJXX63REs6a95FPLURLFa49VVzBb38GM3S4THY9SNcOAEr3zA7g0XIrlNx/O/PowC82r8hgb7uDo7o3zk7WbOn7V2J3W4Qm5yePcLqUuIqPcvGE/+pQxk3BwyOTE+EyO3mdjUlpUREpBA4OZurwJULgWUvw6ZPIf6UmUwpRisD58bLfRuw4dg5TsQm88YfB5nUr2H+NW63w9p34M/XAMNcKW7IN1AuNP+uUVqFtDefVVdKREq5PA+ZeOyxx9i5cyebN2+mXr16PPzwwwQFBfHQQw+xffv2gohRCsqZ3TC1q5mQcvOBO2dD+4fy70fRxR1ue9vc3vgpRO/Ln3bzQWqGjcdn78RmN+jdOIh+TSs5OqR8YbVaqOjtTrOq5ejTJJj7Otfg1f4Neev2JgT4OCjpFrYBDBuUq2Yu2y0iIlIYLBbo+DgM+spcHe7gr/B1H/OGXAni6+HC24MbA/DNhjBWH86nz5d6wVz45s//AwxofjeMWaKEVH6p3NL87zLxjHkDV0SklLrueTzNmzfnww8/5PTp07z00kt8+eWXtGrViqZNmzJt2jQMw8jPOCW/7f8JpnU37xqWrwH3LDen2OW3Wreahc8NmzlFsIj8d/HmooMcP5tMgI8b/9c/H+8oyuVOrDKfq3VybBwiIlI6NbodRv4EHuXMekhfdYPYI46OKl/dVMufu9uZdTGfnrub+JTMG2vw9E74vDMcWQzO7tDvY+j7oXnDUfKHiwdUurhi4cl1jo1FRMSBrjsplZmZyezZs+nbty9PPPEELVu25Msvv2TQoEE899xzDB8+PD/jlPxit5tT6WaPNJfxrd4Vxi0H/9oFd80eb4CLF4RvgF0/FNx1cmnNkbPMWH8SgLdub0JZT1fHBlTS/b3IuYiIiCOEtIexS81RPhdOwle3mtOmSpBne9ajup8XUQlpvPTz3utvaPs38NV/IC7M/L7GLoFmI/ItTvmbv0/hExEppfKclNq+fXuOKXsNGjRg7969rF27ltGjR/PCCy+wbNkyFixYUBDxyo3ISIa5o8zi4wBt7ofhc807hwXJtzJ0ftrcXvKCORzcQeJTMnlqzm4ARrYLoXNtf4fFUiqknDeX5QYlpURExLH8asHYZVCppdkX+aYf7Jnr6KjyjYerE+8MaYLVAgt3nua33Wfy1kBmKvz0EPz8MNjSoXYPGL/SrCMlBSM7KaWRUiJSeuU5KdWqVSuOHDnCp59+SmRkJFOmTKFu3ZzLmFarVo077rgj34KUfBB3ypyut/8nsLpA3/9BzzfMQqCFoe0D4F8XUmJh+auFc80reOGnvUQlpFHdz4uJPes5LI5S4+RawAD/elCmoqOjERGR0q6MP9z9C9TtDbYMmDcW1rxbZMoL3KhmVcvxYNeaAPx34R5iEtJyd+L5E+boqB3fmgvf3PwC3PFDwd+4LO2qtAGLkzkqLT7C0dGIiDhEnpNSx48fZ9GiRQwePBgXF5crHuPl5cX06dNvODjJJ+EbzYLmUXvA08/sjDUfWbgxOLvCbVPM7a3TIHJb4V4f+GXXaX7edRonq4V3hzbFw7VkLQtdJGVP3dOqeyIiUkS4eporyLV9wHy9/BX49TGwZTk0rPzy8M21aBDsw4WUTJ6dv+ff67weXgxfdIao3eBZAUbMh05PgvW6q3xIbrl5/zUSTVP4RKSUyvOvTUxMDJs2bbps/6ZNm9i6dWu+BCX5aMd3MKM3JJ+FgEYwfgWEtHNMLNVugsZDAcMsem63Fdqlo+LT+O9Cs77Cg11r0rRK2UK7dql2co35rKl7IiJSlFidoMdk6PEmYIFtM+CHoZCe6OjIbpirs5X3hjbF1dnKnwdjmLXl1JUPtNvMlfW+HwJp8VC5Fdy7Gmp0LdyASztN4RORUi7PSakHH3yQU6cu/3GLjIzkwQcfzJegJB/YsmDRc/DTg2DPNFfAG7MIylZ1bFy3vgpuPnB6B2wrnNF0hmHw1NxdxKdm0riyLw/fXLNQrlvqJUbD2YOABUI6ODoaERGRy7W9D+6YCc4ecHQZTO8JCXmsxVQE1Q7w5unudQB49df9hJ9LyXlA8jn4bhCsftt83Xo8jPrdrAMqhetSH0kjpUSklMpzUmr//v00b978sv3NmjVj//79+RKU3KDUOPOu18aPzdedn4XB34BbGYeGBYB3gFmnAGD5JEg6W+CX/HZjGGuOxOLmbOXdIU1xcdJw9EJxaZRUUGPwLO/YWERESqCPP/6Y0NBQ3N3dadOmDZs3b77qsTNmzMBiseR4uLu7Z7+fmZnJM888Q6NGjfDy8iI4OJiRI0dy+vTpwvgojlW3F4z6Dbz8zVIHX94C0fscHdUNG9OhGm2qlSc5w8YTc3Zis1+cxhexFT7vBMdXgIsnDPwSbnvbLLUgha9qW8ACsYchKcbR0YiIFLo8/3Xu5uZGdHT0ZfvPnDmDs3MhFc2Wq4s9Ynamji037/oNngFdJxatugCtxkJgY3Oo+NIXC/RSx84m8frvBwCY2LMuNSsWgcRcaXFilfmsqXsiIvlu1qxZTJgwgZdeeont27fTpEkTunfvTkzM1f+o9fHx4cyZM9mPsLCw7PdSUlLYvn07L7zwAtu3b2f+/PkcOnSIvn37FsbHcbzKLeCeZVChFiREwrQecGyFo6O6IVarhSmDm+Dl6sSWkxf4cvUx2DzV/GwJEVChJtyzHBoPdnSopZtneQhoYG5rtJSIlEJ5zlT85z//YeLEicTHx2fvi4uL47nnnuPWW2/N1+Akj44ug6m3wLmj4FMZxi6GBgMcHdXlrE7Q+z3AAru+L7Af4CybnQmzd5GWaadjTT9GtgstkOvIVWQXOe/s2DhEREqgd999l3HjxjF69Gjq16/PZ599hqenJ9OmTbvqORaLhcDAwOxHQEBA9nu+vr4sXbqUIUOGUKdOHdq2bctHH33Etm3bCA8PL4yP5HjlQmHsEnM6VXoCzLzdrM1ZjFUp78lLfRrgQRqBfz4Kvz95saxDXxi3AgLqOzpEgb/VlVJSSkRKnzwnpaZMmcKpU6cICQmha9eudO3alWrVqhEVFcU777xTEDHKvzEM2PAxzBwM6fHm8rLjV/y1mkdRVLnlXysA/vYE2DLz/RIfrzjGrlNx+Lg78/bgxlitlny/hlxFXDhcOGkuc1y1raOjEREpUTIyMti2bRvdunXL3me1WunWrRsbNmy46nlJSUmEhIRQpUoV+vXrx759156iFh8fj8VioWzZslc9Jj09nYSEhByPYs2zPNy1ABoNBnuWWZvzz9fMvlYxNbhaGku9J9HPuhYbVrK6vWquPuju4+jQ5BIlpUSkFMtzUqpSpUrs3r2bt956i/r169OiRQs++OAD9uzZQ5UqVQoiRrmWrHT46SFY/BwYdmg2Au7+BcpUdHRk/67by+BRHmL2w6bP87Xp3RFxfPjnEQBe7d+QIF+PfG1f/sWJi/WkKrUwlzsWEZF8Exsbi81myzHSCSAgIICoqKgrnlOnTh2mTZvGTz/9xHfffYfdbqd9+/ZERERc8fi0tDSeeeYZhg0bho/P1ZMXkydPxtfXN/tRIvqCzm4w4Au46Qnz9eq3YMF9kJXh2Liux4FfsEy9mcqZJzlLWYalP897yf8Bi27UFSlVLyalovdC6gXHxiIiUsiuqwiUl5cX48ePz+9YJK+SYmDWCDi1CSxW+M9r0Pb+4tPR8CwPt74CPz8MKydDw4HgE3zDzaZm2Hh8llnQs1fjIPo2ufE2JY+yp+6pnpSISFHQrl072rVrl/26ffv21KtXj88//5xXX301x7GZmZkMGTIEwzD49NNPr9nuxIkTmTBhQvbrhISEkpGYslrhlhehbAj8+jjs/tGsNTX0O/Ao6+jo/p0tC5a/Aus/NF9Xbc+ehm+weX4EW1ce4+a6AbQIKefYGOUv3gFmPbNzRyB8I9Tp6eiIREQKzXVXJt+/fz/h4eFkZOS8a1RqCmI62pld8MOdZqFKN18YPA1qdvv384qapiNg+7cQsdkc7TV4xg03+eaigxw7m0xFbzde698QS3FJ0pUUhqGklIhIAfLz88PJyemyhWeio6MJDAzMVRsuLi40a9aMo0eP5th/KSEVFhbGn3/+ec1RUmAugOPm5pa3D1CctLgbfCvB7LvNVWWndYfhc6BsVUdHdnWJ0TB3DIStNV+3ewi6vczNTi4MPGEwf0ckT8zeye+P3oSnqxYpKjJC2ptJqbB1SkqJSKmS5+l7x48fp0mTJjRs2JBevXrRv39/+vfvz4ABAxgwoAgW1S6J9i3MuXLKuD+LZ0IKzDuRvd4xR3rtWwBHl99Qc2uOnGXG+pMAvD24CWU9tbxxoTt3DBJPg5MbVGnt6GhEREocV1dXWrRowfLlf/1m2u12li9fnmM01LXYbDb27NlDUFBQ9r5LCakjR46wbNkyKlSokO+xF0s1u8GYReAdDGcPwpfd4PQOR0d1ZWEb4PNOZkLK1RsGfw3dXwMnFwBe6tuAIF93Tp5LYfLvBx0crOQQ0sF8Vl0pESll8pyUevTRR6lWrRoxMTF4enqyb98+Vq9eTcuWLVm5cmUBhCjZ7HZYMRnm3A2ZKVDjFnMpX7+ajo7sxgQ1htb3mtu/P2XWyboO8SmZPDVnNwB3tQ2hc23//IpQ8uLEKvO5SmtwUS0vEZG/O3XqVI46Tps3b+axxx7jiy++yFM7EyZMYOrUqXz99dccOHCA+++/n+TkZEaPHg3AyJEjmThxYvbxkyZNYsmSJRw/fpzt27czYsQIwsLCuOeeewAzIXX77bezdetWZs6cic1mIyoqiqioqMtGxZdKgY3gnmUQ0BCSomH6bXBokaOj+sulRW9m9IKkKPCvay5606B/jsN8PVx4+3ZzIZxvN4ax6vBZBwQrV3Sp2PnpnZCe6NBQREQKU56TUhs2bGDSpEn4+flhtVqxWq107NiRyZMn88gjjxREjAKQkWwmo1a9Yb5u9xDcObt41DXIja4ToUwAnD8G6z68riZe/HkvUQlpVPPzYuJtdfM5QMk1Td0TEbmqO++8kxUrVgAQFRXFrbfeyubNm3n++eeZNGlSrtsZOnQoU6ZM4cUXX6Rp06bs3LmTRYsWZRc/Dw8P58yZM9nHX7hwgXHjxlGvXj1uu+02EhISWL9+PfXr1wcgMjKSn3/+mYiICJo2bUpQUFD2Y/16jdwAzGl8o/+A6l3Nm4M/DoPNUx0dlZnAmDPq4qI3Nmh4+8WblrWueHjHWn6Mah8KwNNzdxGfkv8rIMt1KFvFnBZq2ODUZkdHIyJSaPKclLLZbHh7m6tp+fn5cfr0aQBCQkI4dOhQ/kYnprhw+Ko7HPgZnFyh38cXh2KXoDoA7r7Q/XVze80UuHAyT6f/sus0P+08jZPVwrtDmqhGgqPY7XDyYg0LJaVERC6zd+9eWrc2pzbPnj2bhg0bsn79embOnMmMGTPy1NZDDz1EWFgY6enpbNq0iTZt2mS/t3Llyhztvffee9nHRkVF8dtvv9GsWbPs90NDQzEM44qPLl263MhHLlncfcyaUs1GmKse//4kLPmv+fvnCDEH4YuusH8hWF2g59sw6EtwK3PN057pUZfq/l5EJ6Tz4s97CydW+XeawicipVCek1INGzZk165dALRp04a33nqLdevWMWnSJKpXr57vAZZ6YRvMzkb0HvDyh7t/NTtCJVHDQRB6E2SlwR/P5Pq0qPg0/rvQ7FA92KUGzapqNRmHOXsAUmLBxQuCmzs6GhGRIiczMzO7MPiyZcuyF4ipW7dujpFNUoQ5uUDfj6Drf83X6/8Hc0dBZmrhxrFnLky92SyO7R0Mo3+HNuNztQqzh6sT7w5pipPVwk87T/Pr7tOFELD8q0tT+JSUEpFSJM9Jqf/+97/YL94NmjRpEidOnOCmm27i999/58MPr2/alVzF9m/g6z7mH/mBjWDcCqja5t/PK64sFrPoudUFDi+Cg7//6ymGYfD0vN3Ep2bSqJIvD99y5aHqUkguTd0LaQfOKjIvIvJPDRo04LPPPmPNmjUsXbqUHj16AHD69GkVFi9OLBbo/BQM+MLst+z/Cb7pB8nnCv7aWRnmzbt5YyEzGap1hvvW5HlxkaZVyvJgV7Mu6X8X7iU6Ia0gopW8uDRSKnJr4Sc5RUQcJM9Jqe7duzNw4EAAatasycGDB4mNjSUmJoabb7453wMslWxZ8Mez8PPDYM+E+v1hzGJzrnlJ518H2j9kbv/xDGSkXPPw7zaGsfrwWdycrbw3tAkuTnn+T1ryk+pJiYhc05tvvsnnn39Oly5dGDZsGE2amEWnf/755+xpfVKMNBkKdy0wyxCc2gRfdTNXoS0o8ZFmMfNNn5mvb3rCvL6X33U19/DNNWlYyYe4lEyembcbwzDyMVjJs/LVoUwg2DIgcpujoxERKRR5+gs+MzMTZ2dn9u7NOfe8fPnyWHIxVFhyIfUCzLwdNn1qvu7yHAyeAa5eDg2rUHV6CnyrQHy4WV/qKo6fTeK13w8A8GzPutSs6F1YEcqV2LJUT0pE5F906dKF2NhYYmNjmTZtWvb+8ePH89lnnzkwMrlu1W6CsUvNItXnj8OX3SB8U/5f5/gq+LwTRGwGN18Y9iPc8iJYna67SRcnK+8NaYqrs5WVh87yw+ZT+Riw5JnFoil8IlLq5Ckp5eLiQtWqVbHZbAUVT+l29jBMvQWOrwAXTxjyDXR5Jle1AUoUVy/ocXGVwXUfmt/LP2TZ7Dw+exdpmXY61KzA3e1CCzdGuVzULkhPMO8WBzZ2dDQiIkVSamoq6enplCtn1j8MCwvj/fff59ChQ1SsWNHB0cl1868DY5dBcDNIPW+WX9i3MH/attthzbvwbX+zpENAI7h3JdTpmS/N1wrw5unudQD4v9/2E3YuOV/aleuUnZRa59g4REQKSZ7nOj3//PM899xznD9/viDiKb2OLDPvrJ0/Zo4SGrsE6vdzdFSOU7cX1OpuTl/8/Un4x3DyT1YeY9epOLzdnXn79iZYraUscVcUnVhjPod0vKG7tiIiJVm/fv345ptvAIiLi6NNmza888479O/fn08//dTB0ckN8Q6AUb9B7Z5gS4c5d5s3125kSlxqHMwaDstfMVf7azoc7llqTvPKR2M6VKNNtfKkZPx/e/cdHVW1vnH8O5OekAIJSUgIoXdIqKEjitKkSlWKvVeujd+1XCvK9SoqKoooFhSkiqBYQOkdQm9SEiCEECCd1JnfHwcCkRYgyZkkz2etWTM5c2bmmUxcbN/Z+915/OuHzeTZtIzPNGf7Sh1aC3k55mYRESkBV12UmjBhAkuXLiUkJIR69erRvHnzAhe5Sna7sWvLd4MgKxmqtTUamgc3MTuZuSwW6PE2OLvDgSWwbVb+XVsOJ/H+or0AvNa3MSF+HmallPOpn5SIyBVt3LiRjh07AjBz5kyCgoKIiYnh66+/1oYxZYGrFwydCq3vN37+/UXjy7W83Kt/rvit8NkNsPtncHKF3u9D34/ApejHPVarhXcGRVDBzZn1MaeYtGx/kb+GFFLl+uBRCXIyIC7a7DQiIsXO+Wof0K9fv2KIUU7lZML8p2Dzd8bPzUZAr3e1a9lZlWoYDTz/fAN+/TfUuYVMJy+emh5Nns1OryZV6BsZYnZKAWMnoNhVxm0VpURELikjIwNvb6MH4m+//caAAQOwWq20adOGmJgYk9NJkbA6QY9xULG6MX5Z9zkkH4aBXxS+R2j0d8YYMTcTfKvB4K8gtHi//A2r5MlLvRvy7MwtvPvbHjrXrUyDKj7F+ppyEVarsYRv13xjCV9YK7MTiYgUq6suSr388svFkaP8ST1mTMc+vA4sVug2FqIeKH/9o66k3eOw+XujcehfY3krezj7jqcT6O3G6/0aq8G+oziywfhGzzMAAhuYnUZExGHVrl2buXPn0r9/f3799VeeeuopABISEvDxUQGgzLBYoO0j4FsVZt8PexbClz3h9h+MZX6XkpMJC5+DDVOMn2vfDAM+A89KJRJ7UIuq/Lb9GH/sPMZT06P58dH2uDlrSX6Jyy9KrYQOT5qdRkSkWF318j0pAnHRMKmLUZBy94Xhs6DNgypIXYyLO/Q0duCzr/mUNauWAPD2wKZU9NKMModx/tI9/R2LiFzSSy+9xNNPP0316tVp3bo1bdu2BYxZU82aNTM5nRS5hn1h1E/g6Q9Ho43+oQm7Ln5uUix82f1MQcpi7MB8+w8lVpACsFgsjB3QBH8vV3bFp/L+H3tL7LXlPGebnceuAps2mBKRsu2qi1JWqxUnJ6dLXuQKts2CL7pDyhHwrwP3LoZaN5qdyrHVvomcen2w2PN4zeVLRkRVpUs97VDkUPKLUh3NzSEi4uAGDhxIbGws69ev59dff80/ftNNN/Hee++ZmEyKTVhruOd3qFQLkmNh8i3n/t08a+8f8GkniNsEHhVh+ExjB2ZryX9/XNnbjTf6G71NJy7Zx4YYbW5U4oKbgqu3savxsW1mpxERKVZXvXxvzpw5BX7Oyclh06ZNfPXVV7zyyitFFqzMit8Guaehdlejt4C7r9mJSoXXcofznP1XWlr30KRqNBBhdiQ5K+c0HF5r3K7R2dwsIiKlQHBwMMHBwRw+fBiAqlWr0rp1a5NTSbHyr2UUpqbdDodWwzcDoO8EaDIYlo6Dv94C7BDS3Ogf5VfN1LjdGwdzW/OqzNp4mNE/bObnxzvi5XbV/9sg18rqBNXawN+/G0v4qmjcKyJl11V//dK3b98Cl4EDB/LGG28wbtw45s2bVxwZy5YbXzR2Trn9BxWkCmn+lji+3p7L+3m3AeD25yuQoW/tHMahNZCXDT6hRb5FtYhIWWOz2Xj11Vfx9fUlPDyc8PBw/Pz8eO2117DZbGbHk+Lk5Q8jf4SG/cCWA3MegIkd4K+xgB1a3g13LzS9IHXWy30aEuLrTsyJDN78eafZccqfs0v4YlaYm0NEpJgV2ZzgNm3asGjRoqJ6urLLaoVmw41vQOSKjqVk8u85xrRljw6PQuUGkHECFr1qcjLJp35SIiKF9u9//5sJEybw1ltvsWnTJjZt2sSbb77Jhx9+yIsvvmh2PCluLu4w8Eto/4Txc8J2cPaAfhPh1vfA2c3cfOfxcXfhnUHGDJ2pa2L5c3eCyYnKmfD2xnXMSrDbzc0iIlKMiqQodfr0aT744ANCQ0OL4ulEALDb7TwzcwvJp3NoHOrDozc3gF7/M+7cMAUObzA1n5xxflFKREQu66uvvuLzzz/noYceomnTpjRt2pSHH36YSZMmMWXKFLPjSUmwWuHmV6Hvx1D/Vrj3D4gcZnaqi2pXO4C72lcH4LmZW0jKyDY3UHkS0swoWGacgOO7zU4jIlJsrrooVbFiRSpVqpR/qVixIt7e3nzxxRf897//LY6MUk59uyaWpXuO4+ps5b3Bkbg4WaF6e4gYBthhwVPakcRsmSlwZKNxu7qanIuIXMnJkyepX7/+Bcfr16/PyZNaml6uNLsDhk6F4MZmJ7ms57rXp1ZlLxJSs3jxx+1mxyk/nF0hrJVxW0v4RKQMu+qOhe+99x6W85boWK1WKleuTFRUFBUrVizScFJ+7T+expsLjP4Fz3evT50g73N33vwq7PoZjm6G9V9A6/tMSinErgJ7HlSsAX5hZqcREXF4ERERTJgwgQ8++KDA8QkTJtC0aVOTUolcmruLE+8OjmTAJyv5aXMcNzcMok9EiNmxyofw9saM9JiV0Ooes9OIiBSLqy5K3XnnncUQQ+Sc3DwbT/2wmdM5ebSv7c+d7aoXPKFCINz0Ivz8NCx6DRr2NY5JydPSPRGRqzJu3Dh69erFH3/8Qdu2bQFYtWoVhw4d4ueffzY5ncjFRYT58WiX2ry/aC8vzt1GVI1KBPm4mx2r7Du/2bndrt6dIlImXfXyvS+//JIZM2ZccHzGjBl89dVXRRJKyreP/9rH5kNJeLs789+BEVitF/kHuOXdUCUSspLhNzWGNY2KUiIiV6Vz587s2bOH/v37k5SURFJSEgMGDGD79u188803ZscTuaRHb6xNk1Bfkk/n8OzMLdjVfLv4VW0FVhdIPQqnDpidRkSkWFx1UWrs2LEEBARccDwwMJA333yzSEJJ+bX1cDIfLNoLwKt9GxHi53HxE61O0OtdwAJbpsHB5SUXUgwZJyF+q3FbRSkRkUILCQnhjTfeYNasWcyaNYvXX3+dU6dOMXnyZLOjiVySi5OV94ZE4OpsZcme43y3NtbsSGWfiweEtjBux6w0N4uISDG56qJUbGwsNWrUuOB4eHg4sbH6x0muXWZOHk9O30SuzU7PJsH0i7zCbo5VW0CLO43bC56GvJxizyjnObgcsEPlBlo+KSIiUg7UDvTmue5Go/7X5+/kYGK6yYnKgfwlfCpKiUjZdNVFqcDAQLZs2XLB8c2bN+Pv718koaR8envhLvYdT6eytxtv9GtSoKH+Jd30Enj6w/GdsPqT4g8p52jpnoiISLlzV7vqtK3pz+mcPP41YzN5Ni3jK1bh7Y1rrQoQkTLqqotSw4YN4/HHH+fPP/8kLy+PvLw8Fi9ezBNPPMHQoUOLI6OUAyv+TuTLFQcBGDewKRW9XAv3QM9Kxm58AH+9BclHiiegXCi/KNXR3BwiIiJSYqxWC+8MjsDbzZkNMaf4bOl+syOVbdWiwGKFpBhIPmx2GhGRInfVu++99tprHDx4kJtuuglnZ+PhNpuNkSNHqqeUXJPk0zk8PWMzAHdEVaNLvatcChZxO2z8Bg6thl/HwOCviyGlFJAaD4m7Acu5b/BEROSSBgwYcNn7k5KSSiaISBEI9fPg5T6NeHrGZt79fTed61amYYiP2bHKJjdvqBIBcZsgZhU0HWR2IhGRInXVM6VcXV2ZPn06u3fvZurUqcyePZt9+/bxxRdf4OpayNktIud5+cdtHE3OpLq/J//u1eDqn8BqhV7/A4sT7PgR9v5R9CGloLNTyKs0NWariYjIZfn6+l72Eh4ezsiRI82OKVJotzUP5ZaGQeTk2Rn9QzRZuXlmRyq7zn4BGLPC3BwiIsXgqmdKnVWnTh3q1KlTlFmkHFqw5Shzo+OwWuDdIZF4ul7jn2RwY4h6EFZ/BD8/DQ+vBhf3og0r5xxYYlyrn5SISKF8+eWXZkcQKVIWi4U3BzRhQ8wpdsWn8t7ve3m+R32zY5VN4e1g1QQVpUSkTLrqmVK33XYbb7/99gXHx40bx6BBmk4qhZeQksm/524F4OEbatO8WsXre8IbnocKwXDqAKx4vwgSyiXl95PqbG4OERERMU1ABTfGDmgCwKdL97Hu4EmTE5VR1doa14l7IO24uVlERIrYVRelli5dSs+ePS843qNHD5YuXVokoaTss9vtPDNzC0kZOTQK8eHxm4pg1p27D3Q/09ds2f/gpBpvFotTMXDqIFidoVobs9OIiIiIiW5pFMzAFlWx2+FfP2wmJTPH7Ehlj2clCGxk3I5daW4WEZEidtVFqbS0tIv2jnJxcSElJaVIQknZ9+2aWJbsOY6rs5XxQyJxdb7qP8WLazQAat4AeVnwy3Ng1zbFRe7gMuM6pLnRfFNERETKtZd6NyTUz4PYkxk0e/V3bnlvCU9O28SkpftZ8Xcip9KzzY5Y+oW3M65jVJQSkbLlqhv4NGnShOnTp/PSSy8VOD5t2jQaNmxYZMGk7DqQmM6bC3YC8Fz3+tQJKsLChsUCPd+Bj9vC3t9g13xo0Lvonl/OW7qnflIiIiICPu4ufDCsGY9/v4kjSafZcyyNPcfSmBsdl39OiK87DUN8aBjiS8MqPjQK8aFqRQ8sFouJyUuR8HawbhIcVF8pESlbrroo9eKLLzJgwAD27dvHjTfeCMCiRYv47rvvmDlzZpEHlLIlN8/GU9OjOZ2TR7ta/tzVrnrRv0hAHWj/uLGE75fnodaN4OpV9K9THtntcODMTCkVpUREROSMFuEVWf5cFxJSs9gel8yOuBR2HE1he1wKMScyiEvOJC45kz92JuQ/xtvd+UyBytcoWFXxoU5QBVycimgGfVlydge+Y9vg9CnwuM5erCIiDuKqi1K9e/dm7ty5vPnmm8ycORMPDw8iIiJYvHgxlSppa3i5vE/+2kf0oSS83Zz576AIrNZi+nas49OwZQYkx8KScXDzK8XzOuXNiX2QGgdObhDW2uw0IiIi4kAsFgtBPu4E+bhzY/2g/OOpmTnsPJrKjrjk/ELVnmOppGbmsubASdYcONcg3dXJSp2gCjQ6U6RqGOJLgyreeLu7mPGWHId3EPjXhhN/Q+waqNfd7EQiIkXiqotSAL169aJXr14ApKSk8P333/P000+zYcMG8vLyijSglB1bDyfz/qK9ALzStxGhfh7F92KuntBzHHw/1NhCN2IYBGqb4ut2YIlxHdYaXIrx8xMREZEyw9vdhdY1KtG6xrkvsLNzbfydkMaOoynsiEsxZlcdTSE1M5ftcUbh6nzh/p75y/6MWVW+BPm4la/lf+HtjKJUzAoVpUSkzLimohQYu/BNnjyZWbNmERISwoABA/joo4+KMpuUIZk5eTz1QzS5Njs9GgfTv1lo8b9ovR5Qtwfs+QV+fhpG/WT0nJJrl99PqrO5OURERKRUc3W2nukx5QMtjGN2u53Dp06z/czSvx1nlgHGJWcScyKDmBMZ/LItPv85/L1c85/jbMGqRkAFnIprJr7ZwtvDxq+NopSISBlxVUWp+Ph4pkyZwuTJk0lJSWHw4MFkZWUxd+5cNTmXy3p74S7+TkgjoIIbb/RvUnLfavV4C/b/ZewYt3UmNB1UMq9bFtls53beq9HR3CwiIiJS5lgsFsIqeRJWyZPujYPzj59Kz86fUWUs/0tm3/F0TqRns2xvIsv2Juaf6+5ipX6wUag6uwSwfrAPHq5OZrylonW2r1RcNGSlgVsFU+OIiBSFQhelevfuzdKlS+nVqxfjx4+ne/fuODk5MXHixOLMJ2XAir8T+XLFQQDGDWxCJS/XknvxitWh079g8evw6/9B3VvA3bfkXr8sSdgBGSfAxQtCmpudRkRERMqJil6utK8dQPvaAfnHMnPy2B2fWmD5386jqZzOySP6UBLRh5Lyz7VaoGblCv9Y/ueDfwU3E97NdfALA99qRs/Uw2uNzXxEREq5QhelfvnlFx5//HEeeugh6tSpU5yZpAxJPp3D0zM2A3B7VLUCTS9LTLvHYfM0Yw3+n29Cj7dLPkNZcHaWVHhbcC7BwqKIiIjIP7i7OBER5kdEmF/+sTybnYMn0gvs/LcjLpnEtGz+Tkjj74Q05m2Oyz8/2Me9wIyqhiE+hFX0LL6NeIpCeDvYEgsxK1WUEpEyodBFqeXLlzN58mRatGhBgwYNGDFiBEOHDi3ObFIG/Gfedo4mZxLu78m/ezYwJ4SzG/R8B77pB2s/g8jboUqEOVlKs/x+Up3MzSEiIiJyEU5WC7UqV6BW5Qr0jgjJP56QmnmmQHVuCeCBxHTiUzKJT8lk8a6E/HO93ZxpcKZA1TDEhxvqVSbQ292Mt3Nx4e1gyzQ4qL5SIlI2FLoo1aZNG9q0acP48eOZPn06X3zxBaNHj8Zms/H7778TFhaGt7d3cWaVUmbBlqPM2XQEqwXeHRyJl9s199W/frW6QKMBsH02LPgX3P0bWK3m5Slt8nLh4HLjtopSIiIiUooEersTWM+dLvUC84+lZeWy6+iZGVVHjOvd8amkZuWy9uBJ1h48CYCfpwufDm9BVE1/s+IXVL2DcX1kPeRkgosDFcxERK6BxW6326/1wbt372by5Ml88803JCUlcfPNNzNv3ryizGeKlJQUfH19SU5OxsfHx+w4pVJCSia3jF9KUkYOj3SpxTPd6psdCVLiYEIryE6D3h9Ai1FmJyo9jmyASTca/biePQDWMtAsVESkEDQmuDr6fUlplpNnY9/xtPwZVUv2HGdvQhouThbevq0pA5pXNTsi2O3wv3qQdgzu/Bmqtzc7kYjIRRV2THBdU0Xq1avHuHHjOHz4MN9///31PJWUIXa7nWdnbSEpI4dGIT48cVNdsyMZfEKgy/8Zt/94GdJPmJunNDm7dK96RxWkREREpExycTJ27hvQvCov3NqQnx7rQK8mVcjJszP6h828+9turuP7/KJhsRhL+MDoKyUiUsoVyfolJycn+vXrVyZmScn1m7omlr92H8fV2cp7QyJxdXagZXKtH4DARnD6FCz6j9lpSo/zi1IiIiIi5YC7ixMfDmvGwzfUAuCDxX/zxLRoMnPyzA0WfmZ2VMxyc3OIiBQBB6oWSFlwIDGdNxbsBODZbvWoG+RgfcacnKHX/4zbG7+GQ+vMzVMa5GZD7GrjtvpJiYiISDlitVp4tnt9xt3WFGerhXmb4xj++RpOpGWZF+psUerQWsjLMS+HiEgRUFFKikxuno3RP0RzOiePtjX9ubt9DbMjXVx4W4i8w7i94Cmjibdc2pENkJMBngEQaNIOiiIiIiImGtwqjK/vbo2PuzPrY07R/+OV/J2QZk6YyvXBo6IxPju62ZwMIiJFREUpKTITl+xjU2wS3m7OvDM4AqvVYnakS7v5VXD3g/itsH6y2Wkc29mlezU6GX0MRERERMqhdrUDmP1we6pV8iT2ZAYDPl7Byr8TSz6I1QrVzvaVWlHyry8iUoRUlJIisfVwMuP/2AvAf/o0ItTPw+REV+AVADe9ZNxe/Dqkxpubx5GdX5QSERERKcdqB1ZgzsPtaBFekZTMXEZ+sZYf1h8q+SBnm50fVFFKREo3FaXkumXm5PHUD9Hk2ux0bxTMgOahZkcqnBZ3QkhzyEqB3140O41jys6Aw2uN2ypKiYiIiOBfwY2p90bROyKEXJudZ2duYdzCXdhsJbgzX/UzfaViV4PN5MbrIiLXwSGKUh999BHVq1fH3d2dqKgo1q5de8lzb7jhBiwWywWXXr16XfT8Bx98EIvFwvjx44spvYxbuJu/E9IIqODGmwOaYCktS7ysTmeanltg6w/nZgTJOYfWQF42+IRCpZpmpxERERFxCO4uTnwwNJLHb6wNwMd/7eOx7zeV3M58QU3A1RuykuHY9pJ5TRGRYmB6UWr69OmMHj2al19+mY0bNxIREUG3bt1ISEi46PmzZ8/m6NGj+Zdt27bh5OTEoEGDLjh3zpw5rF69mpCQkOJ+G+XWrvgUvlhxAIBxA5tQycvV5ERXKbQ5tLrHuL3gaWOnOTnn4DLjWv2kRERERAqwWCyMvqUe/xsUgYuThQVbjzL0s9UcTy2BnfmcnKFalHE7ZmXxv56ISDExvSj17rvvct9993HXXXfRsGFDJk6ciKenJ1988cVFz69UqRLBwcH5l99//x1PT88LilJHjhzhscceY+rUqbi4uJTEWymXZq4/DMDNDYO4sX6QyWmu0Y0vGDvLJe6G1R+ZncaxqJ+UiIiIyGXd1qIq39wTha+HC9GHkuj/8Qr2Hkst/hc+21cqZnnxv5aISDExtSiVnZ3Nhg0b6Nq1a/4xq9VK165dWbVqVaGeY/LkyQwdOhQvL6/8YzabjREjRvDMM8/QqFGjKz5HVlYWKSkpBS5yZbl5Nn7cHAfAoBZVTU5zHTwqwi2vG7eXjIMkE5pVOqLMFDiy0bhdvaO5WUREREQcWJua/sx5uB3V/T05fOo0Az5eybK9x4v3RcM7GNcxK8Fegv2sRESKkKlFqcTERPLy8ggKKjjDJigoiPj4K++GtnbtWrZt28a9995b4Pjbb7+Ns7Mzjz/+eKFyjB07Fl9f3/xLWFhY4d9EObZi3wmOp2ZR0dOFG+oFmh3n+kQMNbbWzcmAhc+bncYxxK4Ce57RS8pP/02IiDiaq+nJOWXKlAv6cbq7uxc4x26389JLL1GlShU8PDzo2rUre/fuLe63IVJm1KxcgTkPt6d19UqkZuVy55fr+H5tbPG9YEgzcHaHjBOQuKf4XkdEpBiZvnzvekyePJkmTZrQunXr/GMbNmzg/fffzx98FcaYMWNITk7Ovxw6pJkyhTF7o7F0r3dECK7OpfpPyeiX1Ot/YHGCXfNhz29mJzKflu6JiDisq+3JCeDj41OgL2dMTEyB+8eNG8cHH3zAxIkTWbNmDV5eXnTr1o3MzMzifjsiZUZFL1e+ubc1/ZuFkmezM2b2Vsb+vLN4duZzdoWqrYzbMSuK/vlFREqAqZWEgIAAnJycOHbsWIHjx44dIzg4+LKPTU9PZ9q0adxzzz0Fji9btoyEhASqVauGs7Mzzs7OxMTE8K9//Yvq1atf9Lnc3Nzw8fEpcJHLS8vK5dftxmy2/s1CTU5TRIIaQpuHjNs/Pw0ZJ83NY7YDS4xrLd0TEXE4V9uTE4ymzOf35Tx/prrdbmf8+PG88MIL9O3bl6ZNm/L1118TFxfH3LlzS+AdiZQdbs5OvDs4gqe61gXg06X7eWjqBk5nF8POfOHtjeuDKkqJSOlkalHK1dWVFi1asGjRovxjNpuNRYsW0bZt28s+dsaMGWRlZTF8+PACx0eMGMGWLVuIjo7Ov4SEhPDMM8/w66+/Fsv7KI8WbosnM8dGjQAvIsP8zI5TdG54HnyqQlIMfNMfTieZncgcGSchfptxWzOlREQcyrX25ExLSyM8PJywsDD69u3L9u3ntpE/cOAA8fHxBZ7T19eXqKioyz6n+nKKXJzFYuGJrnV4f2gkrk5Wft1+jCGfrSIhpYhnHlY/U5RSXykRKaVMX3M1evRoJk2axFdffcXOnTt56KGHSE9P56677gJg5MiRjBkz5oLHTZ48mX79+uHv71/guL+/P40bNy5wcXFxITg4mHr16pXIeyoP5mwylu71bxZa6GWSpYKbNwyfaezGdzQavh1gNPwubw4uB+xQuQFUKOX9wkREyphr6clZr149vvjiC3788Ue+/fZbbDYb7dq14/Bh49/zs4+72j6f6sspcnl9I0OZel8UFT1d2HI4mX4frWBXfBGOLUNbgtUFUuPg1MGie14RkRJielFqyJAhvPPOO7z00ktERkYSHR3NwoUL8wdFsbGxHD16tMBjdu/ezfLlyy9Yuicl42jyaVbuOwGUoaV75wtsACN/NHblO7IBpg6ErBLY1teRqJ+UiEiZ0rZtW0aOHElkZCSdO3dm9uzZVK5cmU8//fS6nld9OUWurFX1Ssx5uD01A7yIS85k4Cer+Gv3pfu/XRVXTwhtbtyOWVk0zykiUoJML0oBPProo8TExJCVlcWaNWuIiorKv++vv/5iypQpBc6vV68edrudm2++uVDPf/DgQZ588skiTFy+zd0Uh90OratXIqySp9lxikdwYxgxF9x94dAa+G4IZKebnarkqCglIuKwrqcn51kuLi40a9aMv//+GyD/cVf7nOrLKVI41QO8mP1wO9rUrERaVi53T1nHN6tjrvzAwghvZ1yr2bmIlEIOUZSS0sNut59bute8DM6SOl9IJIyYA24+xj/y3w+F7AyzUxW/1HhI3A1YzvUpEBERh3E9PTnPysvLY+vWrVSpUgWAGjVqEBwcXOA5U1JSWLNmTaGfU0Quz8/Tla/vjmJgi6rY7PDi3G28Nn8Hede7M194B+NaRSkRKYVUlJKrsj0uhT3H0nB1ttKzSRWz4xS/0BYwfBa4VjBmD02/A3LK+NbYB5YZ11WaGksYRUTE4VxtT85XX32V3377jf3797Nx40aGDx9OTEwM9957L2A0ZX7yySd5/fXXmTdvHlu3bmXkyJGEhITQr18/M96iSJnk6mzlvwOb8kw3o9ft5OUHeOCbDaRn5V77k4a1BovV6CmVfKRogoqIlBAVpeSqzNlk/EPXtUEgvh4uJqcpIWGt4Y6Z4OIJ+xbDDyMgN8vsVMXnoJbuiYg4uqvtyXnq1Cnuu+8+GjRoQM+ePUlJSWHlypU0bNgw/5xnn32Wxx57jPvvv59WrVqRlpbGwoULcXd3L/H3J1KWWSwWHulSmw+HNcPV2cofO48x+NNVxCdf4xef7j4Q3NS4HXvp3TJFRByRxW7X3qH/lJKSgq+vL8nJyeqNcJ7cPBttxi4mMS2Lz0e2pGvDoCs/qCw5sAymDoLc01CvJwz6CpxdzU5V9N6PML5pu2Mm1Clc3zYRkbJKY4Kro9+XyNXZEHOK+79ez4n0bIJ93Jl8Z0sahfhe/RMt/D9Y/RG0uAt6jy/ynCIiV6uwYwLNlJJCW/Z3IolpWVTycqVzvcpmxyl5NTrCsO/ByQ12/wyz7oG8HLNTFa1TMUZByuoM1dqYnUZERESkTGsRXpG5j7SndmAF4lMyGTRxFYt3HbvyA//pbB9Q7cAnIqWMilJSaHM2Gkv3ejetgotTOf3TqdUFhn4HTq6wcx7Mvh/yrqMHgKM5eKafVGgLcPM2N4uIiIhIORBWyZNZD7WjfW1/MrLzuPer9Xy54sDVPUm1MxsSJO6GtONFH1JEpJiU08qCXK20rFx+2xEPQP/mVU1OY7I6XWHwN2B1ge2z4ceHwZZndqqicUD9pERERERKmq+HC1Puas3QVmHY7PDKTzt4+cdt5ObZCvcEnpUg8EyPOPWVEpFSREUpKZRfth4lM8dGzQAvIqpewzr3sqZedxg0xVjmtmU6zHsMbIUcNDgqu/1cUap6R3OziIiIiJQzLk5Wxg5owpge9QH4alUM9329nrTC7swX3s64jllRTAlFRIqeilJSKLPPLN0b0DwUi8VichoH0eBWuO1zsDhB9FSY/2TpLkyd2AepR42eWWGtzU4jIiIiUu5YLBYe6FyLT+5ojpuzlT93H2fQxFXEJZ2+8oPDz/aVUlFKREoPFaXkiuKSTrP6wAkA+kaGmpzGwTTqD/0/BYsVNn4FvzxjzDgqjQ4sMa7DWoOLh7lZRERERMqxHk2qMP2BtgRUcGPn0RT6fbSCrYeTL/+gszOl4rfB6aRizygiUhRUlJIrmht9BLsdWteoRFglT7PjOJ6mg6Dvx4AF1n0OC8eUzsJUfj+pzubmEBEREREiw/yY+0g76gZVICE1i8GfruK37fGXfoB3MFSqBdjh0JoSyykicj1UlJLLstvt+bvuDWimWVKXFDkM+nxg3F7zCfz+UukqTNls53beU5NzEREREYdQtaInMx9qR8c6AZzOyeOBbzfw+bL92C81zjw7W+rg8pILKSJyHVSUksvaHpfC3oQ0XJ2t9Gxaxew4jq35SOj1rnF75Qew+LXSU5hK2AEZJ8DFC0Kbm51GRERERM7wcXfhyztbcUdUNex2eH3BTv499xI781XvYFzHrCzZkCIi10hFKbmsWRsPA3BzwyB83F1MTlMKtLoHeowzbi/7Hyx529w8hXV26V54W3DS5ywiIiLiSJydrLzerzEv9GqAxQLfrYnlrinrSMnMKXji2ZlSR6MhK63Ec4qIXC0VpeSScvNs/LQ5DtDSvasS9QDc8oZx+6+xsPQdc/MUhpbuiYiIiDg0i8XCvR1rMnF4CzxcnFi2N5GBn6zk8KmMcyf5VQPfMLDlwuF15oUVESkkFaXkkpbtTSQxLRt/L1c61a1sdpzSpd2j0PU/xu3Fr8GK902Nc1l5uef6DqgoJSIiIuLQujUK5ocH2hLo7caeY2n0+2gl0YeSzp1wdrZUzApT8omIXA0VpeSSZm8yGpz3jgjBxUl/Kletw1PQ5d/G7d9fglUfm5vnUuI3Q1YKuPtCcFOz04iIiIjIFTSp6svcR9pTP9ibxLQshny6il+2HjXuDG9vXKuvlIiUAqo0yEWlZObkbzk7oLmW7l2zzs9Cp2eN27+OgbWTzM1zMWf7SVXvCFYnc7OIiIiISKGE+Hkw86F2dKlXmaxcGw9N3cjEJfuwn50pdXg95GSaG1JE5ApUlJKLWrg1nqxcG7Uqe9Ek1NfsOKVbl/8zZk0B/Pw0rP/S3Dz/dLYopaV7IiIiIqVKBTdnJo1syai24QC89csunv8zA7tXIORlQdxGkxOKiFyeilJyUbM3GbvuDWheFYvFYnKaUs5igZtehraPGj/PfxI2fWtqpHy52RC72rhdvaO5WURERETkqjk7WXmlb2Ne7t0QqwWmbzjMGls9486D6islIo5NRSm5wJGk06zefxKAvpEhJqcpIywWuOV1aP2A8fOPj8Lm6eZmAjiyAXIywDMAAhuYnUZERERErtFd7WswaWRLPF2dWJBSC4DMv5eanEpE5PJUlJILzD3T4LxNzUpUrehpcpoyxGKBHm9Dy7sBO8x9ELbNMjfT+Uv3NCNOREREpFS7qUEQMx5sy36PCABsh9ay4UCCyalERC5NRSkpwG63M3vjmaV7zaqanKYMslig5/+g2Qiw22DWfbDjR/PyqJ+UiIiISJnSKMSX/z06lFRLBTzJZOzk6fy0Oc7sWCIiF6WilBSw9Ugy+46n4+ZspUeTYLPjlE1WK/T+ACKGgT0PZt4NuxaUfI7sDDi81ritopSIiIhImRHs54lH7Q4ANLdv57HvNzFh8V7sdrvJyUREClJRSgqYvdFYundzwyC83V1MTlOGWa3Q9yNoMghsufDDKNjzW8lmOLQG8rLBpypUqlmyry0iIiIixcq5hrGJzcCAWADe+W0PT8/YQmZOnpmxREQKUFFK8uXk2fKn9g5oHmpymnLA6gT9JkLDfmDLgenD4e9FJff6+Uv3OqqflIiIiEhZE94OgLqZ23i9T32sFpi18TD9PlrBvuNpJocTETGoKCX5lu09zon0bAIquNKxTmWz45QPTs5w2+dQ/1bIy4Jpt8P+JSXz2geXGddauiciIiJS9gQ3BdcKkJXM8BrpfHV3a/y9XNkVn0rvD5fnb24kImImFaUk36wzS/d6R4Tg4qQ/jRLj5AIDv4S63SE3E74fCgdXFO9rZqbAkY3G7eodi/e1RERERKTkOTlDWJRxO2YlHetU5ucnOtKmZiUysvN4cno0z83cwulsLecTEfOo8iAApGTm8PuOY4B23TOFsysM/hpqd4WcDJg6CGJXF9/rxa4ymqxXqgl+YcX3OiIiIiJinurtjesY4wvPIB93pt7bhsdvqoPFAtPXH6LvR8vZeyzVxJAiUp6pKCUA/LL1KNm5NmoHVqBxqI/ZcconZzcY8i3UvAFy0uHbgXB4ffG8Vn4/KS3dExERESmzws8WpVbCmZ33nKwWRt9cl6n3RBFQwY09x9LoM2EFM9YfMjGoiJRXKkoJcG7Xvf7NQrGo6bV5XDxg6PfGkrrsVPhmwLlldkXpwJm+VSpKiYiIiJRdIc3A2R0yEiFxT4G72tUO4OcnOtC+tj+nc/J4ZuYWRv8QTUZ2rklhRaQ8UlFKOHQygzUHTmKxQL9m2nXPdK6eMGwaVGsLWcnwTX84uqXonj/jJMRvNW6rn5SIiIhI2eXsBlVbGbdjLuxZGujtztd3RzH65rpYLcYX1b0/XM7ueC3nE5GSoaKU8GO0MUuqTQ1/Qv08TE4jALhVgDtmGIOIzCT4ui8c2140z31wuXFduQFUCCya5xQRERERx3T+Er6LcLJaePymOnx3XxsCvd3YdzydPhOWM31dLPYzS/5ERIqLilLlnN1uZ/aZ7WD7N9csKYfi5g3DZxnTrk+fhK/6QMKu639e9ZMSERERKT/C2xnXB1fk95W6mDY1/fn5iY50qluZrFwbz83aylPTo0nL0nI+ESk+KkqVc1sOJ7P/eDpuzlZ6NA42O478k7svjJgDwU2NXgBf94HEvdf3nCpKiYiIiJQfVVuB1RlS4+DUwcueGlDBjSl3tuLZ7vVwslqYGx1Hnw+XsyMupWSyiki5o6JUOTfnzCypbo2C8XZ3MTmNXJRHRRj5IwQ1hrRj8FVvOLHv2p4rNR4SdwOWc1sEi4iIiEjZ5eoJIc2N25dYwnc+q9XCwzfUZtr9baji687+xHT6fbyCb1fHaDmfiBQ5FaXKsZw8G/M2xwFauufwPCsZhanK9SH1qLGU7wrfdF3UgWXGdZUIo9glIiIiImVf9cv3lbqYVtUrseDxjtxYP5DsXBsvzN3Go99vIjUzp5hCikh5pKJUObZk93FOpmcTUMGNjrUDzI4jV+IVACPngX8dSDlszJhKOnR1z3FgiXFdQ7vuiYiIiJQb+c3OL9yB73Iqebny+ciW/F/P+jhbLSzYcpRbP1zOtiPJxRBSRMojFaXKsbNL9/pEhODspD+FUsE7CEb9BJVqQlKsUZhKiSv84w+emSlVo3Px5BMRERERxxPWGixWOHUA3msCM+6ElR9CzCrIzrjsQ61WC/d3qsX0B9oS6udBzIkMBny8kq9WHtRyvuuVlWqsZFjxPvwwEia0hlUfmZ1KpEQ5mx1AzJF8Ooffdx4DYICW7pUuPlWMwtSXPY2BxZRb4a6fwfsKjepPxRhL/qzOUK1NiUQVEREREQfg7gvNRsDGryE51rhsn2PcZ3GCoIYQ2hKqtoTQFhBQF6xOBZ6iRXhFFjzegadnbOGPncd4ed52Vu8/wVu3NcXXQ71pryg3C45tgyMbjUvcRji+G/hHYe/X/wNnd2h1jykxRUqailLl1C9bj5Kda6NuUAUahfiYHUeulm9VuHO+UZg6uc+YMXXnAqgQeOnHnJ0lFdoC3LxLJqeIiIiIOIY+H8Atr0HcJjiyAQ5vgCPrjY104rcalw1fGue6ekNI5Jki1ZlClU8V/DxdmTSyBV+sOMhbv+zkl23xbItLZsKw5kSE+Zn57hyLLQ8S95wrPh3ZAPHbwHaRfly+1SC0mdGMPvUorJkIC/5l9H9tPKDks4uUMBWlyqnZG42le/2bVcVisZicRq6JXzVjxtSUXsY/el/3hVHzwcv/4ucfWGpc1+hUchlFRERExHG4+0LNG4wLgN0OKUfg8HqjcHJkg1G0yk41vtA8+6UmgE8ohDbHEtqSe6q2pPU9ETw0YzeHTp5m4MSVPN+jAXe3r17+/t/CbjfaasRtPDcL6mg0ZKddeK6nv1F8Cm1uFPpCmkOFygWfy5YL6z6H2fcbn1ftm0rsrYiYQUWpcujQyQzWHjyJxQL9moWYHUeuR6Ua55byJew4U5iaZ+zWdz67XUUpERERESnIYjFm4PtWhUb9jGN5uXB8lzGL6uyMquM7jeJVyhHY+RMATSxWlgTUZ5VzdeafDGHGghjW7mvO24Oa4efpat57Km7piWeKTxvOFaIyEi88z8XLmG0W2vxcIcov3PidX4rFAj3+C6dPwbZZMH24sdFRWKtiezsiZlNRqhyae6bBedua/lTx9TA5jVw3/1rnZkwd2wrf9IeRP4KH37lzTvxtTAd2coOqrU2LKiIiIiIOzskZghsblxZ3Gsey0ozZP+fPqEo5gtPxHXRgBx3OtJRK3+/Gnv/WIqRxR4LqtzOW//mEXr4Q48iyUuHo5jPv+cxSvKTYC8+zukBQI2P209kiVOV6F/TlKhSrFfpNhNNJsG8RTB0Idy+EwAbX/XZEHJGKUuWM3W7P33VvQPOqJqeRIlO5rjFDakovY8Dw7QAYMRfcz/QLO7DEuA5rDS7uZqUUERERkdLIrQJU72Bczko5eqZYYxSq8g5vxCsnjWb2HbB1B2z91DivQtCZJuotzi1Zc3fAnrbnNyI/23frYo3IsUBAnXPvJbSFUZAqyjG2sysM+cZYBXF4nfGl892/QsXwonsNEQeholQ5s/lwMvsT03F3sdK98RV2a5PSJbCBMUPqq97GP6JTB8LwWUZT8wNn+gHU6GxuRhEREREpG3yqgM+t0OBWAJxseaTH7WT2Tz9iPbKBCOs+GlhjcUo7BrsXGBcALMYsotAW5y5BjcCpBHfws9mMnqz5faA2GAWpvOwLz/UNg5Bm5/pAVYksmaKaqxfc/oPRpuP4Tvimn1GYutzGRiKlkIpS5czsjYcB6NYomApu+vjLnOAmxgypr/vAoTXw3RDjH7OzTSrVT0pEREREioPVCa+qjRn+YCOmrollwPwdWLNP09k7jn9HZlAtY4dR/EmKNXpWHd8F0VONxzq7G8We0BbnZlRdqf9SYdntkHzovD5QmyAu2mjm/k8elQo2IQ9tbm4RyLMSjJgDX9wCJ/fDt7cZO3C7+5qXSaSIWex2+z/nI5Z7KSkp+Pr6kpycjI+PA04tvUbZuTai3vyDUxk5TLmrFTfUU5W9zDqyAb7uB1kpENgIErYbzRafjynZb6FEREq5sjomKC76fYnIWTviUnj0u43sT0zHyWrhX7fU5cFOtbBmHD/Xl+rweqNYlJV84RN4Bhg9qfJnVDUHj4pXfuH0E2dmQJ3XByr9+IXnuXieKYQ1P9cHqmJ1x+x/dWIffNHNeB/h7Y3VEC7qDSyOrbBjAhWlLqKsDqh+33GM+75eT0AFN1aPuRFnJ6vZkaQ4HVprrD8/ux1t7Zth+ExzM4mIlDJldUxQXPT7EpHzpWXl8sKcrcyNjgOgc93KvDs4Av8KbudOstng5L7zmqivh/htYMu58An9axv9qc7OqKpUE47tOG8nvA2XaETufK4R+dk+UNfaiNwsR7cY/WOzUqBeTxj8jdGUXsRBqSh1HcrqgOrhqRv4eWs893aowQu3NjQ7jpSEmFVG0/OcDLj5NWj/uNmJRERKlbI6Jigu+n2JyD/Z7XZ+WH+Il37cTlaujSAfN94f2ow2Nf0v/aCcTIjfmt9EncPr4dSBwr+of51zs6tCW0BQ47Kx2c/BFcbYPjcTIm6Hvh8Zu/WJOKDCjglUWi0nkjNy+GNHAgD9m4eanEZKTHhbY9359rnQ8i6z04iIiIhIOWOxWBjSqhqRYRV5eOoG9h1P5/ZJq3mya10e6VIbJ+tFlsu5uENYK+NyVsbJ85b8nZlRdfoU+FSF0GbnZkCFRJbdnkvV28OgKTDtDtj8nbGcsdsbjrnkUKSQVJQqJxZsPUp2no16Qd40rKJvLsuVs+vwRURERERMUi/Ym58e68CLc7cza+Nh3v19D2sOnGD8kGZU9na78hN4VoI6NxsXMBqYZ6WWzE54jqReD+j3Mcx5AFZ/BF7+0PFfZqcSuWaa61dOzNlk7LrXv3koFlXSRURERESkhHm6OvO/wRG8MygCDxcnVvx9gh7vL2Pl34lX/2QWS5koSMUnZ7JwWzxv/bKLB75Zzw/rDnHFDjsRQ6H7W8btRa/C+i+KP6hIMdFMqXLg0MkM1h08hcUC/SK1dE9ERERERMwzsEVVIqr68sh3G9lzLI07Jq/hsRvr8MRNdS6+nK+MSMvKZcvhJDYfSib60Ck2H0omPiWzwDm/bj/G7zuP8fZtTank5XrpJ2vzEGScgKX/hfmjjaV8jfoX8zsQKXoqSpUDczYdAaB9rQCCfctAgz8RERERESnV6gR58+MjHfjPvO1MX3+IDxbtZe2BE3wwtBmBPqX//1ly82zsPpZK9KEkNh9KIvpQEnsT0vjnJCirBeoF+xAZ5ouPhwtfLj/I7zuOEX1oKe8OjqBjncqXfpEu/zYKU+u/gFn3Gb20at1YvG9MpIhp+V4ZZ7fbmb3xzNK9ZpolJSIiUlZ89NFHVK9eHXd3d6Kioli7dm2hHjdt2jQsFgv9+vUrcDwtLY1HH32UqlWr4uHhQcOGDZk4cWIxJBcRMXi4OvH2wKaMHxKJp6sTq/efpMf7y1i657jZ0a6K3W7n8KkM5m+J440FOxg0cSWN//MrvT5Yzr/nbOOH9YfZc8woSIX6edCzSTD/17M+PzzQlm2vdOOXJzoydkBTxvRowJxH2lE7sALHU7MYMXktr8/fQVZu3sVf2GKBnu8YM6RsOTBtuNEIXqQwUo7C9BGQZu5/b5opVcZtOpTEwRMZeLg40b1xsNlxREREpAhMnz6d0aNHM3HiRKKiohg/fjzdunVj9+7dBAYGXvJxBw8e5Omnn6Zjx44X3Dd69GgWL17Mt99+S/Xq1fntt994+OGHCQkJoU+fPsX5dkSknOvXLJQmVX15ZOpGdsWnMurLtTx8Qy2e6loXZyfHm0eRfDqHLYeTiI5NYvNhYxZUYlr2Bed5uzkTEeZHRJgvkWEViQjzJdD78rPAGoX48tOjHRj7y06+XhXD58sPsPzvRD4Y1oy6Qd4XPsDqBP0/g8xk2LcYpg6EuxZCYP2iertSFh1eb+zimBYPtlwY9r1pUSz2K3ZRK39SUlLw9fUlOTkZH5/S3Tzvxbnb+GZ1DP0iQxg/tJnZcUREREoVRx0TREVF0apVKyZMmACAzWYjLCyMxx57jOeff/6ij8nLy6NTp07cfffdLFu2jKSkJObOnZt/f+PGjRkyZAgvvvhi/rEWLVrQo0cPXn/99ULlctTfl4iUDpk5ebw6fwffrYkFoHX1Srw/LJIqvh6mZcrOtbHzaIpRfIpNIvpwEvuPp19wnrPVQoMqPkSG+RER5kdkmB81A7ywXkePrEU7j/HszC2cSM/GzdnKv3s1YESb8ItvXJWVBt/0g8PrwDsE7vkV/Kpd82tLGRb9Hfz0BORlQ+UGMOw7qFSzyF+msGMCzZQqw7Jzbfy0JQ6AAc2rmpxGREREikJ2djYbNmxgzJgx+cesVitdu3Zl1apVl3zcq6++SmBgIPfccw/Lli274P527doxb9487r77bkJCQvjrr7/Ys2cP77333iWfMysri6ysrPyfU1JSrvFdiYiAu4sTb/ZvQtua/oyZvZW1B0/S8/1lvDskki71Lj0LtKjY7XZiTmQQfaYHVPShJHbEpZCdZ7vg3HB/TyKq+uUXoRqF+ODu4lSkeW5qEMQvT3bkmRlbWLLnOC/9uJ0/dyUwbmAElb3dCp7sVgFu/wG+7AnHd8LX/eDuX6HCZXpSSfmSlwu/vwSrPzJ+rn8r9J8IbheZgVeCVJQqw/7anUBSRg6B3m60rx1gdhwREREpAomJieTl5REUFFTgeFBQELt27broY5YvX87kyZOJjo6+5PN++OGH3H///VStWhVnZ2esViuTJk2iU6dOl3zM2LFjeeWVV67pfYiIXErviBCahBq7822PS+GuL9fxQOeaPH1LPVyKcDnfyfRsNh9KYtOZZuSbDyeRlJFzwXl+ni75BaizRajL7oxXhAK93ZlyVyu+WnmQN3/ZxZ+7j9Pj/aX8d2AEXer/o1DnWQlGzIbJ3eDkPvh2ANy5ANw1c7XcyzgJM++G/X8aP3d+Djo/D1bzl8eqKFWGzd5o7LrXNzKkTG+tKiIiIpeWmprKiBEjmDRpEgEBl/6S6sMPP2T16tXMmzeP8PBwli5dyiOPPEJISAhdu3a96GPGjBnD6NGj839OSUkhLCysyN+DiJQ/1QO8mPVQO9782eit9OmS/aw/eIoPhjUj1O/ql/Nl5uSxPS6lwG54sSczLjjP1dlKoxAfIqr60ayaHxFV/Qj397z4krkSYrFYuLN9DdrWCuCJaZvYFZ/KXVPWMaptOGN6Nig4Q8snBEbOhcm3QPwW+H4YDJ8FLqV/R0O5Rgk7jb+DUwfAxdOYHdWwr9mp8qmn1EWUhX4IyRk5tHrjD7LzbPz8eEcahpTO9yEiImImRxwTZGdn4+npycyZMwvsoDdq1CiSkpL48ccfC5wfHR1Ns2bNcHI69z8tNpuxFMVqtbJ7925CQkLw9fVlzpw59OrVK/+8e++9l8OHD7Nw4cJCZXPE35eIlH4/bz3KczO3kJqVi6+HC/8bFEHXhkGXPN9ms7M/MY3oQ8lEHzrF5kPJ7DyaQq7twv/1rVnZK38GVGSYH/WDfXB1Nn/2yKVk5uQxbuFuvlhxAIA6gRV4f2izC/9/7+hmmHIrZKVAvV4w+Gtw0pyUcmfXzzD7PshOM3qMDf0eghuXyEurp1Q5N39rHNl5NuoHe6sgJSIiUoa4urrSokULFi1alF+UstlsLFq0iEcfffSC8+vXr8/WrVsLHHvhhRdITU3l/fffJywsjMzMTHJycrD+Yxq/k5NTfgFLRMQsPZtUoXGIL49+v5Eth5O59+v13NuhBs92r4+rs5WE1Ew2nylARR9KYsuhZFKzci94noAKrsbyu6p+RFbzo2lVP3w9XEx4R9fO3cWJl3o3pHO9yjw9YzN7E9Lo99EKnu1ej7vb1zjXWL1KhLGj2re3we4F8NPj0GeCQyzXkhJgt8Oyd2DxG4AdqneEQV+Bl7/ZyS6golQZNefM0r0BzUNNTiIiIiJFbfTo0YwaNYqWLVvSunVrxo8fT3p6OnfddRcAI0eOJDQ0lLFjx+Lu7k7jxgW/FfXz8wPIP+7q6krnzp155pln8PDwIDw8nCVLlvD111/z7rvvluh7ExG5mGr+nsx8sB1v/bKLL1Yc4PPlB1i8O4GsHBtHkk5fcL67i5Umob4FdsML9fMwdRleUepctzILn+jIc7O28sfOY7y+YCdL9hznnUERBPmcWapXvQMM/BKmD4foqeBREW55HcrI70AuITsd5j4MO+YaP7e+H7q9CU6OWYBVUaoMijmRzvqYU1gt0DdSRSkREZGyZsiQIRw/fpyXXnqJ+Ph4IiMjWbhwYX7z89jY2AtmPV3JtGnTGDNmDHfccQcnT54kPDycN954gwcffLA43oKIyFVzdbbyUu+GtKlZiadnbGb/8XTAqLHUDfQmIsyXyLCKRIT5Ui/IG+cibIruiPwruDFpZAu+WxvLa/N3sGxvIt3HL+Wt25rSrVGwcVL9ntD3I5j7IKyaAJ7+0HH05Z9YSq+kWPj+dji2Fawu0OsdaHGn2akuSz2lLqK090MY/8cexv+xl451Avjmniiz44iIiJRapX1MUNL0+xKRknI0+TRL9xwnrJInTav6UcGtfM+3+DshjSembWJ7XAoAw1pX48VbG+Dpeub3suoj+PX/jNu933f4QoVcg4Mr4IcRkHECvCrD4G8gvK1pcQo7JijbpeNyyG63M2eTsXSvfzPNkhIRERERkbKniq8HQ1pVo12tgHJfkAKoHViBOQ+354HONbFY4Pu1sdz6wXK2Hk42Tmj7CHT8l3F7/lOwfa5pWaUYrPscvu5jFKSqRMD9f5lakLoaKkqVMRtjk4g5kYGnq9O5KZsiIiIiIiJSprk6WxnTowFT74ki2Med/Ynp9P94BZ/8tY88mx1ufNGYIWW3wax7Yd9isyPL9crNhp+ehAX/AlsuNB4Idy0E36pmJys0FaXKmDmbDgPQvVEwXvrGQEREREREpFxpVzuAX57oSI/GweTa7Ly9cBd3fL6auORM6PUuNOwHthyYNhwObzA7rlyrtOPwdV/Y8CVgga7/gds+B1dPs5NdFRWlypCs3Dx+2nwUgP7adU9ERERERKRcqujlysd3NGfcbU3xdHVi9f6T9Hh/GQu2JcCAz6BmF8hJh6m3wfHdZseVq3V0M0zqArErwc0Hbp8OHZ4qlTsrqihVhvy56zjJp3MI8nGjXa0As+OIiIiIiIiISSwWC4NbhbHg8Y5EVPUl+XQOj3y3kWfm7CKt/xQIbQGnT8HX/Yxd26R02DYLJneD5EPgXxvuXQR1u5md6pqpKFWGnF261y8yFCdr6auQioiIiIiISNGqEeDFzIfa8WiX2lgsMGPDYXpN3MSWzp9DQD1IjYNv+hvLwcRx2Wzwxysw827IPQ21uxoFqcp1zU52XVSUKiOSMrJZvCsB0NI9EREREREROcfFycrT3eox7b42hPp5EHMig/5TdvFFzXex+1aFE38bS/kyU8yOKheTmQLThsHyd42f2z0Ot/8AHn6mxioKKkqVEfO3HCUnz06DKj7UD/YxO46IiIiIiIg4mKia/vz8REd6R4SQZ7Pz6tJknnB5mTwPf6NP0bTbISfT7JhyvhP74POusGchOLvDgElwy2tgdTI7WZFQUaqMmL3RWLo3oJlmSYmIiIiIiMjF+Xq48MHQSN4bEkEFN2fmHfZiWMYz5Dh7wcFlMOseyMs1O6YA/P2H0dA8cTd4h8Bdv0DTwWanKlIqSpUBBxPT2RibhNUCfSNDzI4jIiIiIiIiDsxisdC/WVV+eaIjLcIrsjarGiMyniLH4gq75sNPT4DdbnbM8stuh5UfwtRBkJkMVVvD/X9BaHOzkxU5FaXKgDmbjgDQoU5lAn3cTU4jIiIiIiIipUFYJU+m39+Gp7rWZR2NeDjrUfKwQvS38PuLKkyZIScT5jwIv70Adhs0Gw53zgfvILOTFQsVpUo5u92eX5TS0j0RERERERG5Gs5OVp7oWocfHmjLLr+OPJ9zr3HHyg/JW/aeueHKm5Q4+LIHbJkGFifoMQ76TABnN7OTFRuHKEp99NFHVK9eHXd3d6Kioli7du0lz73hhhuwWCwXXHr16gVATk4Ozz33HE2aNMHLy4uQkBBGjhxJXFxcSb2dErUh5hSxJzPwdHXilkZls3IqIiIiIiIixatFeEV+frwjtojhvJ5zBwBOi1/hxJLPTE5WThxaB591gbiN4FERRsyBqAfAYjE7WbEyvSg1ffp0Ro8ezcsvv8zGjRuJiIigW7duJCQkXPT82bNnc/To0fzLtm3bcHJyYtCgQQBkZGSwceNGXnzxRTZu3Mjs2bPZvXs3ffr0Kcm3VWJmn5kl1b1xMJ6uzianERERERERkdLK292F/w2OIGLwC0yiHwB+i59l1U9fYNdSvuKzaSpM6Qlp8RDYEO77E2p2NjtVibDYTf7LioqKolWrVkyYMAEAm81GWFgYjz32GM8///wVHz9+/Hheeukljh49ipeX10XPWbduHa1btyYmJoZq1apd8TlTUlLw9fUlOTkZHx+fq3tDJSgrN4/Wbywi+XQOU++Non3tALMjiYiIlCmlZUzgKPT7EhEpO46cymDnZ3fT9fQvZNmd+TTsLUbdfie+ni5mRys78nKN3lFrPjF+rn8r9P8U3CqYm6sIFHZMYOpMqezsbDZs2EDXrl3zj1mtVrp27cqqVasK9RyTJ09m6NChlyxIASQnJ2OxWPDz87vo/VlZWaSkpBS4lAZ/7kog+XQOwT7utKnpb3YcERERERERKSNCK3rS5V/f8nfATbhZcrn70As89d5kVu07YXa0siHjJEy97VxBqvPzMPibMlGQuhqmFqUSExPJy8sjKKhgL6SgoCDi4+Ov+Pi1a9eybds27r333kuek5mZyXPPPcewYcMuWZ0bO3Ysvr6++ZewsLCreyMmmb3RWLrXt1kITtayvc5URERERERESpaTszO1H/ye1JD2VLBk8k72a7w4eRZvL9xFdq7N7HilV8JOmNQF9v8FLl5GMarLGLCa3mGpxJXqdzx58mSaNGlC69atL3p/Tk4OgwcPxm6388knn1zyecaMGUNycnL+5dChQ8UVucicSs/mz91G360BzaqanEZERERERETKJGc3vEdNJ69KMypZ0vjaZSw//rWG2z5Zyb7jaWanK312LYDPu8Kpg+AXDvf+Dg3LZg/swjC1KBUQEICTkxPHjh0rcPzYsWMEBwdf9rHp6elMmzaNe+6556L3ny1IxcTE8Pvvv192DaObmxs+Pj4FLo5u/pY4cvLsNKziQ71gb7PjiIiIiIiISFnl5o3T8FkQUI8Qy0mmur/FkSOHuPWD5Xy/NlZN0AvDbocl42Da7ZCdBtU7Gg3NgxqZncxUphalXF1dadGiBYsWLco/ZrPZWLRoEW3btr3sY2fMmEFWVhbDhw+/4L6zBam9e/fyxx9/4O9f9votnd11b0DzUJOTiIiIiIiISJnn5Q8j5oBvGDWIY6b3/3DKSWXM7K088M0GTqZnm53QcWWlwYxR8Ocbxs+tHzB+l15lr1ZxtUxfvjd69GgmTZrEV199xc6dO3nooYdIT0/nrrvuAmDkyJGMGTPmgsdNnjyZfv36XVBwysnJYeDAgaxfv56pU6eSl5dHfHw88fHxZGeXjf9IDiSmsyk2CasF+kSGmB1HREREREREygPfUKOY4ulPzZy9/Bb8KV5OOfy24xjdxy9l2d7jZid0PKdi4ItusONHsLpAnw+h5zhw0i6GAM5mBxgyZAjHjx/npZdeIj4+nsjISBYuXJjf/Dw2NhbrP5p97d69m+XLl/Pbb79d8HxHjhxh3rx5AERGRha4788//+SGG24olvdRkuacmSXVsU5lAr3dTU4jIiIiIiIi5UZAHRg+C6bcSkjSOlbV/o7bTjzA3sRMRkxey70davBM93q4OTuZndR8B5bBDyPh9EnwCoQh30K1KLNTORSLXYs/L5CSkoKvry/JyckO11/KbrfT6b9/cujkad4fGknfSC3fExERKS6OPCZwRPp9iYiUIweWwre3QV42uRF38B8e5Ns1xqZhDar48MHQSOoEldP+x3Y7rPscFj4PtlyoEglDp4Jv+dmkrLBjAtOX78nVWR9zikMnT+Pl6sQtDS/fDF5ERERERESkWNToBAO/BIsV581Teb3CTD4f2ZJKXq7sPJrCrR8uZ9LS/WTm5JmdtGTlZsP8J+Hnp42CVJNBcPfCclWQuhoqSpUyszcaS/d6NKmCh6umQ4qIiIiIiIhJGtxq9EgCWPE+XU9+z8InO9KpbmWycm288fNOOo37k8nLD3A6uxwUp9IS4KvesGEKYIGbX4UBk8DFw+xkDktFqVIkMyePBVviABjQTMv2RERERERExGTNhsPNrxm3/3iZwD3TmXJnK8YOaEKIrzsJqVm8Nn8HHcf9yaSl+8nIzjU3b3GJi4bPusCh1eDmC3fMgPZPgMVidjKHpqJUKbJ4VwIpmblU8XWnTU1tHSkiIiIiIiIOoP3j0P5J4/b8J7Hu+olhravx1zNdGDugCVUrepCYlsUbP++kw9t/8slf+0jLKkPFqa0z4YvukHIY/GvDfYugzs1mpyoVVJQqRc4u3esbGYrVqmqriIiIiIiIOIiu/4FmI8Bug1n3wP4luDpbGda6Gn8+fQPjBjYl3N+Tk+nZvL1wFx3eXsyExXtJycwxO/m1s+XBH/8x3m/uaahzC9y7yNihUApFRalS4mR6Nn/tTgBgQHMt3RMREREREREHYrHAreOhQW/Iy4Zpt8PuhXBiHy6ZpxjcrAqLRnfmf4MiqBHgRVJGDu/8tocOby1m/B97SD5dyopTmcnw/TBY/p7xc/snYdg08PAzM1Wp42x2ACmc+VviyLXZaRzqQ93yuq2miIiIiIiIOC4nZxjwOXw3CA4she+HFLjb2bUCt7n7MsDTlxOhHuxJduJolispf3nx3TJvGtSoSusGNfD0CQB3X+Pi4Wdcu3qD1UHm1ST+DdOGQeIecHaHPhOg6SCzU5VKKkqVEmeX7vVvpm0kRURERERExEG5uMPQ72DeY3BorTGjKDvNuC87DbLTsKQcIQAIADh/U/mDZy4XZQF3H3D3u7Bg5f7P6/PvO3Nx8SyapuN7/4CZd0NWMviEwtCpENLs+p+3nFJRqhTYdzyN6ENJOFkt9IkIMTuOiIiIiIiIyKW5ecOgKed+zsuBzBTITDKKVPnXxsWekcSho0fZdygOS2YSPpZ0fC0ZBLlm4WVLw5KXBdjPPeZaWF0uUbDyK1i8KlDoOu8+JxdY+SH88bLRNyssCgZ/A95B1/WrKu9UlCoF5m4yZkl1qhNAZW83k9OIiIiIiIiIXAUnF/DyNy4XYQGqAWF2O7/vOMZLi/ey7UgKZIG7i5WRrYK5v5U/Ac6nCxSzOH3qvJ+T/nHfeT/b88CWAxmJxuWa3oMb5GUZt5uPhJ7vgLP+//x6qSjl4Gw2O3POFKX6N9fSPRERERERESmbLBYLtzQK5uaGQfy5O4H3F/3N5kNJfLYyjq/WxjOsdTUe7NyU4DD3wj+p3Q7Z6QWLVucXrP5Z0PrnfVlnZmblZYHVGbqNhdb3Fc1SQFFRytGtjznF4VOnqeDmzC0NNS1QREREREREyjaLxcKN9YPoUi+QpXsTef+PPWyMTWLKyoN8tyaWIa3CePCGWoT6eRTmycCtgnHxvYaJHrY8yEoxClRuPuBZ6eqfQy5JRSkHN2fTYQB6NA7G3cXpCmeLiIiIiIiIlA0Wi4XOdSvTqU4AK/ed4P0/9rL24Em+WR3DtHWxDGwRxsM31CKskmfxhbA6gUdF4yJFTkUpB5aZk8f8LUcB6N881OQ0IiIiIiIiIiXPYrHQvnYA7WsHsGrfCT5YtJdV+0/w/dpYZqw/xIDmoTzSpTbh/l5mR5WrpKKUA1u0M4HUzFxCfN1pU+PiDeFEREREREREyou2tfxpW8uftQdO8uHivSzbm8gP6w8za+MR+kWG8kiXWtSsXMHsmFJIVrMDyKWdXbrXr1koVquaqImIiIiIiIgAtK5RiW/uiWLWQ+24oV5l8mx2Zm08TNd3l/DktE38nZBqdkQpBBWlHNSJtCz+2n0cgAFauiciIiIiIiJygRbhFZlyV2t+fKQ9XRsEYrPD3Og4bn5vKY9+t5Hd8SpOOTIVpRzUT5vjyLXZaRLqS+1Ab7PjiIiIiIiIiDisiDA/Ph/VivmPdeCWhkHY7TB/y1G6jV/Kw1M3sCMuxeyIchEqSjmoOZuOAJolJSIiIiIiIlJYjUN9+WxkS35+vCM9mwQD8PPWeHp+sIz7v17PtiPJJieU86ko5YD2HU9j8+FknKwWekeEmB1HREREREREpFRpGOLDx3e04NcnO9E7IgSLBX7bcYxbP1zOPVPWEX0oyeyIgopSDmnORmOWVOe6lQmo4GZyGhEREREREZHSqV6wNx8Oa8bvT3Wif7NQrBZYtCuBfh+tYNQXa9kQc8rsiOWailIOxmaz5y/d699MS/dERERERERErlftQG/eGxLJon/dwG3Nq+JktbBkz3Fu+2Qlwz9fw7qDJ82OWC6pKOVg1h48yZGk03i7OXNzwyCz44iIiIiIiIiUGTUCvPjf4AgW/6szQ1qG4Wy1sPzvRAZNXMWwz1azat8J7Ha72THLDRWlHMzZpXs9m1TB3cXJ5DQiIiIiIiIiZU+4vxdvD2zKn0/fwO1R1XBxsrBq/wmGTVrNkE9Xs3xvoopTJUBFKQeSmZPHz1uPAtBfu+6JiIiIiIiIFKuwSp682b8JS57pwsi24bg6WVl78CTDJ69h4MRVLNlzXMWpYqSilAP5Y+cxUrNyCfXzoHX1SmbHERERERERESkXQvw8eLVvY5Y+24U721XHzdnKhphTjPpiLf0+XsniXcdUnCoGKko5kLNL9/o1C8FqtZicRkRERERERKR8CfZ15z99GrHs2S7c26EG7i5WNh9K4u4p6+n5wXI+X7afYymZZscsM1SUchCJaVn8tec4AP2bVTU5jYiIiIiIiEj5Fejjzgu3NmT5czfyQOeaeLo6sfNoCq8v2EmbsYsY+tkqvlsTy6n0bLOjlmoqSjmInzbHkWezE1HVl9qBFcyOIyIiIg7uo48+onr16ri7uxMVFcXatWsL9bhp06ZhsVjo16/fBfft3LmTPn364Ovri5eXF61atSI2NraIk4uIiJQeARXcGNOjASueu5FX+zaiZXhF7HZYvf8k/zdnK63e+IO7vlzLnE2HScvKNTtuqeNsdgAxzNlkLN3r30wNzkVEROTypk+fzujRo5k4cSJRUVGMHz+ebt26sXv3bgIDAy/5uIMHD/L000/TsWPHC+7bt28fHTp04J577uGVV17Bx8eH7du34+7uXpxvRUREpFSo6OXKyLbVGdm2OodPZbBgy1HmbY5je1wKf+4+zp+7j+PmvJWbGgTSJyKEG+oF4u7iZHZsh2exq1PXBVJSUvD19SU5ORkfH59if72/E1Lp+u5SnK0W1vzfTfhXcCv21xQREZErK+kxQWFFRUXRqlUrJkyYAIDNZiMsLIzHHnuM559//qKPycvLo1OnTtx9990sW7aMpKQk5s6dm3//0KFDcXFx4ZtvvrnmXI76+xIRESkufyek8dPmOH7aHMf+xPT8495uztzSKJg+kSG0r+WPs1P5WqhW2DFB+fqtOKjZZxqcd65bWQUpERERuazs7Gw2bNhA165d849ZrVa6du3KqlWrLvm4V199lcDAQO65554L7rPZbCxYsIC6devSrVs3AgMDiYqKKlC0upisrCxSUlIKXERERMqT2oEVeOrmuiz6V2fmP9aBBzrVJMTXndSsXGZtPMyoL9bS+s1FvDB3K2sPnMRm07yg86koZTKbzc6P0XEADGiuBuciIiJyeYmJieTl5REUFFTgeFBQEPHx8Rd9zPLly5k8eTKTJk266P0JCQmkpaXx1ltv0b17d3777Tf69+/PgAEDWLJkySWzjB07Fl9f3/xLWFjYtb8xERGRUsxisdA41JcxPRuw/LkbmfFgW0a0Ccffy5WT6dl8uzqWwZ+uov3bi3ljwQ62Hk5GC9fUU8p0aw6c5EjSabzdnbmpwaV7QIiIiIhci9TUVEaMGMGkSZMICAi46Dk2mw2Avn378tRTTwEQGRnJypUrmThxIp07d77o48aMGcPo0aPzf05JSVFhSkREyj2r1UKr6pVoVb0SL/duyMp9J5i3OY5ft8VzNDmTScsOMGnZAWoEeNE7IoQ+ESHldsMzFaVMNmfTYQB6NamiJmgiIiJyRQEBATg5OXHs2LECx48dO0ZwcPAF5+/bt4+DBw/Su3fv/GNni1DOzs7s3r2bsLAwnJ2dadiwYYHHNmjQgOXLl18yi5ubG25uaj0gIiJyKc5OVjrVrUynupV5vV9j/tp9nJ+2xLFo5zEOJKbzwaK9fLBoLw2q+NAnIoTeEVWoWtHT7NglRkUpE2Xm5PHzVmOavXbdExERkcJwdXWlRYsWLFq0iH79+gFGkWnRokU8+uijF5xfv359tm7dWuDYCy+8QGpqKu+//z5hYWG4urrSqlUrdu/eXeC8PXv2EB4eXmzvRUREpDxxd3Gie+NgujcOJi0rlz92HGPe5jiW7jnOzqMp7DyawtsLd9G8mh99IkLo1TSEyt5l+8sfFaVM9NuOY6Rl5VK1ogetqlcyO46IiIiUEqNHj2bUqFG0bNmS1q1bM378eNLT07nrrrsAGDlyJKGhoYwdOxZ3d3caN25c4PF+fn4ABY4/88wzDBkyhE6dOtGlSxcWLlzITz/9xF9//VVSb0tERKTcqODmTL9mofRrFkpSRja/bItnXnQcqw+cYGNsEhtjk3h1/g7a1QqgT0QI3RoF4+vpYnbsIqeilInmbDSW7vVvForVajE5jYiIiJQWQ4YM4fjx47z00kvEx8cTGRnJwoUL85ufx8bGYrVe3X42/fv3Z+LEiYwdO5bHH3+cevXqMWvWLDp06FAcb0FERETO8PN0ZVjragxrXY1jKZnM33KUnzbHEX0oieV/J7L870RemLuNTnUr0ycyhK4NAvF0LRvlHItd7d4vkJKSgq+vL8nJyfj4+BTLaxxPzaLN2EXk2ews/ldnalYun03NREREHFlJjAnKEv2+REREik7siQx+2hLHvOg4dh9LzT/u4eJE14ZB9IkIoVPdANycHa8/dWHHBGWjtFYK/bQ5jjybnYgwPxWkRERERERERKSAav6ePNKlNo90qc3u+FR+2hzHvM1xxJ7M4KfNcfy0OQ4fd2d6NK5C74gQ2tbyx6mUrcJSUcokczYdAWCAGpyLiIiIiIiIyGXUC/amXnA9/nVLXTYfTmZedBzzt8SRkJrF9PWHmL7+EAEV3Li1qVGgal7ND4vF8QtUKkqZYO+xVLYeScbZaqF3RIjZcURERERERESkFLBYLESG+REZ5se/ezVg7YGTzNscxy/bjpKYlsWUlQeZsvIgoX4e9I4IoU9ECA2qeDtsgUpFKRPMPjNL6oZ6gVTycjU5jYiIiIiIiIiUNk5WC21r+dO2lj+v9GnEir8Tmbc5jt+2x3Mk6TQTl+xj4pJ91A6sQJ+IEHpHhFAjwMvs2AWoKFXCbDY7P55dutdcS/dERERERERE5Pq4OlvpUj+QLvUDOZ2dx+JdCczbfIQ/dx/n74Q03v19D+/+vocmob70iQjh1ogqVPH1MDu2ilIlbfWBE8QlZ+Lt7syN9QPNjiMiIiIiIiIiZYiHqxO9mlahV9MqpGTm8Nv2Y8zbHMeKvxPZeiSZrUeSefOXnbSqXokRbcJNbSukolQJm7PRmCV1a9MQ3F0cb9tGERERERERESkbfNxdGNiiKgNbVCUxLYtfth5l3uY41h08xdoDJ2kRXlFFqfLk2e71aVDFh9Y1KpkdRURERERERETKiYAKboxoW50RbasTl3Sa+VviuLF+kKmZVJQqYZW93bi7Qw2zY4iIiIiIiIhIORXi58H9nWqZHQOr2QFERERERERERKT8UVFKRERERERERERKnIpSIiIiIiIiIiJS4lSUEhERERERERGREqeilIiIiIiIiIiIlDgVpUREREREREREpMSpKCUiIiIiIiIiIiVORSkRERERERERESlxKkqJiIiIiIiIiEiJU1FKRERERERERERKnIpSIiIiIiIiIiJS4lSUEhERERERERGREqeilIiIiIiIiIiIlDgVpUREREREREREpMSpKCUiIiIiIiIiIiXO2ewAjshutwOQkpJichIREREx09mxwNmxgVyexlAiIiIChR9DqSh1EampqQCEhYWZnEREREQcQWpqKr6+vmbHcHgaQ4mIiMj5rjSGstj11d8FbDYbcXFxeHt7Y7FYivz5U1JSCAsL49ChQ/j4+BT588v10efj+PQZOTZ9Po5Pn1Hh2e12UlNTCQkJwWpV14MrKc4xlP5uHZ8+I8emz8fx6TNybPp8rk5hx1CaKXURVquVqlWrFvvr+Pj46I/ZgenzcXz6jBybPh/Hp8+ocDRDqvBKYgylv1vHp8/IsenzcXz6jBybPp/CK8wYSl/5iYiIiIiIiIhIiVNRSkRERERERERESpyKUiZwc3Pj5Zdfxs3NzewochH6fByfPiPHps/H8ekzktJIf7eOT5+RY9Pn4/j0GTk2fT7FQ43ORURERERERESkxGmmlIiIiIiIiIiIlDgVpUREREREREREpMSpKCUiIiIiIiIiIiVORakS9tFHH1G9enXc3d2Jiopi7dq1ZkeSM8aOHUurVq3w9vYmMDCQfv36sXv3brNjySW89dZbWCwWnnzySbOjyHmOHDnC8OHD8ff3x8PDgyZNmrB+/XqzYwmQl5fHiy++SI0aNfDw8KBWrVq89tprqLWklBYaQzkmjZ9KF42fHJPGT45NY6jipaJUCZo+fTqjR4/m5ZdfZuPGjURERNCtWzcSEhLMjibAkiVLeOSRR1i9ejW///47OTk53HLLLaSnp5sdTf5h3bp1fPrppzRt2tTsKHKeU6dO0b59e1xcXPjll1/YsWMH//vf/6hYsaLZ0QR4++23+eSTT5gwYQI7d+7k7bffZty4cXz44YdmRxO5Io2hHJfGT6WHxk+OSeMnx6cxVPHS7nslKCoqilatWjFhwgQAbDYbYWFhPPbYYzz//PMmp5N/On78OIGBgSxZsoROnTqZHUfOSEtLo3nz5nz88ce8/vrrREZGMn78eLNjCfD888+zYsUKli1bZnYUuYhbb72VoKAgJk+enH/stttuw8PDg2+//dbEZCJXpjFU6aHxk2PS+Mlxafzk+DSGKl6aKVVCsrOz2bBhA127ds0/ZrVa6dq1K6tWrTIxmVxKcnIyAJUqVTI5iZzvkUceoVevXgX+WxLHMG/ePFq2bMmgQYMIDAykWbNmTJo0yexYcka7du1YtGgRe/bsAWDz5s0sX76cHj16mJxM5PI0hipdNH5yTBo/OS6NnxyfxlDFy9nsAOVFYmIieXl5BAUFFTgeFBTErl27TEoll2Kz2XjyySdp3749jRs3NjuOnDFt2jQ2btzIunXrzI4iF7F//34++eQTRo8ezf/93/+xbt06Hn/8cVxdXRk1apTZ8cq9559/npSUFOrXr4+TkxN5eXm88cYb3HHHHWZHE7ksjaFKD42fHJPGT45N4yfHpzFU8VJRSuQiHnnkEbZt28by5cvNjiJnHDp0iCeeeILff/8dd3d3s+PIRdhsNlq2bMmbb74JQLNmzdi2bRsTJ07UoMoB/PDDD0ydOpXvvvuORo0aER0dzZNPPklISIg+HxEpEho/OR6Nnxyfxk+OT2Oo4qWiVAkJCAjAycmJY8eOFTh+7NgxgoODTUolF/Poo48yf/58li5dStWqVc2OI2ds2LCBhIQEmjdvnn8sLy+PpUuXMmHCBLKysnBycjIxoVSpUoWGDRsWONagQQNmzZplUiI53zPPPMPzzz/P0KFDAWjSpAkxMTGMHTtWAypxaBpDlQ4aPzkmjZ8cn8ZPjk9jqOKlnlIlxNXVlRYtWrBo0aL8YzabjUWLFtG2bVsTk8lZdrudRx99lDlz5rB48WJq1KhhdiQ5z0033cTWrVuJjo7Ov7Rs2ZI77riD6OhoDagcQPv27S/YBnzPnj2Eh4eblEjOl5GRgdVa8J99JycnbDabSYlECkdjKMem8ZNj0/jJ8Wn85Pg0hipemilVgkaPHs2oUaNo2bIlrVu3Zvz48aSnp3PXXXeZHU0wppx/9913/Pjjj3h7exMfHw+Ar68vHh4eJqcTb2/vC/pTeHl54e/vr74VDuKpp56iXbt2vPnmmwwePJi1a9fy2Wef8dlnn5kdTYDevXvzxhtvUK1aNRo1asSmTZt49913ufvuu82OJnJFGkM5Lo2fHJvGT45P4yfHpzFU8bLY7Xa72SHKkwkTJvDf//6X+Ph4IiMj+eCDD4iKijI7lgAWi+Wix7/88kvuvPPOkg0jhXLDDTdoS2MHM3/+fMaMGcPevXupUaMGo0eP5r777jM7lgCpqam8+OKLzJkzh4SEBEJCQhg2bBgvvfQSrq6uZscTuSKNoRyTxk+lj8ZPjkfjJ8emMVTxUlFKRERERERERERKnHpKiYiIiIiIiIhIiVNRSkRERERERERESpyKUiIiIiIiIiIiUuJUlBIRERERERERkRKnopSIiIiIiIiIiJQ4FaVERERERERERKTEqSglIiIiIiIiIiIlTkUpEREREREREREpcSpKiYgUMYvFwty5c82OISIiIlJqaPwkUj6pKCUiZcqdd96JxWK54NK9e3ezo4mIiIg4JI2fRMQszmYHEBEpat27d+fLL78scMzNzc2kNCIiIiKOT+MnETGDZkqJSJnj5uZGcHBwgUvFihUBY2r4J598Qo8ePfDw8KBmzZrMnDmzwOO3bt3KjTfeiIeHB/7+/tx///2kpaUVOOeLL76gUaNGuLm5UaVKFR599NEC9ycmJtK/f388PT2pU6cO8+bNK943LSIiInIdNH4SETOoKCUi5c6LL77IbbfdxubNm7njjjsYOnQoO3fuBCA9PZ1u3bpRsWJF1q1bx4wZM/jjjz8KDJo++eQTHnnkEe6//362bt3KvHnzqF27doHXeOWVVxg8eDBbtmyhZ8+e3HHHHZw8ebJE36eIiIhIUdH4SUSKhV1EpAwZNWqU3cnJye7l5VXg8sYbb9jtdrsdsD/44IMFHhMVFWV/6KGH7Ha73f7ZZ5/ZK1asaE9LS8u/f8GCBXar1WqPj4+32+12e0hIiP3f//73JTMA9hdeeCH/57S0NDtg/+WXX4rsfYqIiIgUFY2fRMQs6iklImVOly5d+OSTTwocq1SpUv7ttm3bFrivbdu2REdHA7Bz504iIiLw8vLKv799+/bYbDZ2796NxWIhLi6Om2666bIZmjZtmn/by8sLHx8fEhISrvUtiYiIiBQrjZ9ExAwqSolImePl5XXBdPCi4uHhUajzXFxcCvxssViw2WzFEUlERETkumn8JCJmUE8pESl3Vq9efcHPDRo0AKBBgwZs3ryZ9PT0/PtXrFiB1WqlXr16eHt7U716dRYtWlSimUVERETMpPGTiBQHzZQSkTInKyuL+Pj4AsecnZ0JCAgAYMaMGbRs2ZIOHTowdepU1q5dy+TJkwG44447ePnllxk1ahT/+c9/OH78OI899hgjRowgKCgIgP/85z88+OCDBAYG0qNHD1JTU1mxYgWPPfZYyb5RERERkSKi8ZOImEFFKREpcxYuXEiVKlUKHKtXrx67du0CjJ1dpk2bxsMPP0yVKlX4/vvvadiwIQCenp78+uuvPPHEE7Rq1QpPT09uu+023n333fznGjVqFJmZmbz33ns8/fTTBAQEMHDgwJJ7gyIiIiJFTOMnETGDxW63280OISJSUiwWC3PmzKFfv35mRxEREREpFTR+EpHiop5SIiIiIiIiIiJS4lSUEhERERERERGREqfleyIiIiIiIiIiUuI0U0pEREREREREREqcilIiIiIiIiIiIlLiVJQSEREREREREZESp6KUiIiIiIiIiIiUOBWlRERERERERESkxKkoJSIiIiIiIiIiJU5FKRERERERERERKXEqSomIiIiIiIiISIlTUUpERERERERERErc/wMJSifSLdS3vgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_graph(resnet_history)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Kaj-fdL9KUbx" + }, + "source": [ + "# MobileNetV2" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "id": "u9g8oHDsKTuT" + }, + "outputs": [], + "source": [ + "from tensorflow.keras.applications import MobileNetV2\n", + "from tensorflow.keras.optimizers import RMSprop\n" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BujCMkfuKf5j", + "outputId": "ef40d1bf-0c65-4fbc-a540-c262403a57b1" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.RMSprop.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_11\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " mobilenetv2_1.00_224 (Func (None, 7, 7, 1280) 2257984 \n", + " tional) \n", + " \n", + " global_average_pooling2d_9 (None, 1280) 0 \n", + " (GlobalAveragePooling2D) \n", + " \n", + " dense_26 (Dense) (None, 256) 327936 \n", + " \n", + " dropout_15 (Dropout) (None, 256) 0 \n", + " \n", + " dense_27 (Dense) (None, 1) 257 \n", + " \n", + "=================================================================\n", + "Total params: 2586177 (9.87 MB)\n", + "Trainable params: 2189633 (8.35 MB)\n", + "Non-trainable params: 396544 (1.51 MB)\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "base_model = MobileNetV2(weights='imagenet', include_top=False, input_shape=(img_width, img_height, 3))\n", + "\n", + "# Fine-tuning: Unfreeze some layers of the base model\n", + "fine_tune_at = 100\n", + "for layer in base_model.layers[:fine_tune_at]:\n", + " layer.trainable = False\n", + "\n", + "# Add custom layers on top of the base model\n", + "mobilenet_model = models.Sequential([\n", + " base_model,\n", + " layers.GlobalAveragePooling2D(),\n", + " layers.Dense(256, activation='relu'),\n", + " layers.Dropout(0.25),\n", + " layers.Dense(1, activation='sigmoid')\n", + "])\n", + "\n", + "# Compile the model\n", + "mobilenet_model.compile(optimizer=RMSprop(lr=2e-5),\n", + " loss='binary_crossentropy',\n", + " metrics=['accuracy'])\n", + "mobilenet_model.summary()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "TYvYrPf0K7M9", + "outputId": "24b19e54-36d9-46c7-e7ec-0fb9a14600e9" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/8\n", + "612/612 [==============================] - 50s 72ms/step - loss: 0.2494 - accuracy: 0.9081 - val_loss: 0.4931 - val_accuracy: 0.8877\n", + "Epoch 2/8\n", + "612/612 [==============================] - 42s 69ms/step - loss: 0.1715 - accuracy: 0.9365 - val_loss: 3.7807 - val_accuracy: 0.6883\n", + "Epoch 3/8\n", + "612/612 [==============================] - 44s 71ms/step - loss: 0.1329 - accuracy: 0.9525 - val_loss: 2.0640 - val_accuracy: 0.8540\n", + "Epoch 4/8\n", + "612/612 [==============================] - 42s 69ms/step - loss: 0.1052 - accuracy: 0.9633 - val_loss: 1.2584 - val_accuracy: 0.8940\n", + "Epoch 5/8\n", + "612/612 [==============================] - 44s 71ms/step - loss: 0.0821 - accuracy: 0.9710 - val_loss: 1.0975 - val_accuracy: 0.9173\n", + "Epoch 6/8\n", + "612/612 [==============================] - 44s 71ms/step - loss: 0.0633 - accuracy: 0.9783 - val_loss: 0.6710 - val_accuracy: 0.9340\n", + "Epoch 7/8\n", + "612/612 [==============================] - 43s 70ms/step - loss: 0.0511 - accuracy: 0.9821 - val_loss: 0.6188 - val_accuracy: 0.9410\n", + "Epoch 8/8\n", + "612/612 [==============================] - 43s 70ms/step - loss: 0.0416 - accuracy: 0.9853 - val_loss: 0.4923 - val_accuracy: 0.9410\n" + ] + } + ], + "source": [ + "mobilenet_history = mobilenet_model.fit(normalized_train_ds,validation_data=normalized_val_ds,epochs=8)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 507 + }, + "id": "c5ITT-J5M1If", + "outputId": "a52f8691-c37c-45e2-c48b-3a315aa22018" + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_graph(mobilenet_history)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Z9QuDPn2ReOQ" + }, + "source": [ + "\n", + "# Model Testing and Evaluation" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "TF5zCB0qRgHy", + "outputId": "2133e36e-9c3c-4fd5-b234-6c4e8833df2e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "79/79 [==============================] - 3s 33ms/step - loss: 0.5213 - accuracy: 0.8173\n", + "ANN Accuracy: 81.73%\n" + ] + } + ], + "source": [ + "\n", + "# ANN Model\n", + "ann_loss, ann_accuracy = ann_model.evaluate(normalized_test_ds)\n", + "print(\"ANN Accuracy: {:.2f}%\".format(ann_accuracy * 100))\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Y8FSJ9xijUGX", + "outputId": "c71745ed-2ddd-495b-93fc-00cd464e66f8" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "79/79 [==============================] - 3s 37ms/step - loss: 0.3933 - accuracy: 0.8731\n", + "CNN Accuracy: 87.31%\n" + ] + } + ], + "source": [ + "# CNN Model\n", + "cnn_loss, cnn_accuracy = cnn_model.evaluate(normalized_test_ds)\n", + "print(\"CNN Accuracy: {:.2f}%\".format(cnn_accuracy * 100))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pOkd8PjvjY25", + "outputId": "e0cc4a2f-fae9-4a53-b6a6-266b61b1c8bf" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "79/79 [==============================] - 10s 123ms/step - loss: 0.2617 - accuracy: 0.9065\n", + "VGG Accuracy: 90.65%\n" + ] + } + ], + "source": [ + "# VGG Model\n", + "vgg_loss, vgg_accuracy = vgg_model.evaluate(normalized_test_ds)\n", + "print(\"VGG Accuracy: {:.2f}%\".format(vgg_accuracy * 100))\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "7V8lsN_yjnAV", + "outputId": "7e314625-8175-4164-d580-6ed35a5ff8a3" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "79/79 [==============================] - 8s 97ms/step - loss: 0.5250 - accuracy: 0.7688\n", + "ResNet Accuracy: 76.88%\n" + ] + } + ], + "source": [ + "# ResNet Model\n", + "resnet_loss, resnet_accuracy = resnet_model.evaluate(normalized_test_ds)\n", + "print(\"ResNet Accuracy: {:.2f}%\".format(resnet_accuracy * 100))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "h2sLashmLKnR", + "outputId": "069e18d7-1aba-4475-c37a-187b330697fd" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "79/79 [==============================] - 4s 44ms/step - loss: 1.0736 - accuracy: 0.9085\n", + "MobileNet Accuracy: 90.85%\n" + ] + } + ], + "source": [ + "# MobileNet Model\n", + "mobilenet_loss, mobilenet_accuracy = mobilenet_model.evaluate(normalized_test_ds)\n", + "print(\"MobileNet Accuracy: {:.2f}%\".format(mobilenet_accuracy * 100))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NadHAaQ-NaHI" + }, + "source": [ + "# Conclusions" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UrMH3FNk_G3f" + }, + "source": [ + "# Tabulating the models and their Accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gRG79sZQHbNu" + }, + "source": [ + "| Model | Architecture | Accuracy in % (on testing data) |\n", + "|--------------------|:---------------------------:|:------------------------------:|\n", + "| Model 1 | ANN Model | 81.73 |\n", + "| Model 2 | Basic CNN Model | 87.31 |\n", + "| Model 3 | VGG16 Model | 90.65 |\n", + "| Model 4 | ResNet Model | 76.88 |\n", + "| Model 5 | MobileNetV2 Model | 90.85 |\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3YClSga1Hcph" + }, + "source": [ + "**Key Observations:**\n", + "\n", + "1. **Artificial Neural Network (ANN) Model**: Achieved an accuracy of 81.73%. While it provided a decent baseline, it was outperformed by the other convolutional neural network-based models.\n", + "\n", + "2. **Basic CNN Model**: Improved accuracy significantly to 87.31%, demonstrating the power of convolutional layers in image classification tasks.\n", + "\n", + "3. **VGG16 Model**: Achieved an impressive accuracy of 90.65%. This pre-trained model showed strong performance, indicating the benefits of transfer learning.\n", + "\n", + "4. **ResNet Model**: Obtained an accuracy of 76.88%, which was lower than expected. This might be due to overfitting or insufficient fine-tuning.\n", + "\n", + "5. **MobileNetV2 Model**: Achieved the highest accuracy of 90.85%. This lightweight model is particularly suitable for deployment in resource-constrained environments.\n", + "\n", + "**Conclusion:**\n", + "\n", + "The MobileNetV2 model emerged as the best-performing model for the waste classification task, with an accuracy of 90.85%. Its balance of high accuracy and computational efficiency makes it an excellent choice for practical applications. The VGG16 model also performed very well and could be a suitable alternative depending on the specific use case requirements.\n", + "\n", + "Further improvements can be made by fine-tuning the models, exploring additional data augmentation techniques, and experimenting with different hyperparameters.\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "XiYnT4z8O4Ve" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Waste Classification using DL/requirements.txt b/Waste Classification using DL/requirements.txt new file mode 100644 index 000000000..66bbac671 --- /dev/null +++ b/Waste Classification using DL/requirements.txt @@ -0,0 +1,5 @@ +tensorflow +matplotlib +opencv-python +random +shutil \ No newline at end of file From b2678e569e9a3840bd56645b2619a95b087cb864 Mon Sep 17 00:00:00 2001 From: Barrenkala Veera Venkata Karthik <140957865+Karthik110505@users.noreply.github.com> Date: Wed, 22 May 2024 18:38:54 +0530 Subject: [PATCH 3/6] Last_minute Changes --- ..._Pest_and_Disease_Detection_using_DL.ipynb | 13203 ---------------- 1 file changed, 13203 deletions(-) delete mode 100644 Crop Pest and Disease Detection using DL/Model/Crop_Pest_and_Disease_Detection_using_DL.ipynb diff --git a/Crop Pest and Disease Detection using DL/Model/Crop_Pest_and_Disease_Detection_using_DL.ipynb b/Crop Pest and Disease Detection using DL/Model/Crop_Pest_and_Disease_Detection_using_DL.ipynb deleted file mode 100644 index 04cafd22c..000000000 --- a/Crop Pest and Disease Detection using DL/Model/Crop_Pest_and_Disease_Detection_using_DL.ipynb +++ /dev/null @@ -1,13203 +0,0 @@ -{ -<<<<<<< HEAD:Crop Pest and Disease Detection using DL/Model/Crop_Pest_and_Disease_Detection_using_DL_.ipynb - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Note: This notebook is prepared in Google collab" - ] -======= - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Note: This notebook is prepared in Google collab" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "IRYcu-UwzQAU" - }, - "source": [ - "# Loading Basic Libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "_ryJq3BVUI5F" - }, - "outputs": [], - "source": [ - "import os\n", - "import cv2\n", - "import tensorflow as tf\n", - "from tensorflow.keras import layers, models\n", - "import matplotlib.pyplot as plt\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "Mukt0YWFjy8U" - }, - "outputs": [], - "source": [ - "# Creating dataset folder to download the dataset\n", - "mkdir dataset\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "OOTLURUXlxxL", - "outputId": "a34cf209-2f49-4270-978f-d51147037fe5" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/content/dataset\n" - ] - } - ], - "source": [ - "# changing current directory path\n", - "cd dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "QieK7HFzidPC", - "outputId": "3c9a5312-d30e-4950-eb6d-1913c3f56a79" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.6.14 / client 1.6.12)\n", - "Dataset URL: https://www.kaggle.com/datasets/nirmalsankalana/crop-pest-and-disease-detection\n", - "License(s): CC0-1.0\n", - "Downloading crop-pest-and-disease-detection.zip to /content/dataset\n", - "100% 1.24G/1.25G [00:46<00:00, 31.4MB/s]\n", - "100% 1.25G/1.25G [00:46<00:00, 29.0MB/s]\n" - ] - } - ], - "source": [ - "# Downloading the dataset\n", - "!kaggle datasets download -d nirmalsankalana/crop-pest-and-disease-detection" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "collapsed": true, - "id": "FhJ3tB5mjVe0", - "outputId": "8b6fff35-753b-441f-ecc9-4095b21eed32" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n", - " inflating: Tomato leaf blight/leaf blight1302_.jpg \n", - " inflating: Tomato leaf blight/leaf blight1303_.jpg \n", - " inflating: Tomato leaf blight/leaf blight1304_.jpg \n", - " inflating: Tomato leaf blight/leaf blight1305_.jpg \n", - " inflating: Tomato leaf blight/leaf blight1306_.jpg \n", - " inflating: Tomato leaf blight/leaf blight1307_.jpg \n", - " inflating: Tomato leaf blight/leaf blight1308_.jpg \n", - " inflating: Tomato leaf blight/leaf blight1309_.jpg \n", - " inflating: Tomato leaf blight/leaf blight130_.jpg \n", - 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"id": "WlsvA2X-UI5K" - }, - "source": [ - "# Some predifined functions" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "id": "27ZBrqgWUI5M" - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "def plot_graph(history):\n", - " fig, ax = plt.subplots(1, 2, figsize=(12, 5)) # Create a figure with 1 row and 2 columns\n", - "\n", - " # Plot training & validation accuracy values\n", - " ax[0].plot(history.history['accuracy'])\n", - " ax[0].plot(history.history['val_accuracy'])\n", - " ax[0].set_title('Model accuracy')\n", - " ax[0].set_ylabel('Accuracy')\n", - " ax[0].set_xlabel('Epoch')\n", - " ax[0].legend(['Train', 'Validation'], loc='upper left')\n", - "\n", - " # Plot training & validation loss values\n", - " ax[1].plot(history.history['loss'])\n", - " ax[1].plot(history.history['val_loss'])\n", - " ax[1].set_title('Model loss')\n", - " ax[1].set_ylabel('Loss')\n", - " ax[1].set_xlabel('Epoch')\n", - " ax[1].legend(['Train', 'Validation'], loc='upper left')\n", - "\n", - " plt.tight_layout()\n", - " plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "LOwqeXYpUI5N" - }, - "source": [ - "# Data Cleaning and filter valid image data" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Lc9naWsczqaA" - }, - "source": [ - "The given dataset contains some invalid images. The below code filters the dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "collapsed": true, - "id": "yj3Dw9mWUI5O", - "outputId": "ad848410-2b0e-4988-a53c-458d917fdfae" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 392 image files in dataset/Cashew gumosis\n", - "Copied 274 images to train/Cashew gumosis\n", - "Copied 78 images to test/Cashew gumosis\n", - "Copied 40 images to val/Cashew gumosis\n", - "Found 673 image files in dataset/Maize grasshoper\n", - "Copied 471 images to train/Maize grasshoper\n", - "Copied 134 images to test/Maize grasshoper\n", - "Copied 68 images to val/Maize grasshoper\n", - "Found 1368 image files in dataset/Cashew healthy\n", - "Copied 957 images to train/Cashew healthy\n", - "Copied 273 images to test/Cashew healthy\n", - "Copied 138 images to val/Cashew healthy\n", - "Found 1015 image files in dataset/Cassava green mite\n", - "Copied 710 images to train/Cassava green mite\n", - "Copied 203 images to test/Cassava green mite\n", - "Copied 102 images to val/Cassava green mite\n", - "Found 948 image files in dataset/Maize leaf beetle\n", - "Copied 663 images to train/Maize leaf beetle\n", - "Copied 189 images to test/Maize leaf beetle\n", - "Copied 96 images to val/Maize leaf beetle\n", - "Found 1205 image files in dataset/Cassava mosaic\n", - "Copied 843 images to train/Cassava mosaic\n", - "Copied 241 images to test/Cassava mosaic\n", - "Copied 121 images to val/Cassava mosaic\n", - "Found 1006 image files in dataset/Maize leaf blight\n", - "Copied 704 images to train/Maize leaf blight\n", - "Copied 201 images to test/Maize leaf blight\n", - "Copied 101 images to val/Maize leaf blight\n", - "Found 979 image files in dataset/Maize streak virus\n", - "Copied 685 images to train/Maize streak virus\n", - "Copied 195 images to test/Maize streak virus\n", - "Copied 99 images to val/Maize streak virus\n", - "Found 1259 image files in dataset/Maize leaf spot\n", - "Copied 881 images to train/Maize leaf spot\n", - "Copied 251 images to test/Maize leaf spot\n", - "Copied 127 images to val/Maize leaf spot\n", - "Found 285 image files in dataset/Maize fall armyworm\n", - "Copied 199 images to train/Maize fall armyworm\n", - "Copied 57 images to test/Maize fall armyworm\n", - "Copied 29 images to val/Maize fall armyworm\n", - "Found 470 image files in dataset/Tomato healthy\n", - "Copied 329 images to train/Tomato healthy\n", - "Copied 94 images to test/Tomato healthy\n", - "Copied 47 images to val/Tomato healthy\n", - "Found 1193 image files in dataset/Cassava healthy\n", - "Copied 835 images to train/Cassava healthy\n", - "Copied 238 images to test/Cassava healthy\n", - "Copied 120 images to val/Cassava healthy\n", - "Found 518 image files in dataset/Tomato leaf curl\n", - "Copied 362 images to train/Tomato leaf curl\n", - "Copied 103 images to test/Tomato leaf curl\n", - "Copied 53 images to val/Tomato leaf curl\n", - "Found 1301 image files in dataset/Tomato leaf blight\n", - "Copied 910 images to train/Tomato leaf blight\n", - "Copied 260 images to test/Tomato leaf blight\n", - "Copied 131 images to val/Tomato leaf blight\n", - "Found 1682 image files in dataset/Cashew red rust\n", - "Copied 1177 images to train/Cashew red rust\n", - "Copied 336 images to test/Cashew red rust\n", - "Copied 169 images to val/Cashew red rust\n", - "Found 1481 image files in dataset/Cassava brown spot\n", - "Copied 1036 images to train/Cassava brown spot\n", - "Copied 296 images to test/Cassava brown spot\n", - "Copied 149 images to val/Cassava brown spot\n", - "Found 773 image files in dataset/Tomato verticulium wilt\n", - "Copied 541 images to train/Tomato verticulium wilt\n", - "Copied 154 images to test/Tomato verticulium wilt\n", - "Copied 78 images to val/Tomato verticulium wilt\n", - "Found 1729 image files in dataset/Cashew anthracnose\n", - "Copied 1210 images to train/Cashew anthracnose\n", - "Copied 345 images to test/Cashew anthracnose\n", - "Copied 174 images to val/Cashew anthracnose\n", - "Found 2743 image files in dataset/Tomato septoria leaf spot\n", - "Copied 1920 images to train/Tomato septoria leaf spot\n", - "Copied 548 images to test/Tomato septoria leaf spot\n", - "Copied 275 images to val/Tomato septoria leaf spot\n", - "Found 1378 image files in dataset/Cashew leaf miner\n", - "Copied 964 images to train/Cashew leaf miner\n", - "Copied 275 images to test/Cashew leaf miner\n", - "Copied 139 images to val/Cashew leaf miner\n", - "Found 208 image files in dataset/Maize healthy\n", - "Copied 145 images to train/Maize healthy\n", - "Copied 41 images to test/Maize healthy\n", - "Copied 22 images to val/Maize healthy\n", - "Found 2614 image files in dataset/Cassava bacterial blight\n", - "Copied 1829 images to train/Cassava bacterial blight\n", - "Copied 522 images to test/Cassava bacterial blight\n", - "Copied 263 images to val/Cassava bacterial blight\n" - ] - } - ], - "source": [ - "import random\n", - "import shutil\n", - "\n", - "# Set the source directory\n", - "src_dir = 'dataset'\n", - "\n", - "# Set the destination directories\n", - "train_dir = 'train'\n", - "test_dir = 'test'\n", - "val_dir = 'val'\n", - "\n", - "# Set the split ratios\n", - "train_ratio = 0.7\n", - "test_ratio = 0.2\n", - "val_ratio = 0.1\n", - "\n", - "# Create the destination directories if they don't exist\n", - "os.makedirs(train_dir, exist_ok=True)\n", - "os.makedirs(test_dir, exist_ok=True)\n", - "os.makedirs(val_dir, exist_ok=True)\n", - "\n", - "# Get a list of all subdirectories in the source directory\n", - "subdirs = [d for d in os.listdir(src_dir) if os.path.isdir(os.path.join(src_dir, d))]\n", - "\n", - "for subdir in subdirs:\n", - " subdir_path = os.path.join(src_dir, subdir)\n", - " image_files = [f for f in os.listdir(subdir_path) if f.endswith('.jpg') or f.endswith('.png')]\n", - "\n", - " print(f\"Found {len(image_files)} image files in {subdir_path}\")\n", - "\n", - " # If no image files found in the subdirectory, skip it\n", - " if not image_files:\n", - " print(f\"No image files found in {subdir_path}. Skipping...\")\n", - " continue\n", - "\n", - " # Shuffle the list of image files\n", - " random.shuffle(image_files)\n", - "\n", - " # Calculate the number of images for each set\n", - " num_images = len(image_files)\n", - " num_train = int(num_images * train_ratio)\n", - " num_test = int(num_images * test_ratio)\n", - " num_val = num_images - num_train - num_test\n", - "\n", - " # Create subdirectories for train, test, and val sets\n", - " train_subdir = os.path.join(train_dir, subdir)\n", - " test_subdir = os.path.join(test_dir, subdir)\n", - " val_subdir = os.path.join(val_dir, subdir)\n", - "\n", - " os.makedirs(train_subdir, exist_ok=True)\n", - " os.makedirs(test_subdir, exist_ok=True)\n", - " os.makedirs(val_subdir, exist_ok=True)\n", - "\n", - " # Copy the images to their respective sets\n", - " train_files = image_files[:num_train]\n", - " test_files = image_files[num_train:num_train+num_test]\n", - " val_files = image_files[num_train+num_test:]\n", - "\n", - " for file in train_files:\n", - " src_path = os.path.join(subdir_path, file)\n", - " dst_path = os.path.join(train_subdir, file)\n", - " shutil.copy(src_path, dst_path)\n", - "\n", - " for file in test_files:\n", - " src_path = os.path.join(subdir_path, file)\n", - " dst_path = os.path.join(test_subdir, file)\n", - " shutil.copy(src_path, dst_path)\n", - "\n", - " for file in val_files:\n", - " src_path = os.path.join(subdir_path, file)\n", - " dst_path = os.path.join(val_subdir, file)\n", - " shutil.copy(src_path, dst_path)\n", - "\n", - " print(f\"Copied {num_train} images to {train_subdir}\")\n", - " print(f\"Copied {num_test} images to {test_subdir}\")\n", - " print(f\"Copied {num_val} images to {val_subdir}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true, - "id": "KwzUAq8xUI5Q" - }, - "outputs": [], - "source": [ - "# Function to check if an image is valid using OpenCV\n", - "def is_image_valid(image_path):\n", - " try:\n", - " img = cv2.imread(image_path)\n", - " if img is None:\n", - " return False\n", - " return True\n", - " except:\n", - " return False\n", - "\n", - "# Function to filter out corrupted images\n", - "def filter_and_save_valid_images(source_dir, target_dir):\n", - " if not os.path.exists(target_dir):\n", - " os.makedirs(target_dir)\n", - "\n", - " for root, _, files in os.walk(source_dir):\n", - " for file in files:\n", - " file_path = os.path.join(root, file)\n", - " if is_image_valid(file_path):\n", - " relative_path = os.path.relpath(file_path, source_dir)\n", - " target_path = os.path.join(target_dir, relative_path)\n", - " target_subdir = os.path.dirname(target_path)\n", - " if not os.path.exists(target_subdir):\n", - " os.makedirs(target_subdir)\n", - " cv2.imwrite(target_path, cv2.imread(file_path))\n", - "\n", - "# Specify the source and target directories\n", - "train_source_dir = 'train'\n", - "train_target_dir = 'filtered_train'\n", - "val_source_dir = 'val'\n", - "val_target_dir = 'filtered_val'\n", - "test_source_dir = 'test'\n", - "test_target_dir = 'filtered_test'\n", - "\n", - "# Filter and save valid images\n", - "filter_and_save_valid_images(train_source_dir, train_target_dir)\n", - "filter_and_save_valid_images(val_source_dir, val_target_dir)\n", - "filter_and_save_valid_images(test_source_dir, test_target_dir)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "aKljc92wz7Es" - }, - "source": [ - "# Loading the dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "kIbRzkJ5UI5R", - "outputId": "a0820fa1-f15a-4f26-a72e-6c8350757de6" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 17605 files belonging to 22 classes.\n", - "Found 2538 files belonging to 22 classes.\n" - ] - } - ], - "source": [ - "\n", - "# Function to load and preprocess the dataset\n", - "def load_and_preprocess_dataset(directory, image_size=(150, 150), batch_size=32):\n", - " dataset = tf.keras.utils.image_dataset_from_directory(\n", - " directory=directory,\n", - " labels='inferred',\n", - " label_mode='categorical',\n", - " batch_size=batch_size,\n", - " image_size=image_size)\n", - "\n", - " class_names = dataset.class_names\n", - "\n", - " # Define a preprocessing function to scale the images\n", - " def preprocess(image, label):\n", - " # Rescale the image pixel values from [0, 255] to [0, 1]\n", - " image = tf.cast(image, tf.float32) / 255.0\n", - " return image, label\n", - "\n", - " # Apply the preprocessing function to the dataset\n", - " dataset = dataset.map(preprocess, num_parallel_calls=tf.data.AUTOTUNE)\n", - " return dataset, class_names\n", - "\n", - "# Load the datasets\n", - "train_ds, train_class_names = load_and_preprocess_dataset(train_target_dir)\n", - "val_ds, val_class_names = load_and_preprocess_dataset(val_target_dir)\n", - "\n", - "# Ensure the class names are consistent\n", - "assert train_class_names == val_class_names, \"Class names in training and validation datasets do not match.\"\n", - "\n", - "# Prefetching to improve performance\n", - "train_ds = train_ds.prefetch(buffer_size=tf.data.AUTOTUNE)\n", - "val_ds = val_ds.prefetch(buffer_size=tf.data.AUTOTUNE)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "3SStCzWbUI5S", - "outputId": "32ff7f88-d132-427a-d79a-40ae71c41ea9" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 5027 files belonging to 22 classes.\n" - ] - } - ], - "source": [ - "test_ds, test_class_names = load_and_preprocess_dataset(test_target_dir)\n", - "test_ds = test_ds.prefetch(buffer_size=tf.data.AUTOTUNE)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "GOC_HjrYUI5U" - }, - "source": [ - "# ANN model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "MvkyNR5CUI5V", - "outputId": "d44f1c8f-194e-40b6-ccd6-df8c1f68712c" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"sequential\"\n", - "_________________________________________________________________\n", - " Layer (type) Output Shape Param # \n", - "=================================================================\n", - " flatten (Flatten) (None, 67500) 0 \n", - " \n", - " dense (Dense) (None, 512) 34560512 \n", - " \n", - " dense_1 (Dense) (None, 256) 131328 \n", - " \n", - " dense_2 (Dense) (None, 22) 5654 \n", - " \n", - "=================================================================\n", - "Total params: 34697494 (132.36 MB)\n", - "Trainable params: 34697494 (132.36 MB)\n", - "Non-trainable params: 0 (0.00 Byte)\n", - "_________________________________________________________________\n" - ] - } - ], - "source": [ - "from tensorflow.keras import layers, models\n", - "ann_model = models.Sequential([\n", - " layers.Flatten(input_shape=(150,150,3)), # Input layer flattens the image\n", - " layers.Dense(512, activation='relu'), # Hidden layer with 512 units and ReLU activation\n", - " layers.Dense(256, activation='relu'), # Hidden layer with 256 units and ReLU activation\n", - " layers.Dense(22, activation='softmax') # Output layer with softmax activation for classification\n", - "])\n", - "\n", - "# Compile the model\n", - "ann_model.compile(optimizer='adam',\n", - " loss='categorical_crossentropy',\n", - " metrics=['accuracy'])\n", - "\n", - "ann_model.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "cWbWa40UUI5V", - "outputId": "f5c99b23-20d6-4298-e480-a68e572dad6c" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/10\n", - "551/551 [==============================] - 32s 56ms/step - loss: 2.5090 - accuracy: 0.2331 - val_loss: 2.4573 - val_accuracy: 0.2368\n", - "Epoch 2/10\n", - "551/551 [==============================] - 35s 62ms/step - loss: 2.2748 - accuracy: 0.2769 - val_loss: 2.1671 - val_accuracy: 0.3073\n", - "Epoch 3/10\n", - "551/551 [==============================] - 31s 55ms/step - loss: 2.1548 - accuracy: 0.3088 - val_loss: 2.1161 - val_accuracy: 0.3180\n", - "Epoch 4/10\n", - "551/551 [==============================] - 29s 51ms/step - loss: 2.0701 - accuracy: 0.3264 - val_loss: 2.0176 - val_accuracy: 0.3416\n", - "Epoch 5/10\n", - "551/551 [==============================] - 29s 52ms/step - loss: 2.0042 - accuracy: 0.3417 - val_loss: 2.0083 - val_accuracy: 0.3424\n", - "Epoch 6/10\n", - "551/551 [==============================] - 28s 51ms/step - loss: 1.9771 - accuracy: 0.3518 - val_loss: 1.9500 - val_accuracy: 0.3660\n", - "Epoch 7/10\n", - "551/551 [==============================] - 29s 51ms/step - loss: 1.9136 - accuracy: 0.3694 - val_loss: 1.8914 - val_accuracy: 0.3881\n", - "Epoch 8/10\n", - "551/551 [==============================] - 31s 56ms/step - loss: 1.8748 - accuracy: 0.3789 - val_loss: 2.0321 - val_accuracy: 0.3416\n", - "Epoch 9/10\n", - "551/551 [==============================] - 31s 56ms/step - loss: 1.8413 - accuracy: 0.3881 - val_loss: 1.9444 - val_accuracy: 0.3751\n", - "Epoch 10/10\n", - "551/551 [==============================] - 29s 51ms/step - loss: 1.8477 - accuracy: 0.3902 - val_loss: 1.8945 - val_accuracy: 0.3822\n" - ] - } - ], - "source": [ - "ann_history= ann_model.fit(train_ds,validation_data=val_ds,epochs=10)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 507 - }, - "id": "N1G8-LeCBU8d", - "outputId": "6d6717f7-e10d-4757-e619-71b63f3e2338" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_graph(ann_history)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "dlI2JELTUI5W" - }, - "source": [ - "# Basic CNN model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "WmkAduHdUI5W" - }, - "outputs": [], - "source": [ - "from keras.optimizers import Adam" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "O9R68g_lUI5W", - "outputId": "21d1cdb3-56ea-480d-e662-375c82003429" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"sequential_1\"\n", - "_________________________________________________________________\n", - " Layer (type) Output Shape Param # \n", - "=================================================================\n", - " conv2d (Conv2D) (None, 148, 148, 32) 896 \n", - " \n", - " batch_normalization (Batch (None, 148, 148, 32) 128 \n", - " Normalization) \n", - " \n", - " max_pooling2d (MaxPooling2 (None, 74, 74, 32) 0 \n", - " D) \n", - " \n", - " conv2d_1 (Conv2D) (None, 72, 72, 64) 18496 \n", - " \n", - " batch_normalization_1 (Bat (None, 72, 72, 64) 256 \n", - " chNormalization) \n", - " \n", - " conv2d_2 (Conv2D) (None, 70, 70, 64) 36928 \n", - " \n", - " batch_normalization_2 (Bat (None, 70, 70, 64) 256 \n", - " chNormalization) \n", - " \n", - " max_pooling2d_1 (MaxPoolin (None, 35, 35, 64) 0 \n", - " g2D) \n", - " \n", - " conv2d_3 (Conv2D) (None, 33, 33, 128) 73856 \n", - " \n", - " batch_normalization_3 (Bat (None, 33, 33, 128) 512 \n", - " chNormalization) \n", - " \n", - " max_pooling2d_2 (MaxPoolin (None, 16, 16, 128) 0 \n", - " g2D) \n", - " \n", - " conv2d_4 (Conv2D) (None, 14, 14, 128) 147584 \n", - " \n", - " batch_normalization_4 (Bat (None, 14, 14, 128) 512 \n", - " chNormalization) \n", - " \n", - " conv2d_5 (Conv2D) (None, 12, 12, 128) 147584 \n", - " \n", - " batch_normalization_5 (Bat (None, 12, 12, 128) 512 \n", - " chNormalization) \n", - " \n", - " max_pooling2d_3 (MaxPoolin (None, 6, 6, 128) 0 \n", - " g2D) \n", - " \n", - " flatten_1 (Flatten) (None, 4608) 0 \n", - " \n", - " dense_3 (Dense) (None, 1024) 4719616 \n", - " \n", - " batch_normalization_6 (Bat (None, 1024) 4096 \n", - " chNormalization) \n", - " \n", - " dropout (Dropout) (None, 1024) 0 \n", - " \n", - " dense_4 (Dense) (None, 22) 22550 \n", - " \n", - "=================================================================\n", - "Total params: 5173782 (19.74 MB)\n", - "Trainable params: 5170646 (19.72 MB)\n", - "Non-trainable params: 3136 (12.25 KB)\n", - "_________________________________________________________________\n" - ] - } - ], - "source": [ - "from tensorflow.keras import layers, models\n", - "\n", - "# Define the CNN model with batch normalization\n", - "cnn_model = models.Sequential([\n", - " layers.Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)),\n", - " layers.BatchNormalization(),\n", - " layers.MaxPooling2D((2, 2)),\n", - " layers.Conv2D(64, (3, 3), activation='relu'),\n", - " layers.BatchNormalization(),\n", - " layers.Conv2D(64, (3, 3), activation='relu'),\n", - " layers.BatchNormalization(),\n", - " layers.MaxPooling2D((2, 2)),\n", - " layers.Conv2D(128, (3, 3), activation='relu'),\n", - " layers.BatchNormalization(),\n", - " layers.MaxPooling2D((2, 2)),\n", - " layers.Conv2D(128, (3, 3), activation='relu'),\n", - " layers.BatchNormalization(),\n", - " layers.Conv2D(128, (3, 3), activation='relu'),\n", - " layers.BatchNormalization(),\n", - " layers.MaxPooling2D((2, 2)),\n", - " layers.Flatten(),\n", - " layers.Dense(1024, activation='relu'),\n", - " layers.BatchNormalization(),\n", - " layers.Dropout(0.75),\n", - " layers.Dense(22, activation='softmax')\n", - "])\n", - "\n", - "\n", - "# Compile the model\n", - "cnn_model.compile(optimizer='adam',\n", - " loss='categorical_crossentropy',\n", - " metrics=['accuracy'])\n", - "\n", - "# Display the model summary\n", - "cnn_model.summary()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "QjOA_sUCUI5W", - "outputId": "0b72dc7c-2df8-485f-8746-c999f31cb301" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/10\n", - "551/551 [==============================] - 46s 64ms/step - loss: 3.2072 - accuracy: 0.2810 - val_loss: 2.7506 - val_accuracy: 0.2116\n", - "Epoch 2/10\n", - "551/551 [==============================] - 35s 63ms/step - loss: 2.0666 - accuracy: 0.4187 - val_loss: 1.9861 - val_accuracy: 0.4283\n", - "Epoch 3/10\n", - "551/551 [==============================] - 36s 65ms/step - loss: 1.5801 - accuracy: 0.5168 - val_loss: 1.7026 - val_accuracy: 0.4803\n", - "Epoch 4/10\n", - "551/551 [==============================] - 37s 67ms/step - loss: 1.3267 - accuracy: 0.5757 - val_loss: 1.1292 - val_accuracy: 0.6340\n", - "Epoch 5/10\n", - "551/551 [==============================] - 38s 69ms/step - loss: 1.1433 - accuracy: 0.6254 - val_loss: 1.0762 - val_accuracy: 0.6466\n", - "Epoch 6/10\n", - "551/551 [==============================] - 35s 64ms/step - loss: 1.0291 - accuracy: 0.6573 - val_loss: 1.1473 - val_accuracy: 0.6399\n", - "Epoch 7/10\n", - "551/551 [==============================] - 35s 62ms/step - loss: 0.9183 - accuracy: 0.6904 - val_loss: 0.9613 - val_accuracy: 0.6848\n", - "Epoch 8/10\n", - "551/551 [==============================] - 36s 65ms/step - loss: 0.8195 - accuracy: 0.7247 - val_loss: 1.1618 - val_accuracy: 0.6584\n", - "Epoch 9/10\n", - "551/551 [==============================] - 36s 65ms/step - loss: 0.7272 - accuracy: 0.7481 - val_loss: 0.9426 - val_accuracy: 0.7104\n", - "Epoch 10/10\n", - "551/551 [==============================] - 36s 65ms/step - loss: 0.6606 - accuracy: 0.7719 - val_loss: 1.0112 - val_accuracy: 0.6899\n" - ] - } - ], - "source": [ - "cnn_history = cnn_model.fit(\n", - " train_ds,\n", - " validation_data=val_ds,\n", - "\n", - " epochs=10 \n", - ")\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 507 - }, - "id": "09A0ihQVUI5X", - "outputId": "29a4f519-ff28-41f9-b2e6-6de784815841" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_graph(cnn_history)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "edlmW0YVUI5b" - }, - "source": [ - "# Resnet(Basic)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "id": "Ggk2xWRSsy-3" - }, - "outputs": [], - "source": [ - "from tensorflow.keras.applications import ResNet50\n", - "from tensorflow.keras.models import Model\n", - "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D , Dropout\n", - "from tensorflow.keras.optimizers import Adam\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "bYBGWOAPsL1k", - "outputId": "5ff7de24-5683-44dc-a959-e4b69a9b11eb" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n", - "94765736/94765736 [==============================] - 3s 0us/step\n" - ] - } - ], - "source": [ - "base_model = ResNet50(weights='imagenet', include_top=False)\n", - "\n", - "for layer in base_model.layers[-10:]:\n", - " layer.trainable = True\n", - "\n", - "# Add new top layers\n", - "x = base_model.output\n", - "x = GlobalAveragePooling2D()(x)\n", - "x = Dense(1024, activation='relu')(x)\n", - "predictions = Dense(22, activation='softmax')(x)\n", - "\n", - "res_model = Model(inputs=base_model.input, outputs=predictions)\n", - "\n", - "\n", - "# Compile the model\n", - "res_model.compile(optimizer=Adam(learning_rate=1e-5),\n", - " loss='categorical_crossentropy',\n", - " metrics=['accuracy'])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "6ANqeRP8tWV4", - "outputId": "5091ac68-6b19-4668-8584-3b046dd64b7e" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/10\n", - "551/551 [==============================] - 150s 201ms/step - loss: 1.3461 - accuracy: 0.6010 - val_loss: 3.0756 - val_accuracy: 0.0867\n", - "Epoch 2/10\n", - "551/551 [==============================] - 104s 188ms/step - loss: 0.5116 - accuracy: 0.8258 - val_loss: 0.5763 - val_accuracy: 0.8042\n", - "Epoch 3/10\n", - "551/551 [==============================] - 110s 200ms/step - loss: 0.3162 - accuracy: 0.8965 - val_loss: 0.4449 - val_accuracy: 0.8479\n", - "Epoch 4/10\n", - "551/551 [==============================] - 104s 189ms/step - loss: 0.2023 - accuracy: 0.9386 - val_loss: 0.4379 - val_accuracy: 0.8522\n", - "Epoch 5/10\n", - "551/551 [==============================] - 104s 187ms/step - loss: 0.1295 - accuracy: 0.9632 - val_loss: 0.4506 - val_accuracy: 0.8542\n", - "Epoch 6/10\n", - "551/551 [==============================] - 104s 187ms/step - loss: 0.0870 - accuracy: 0.9776 - val_loss: 0.4571 - val_accuracy: 0.8601\n", - "Epoch 7/10\n", - "551/551 [==============================] - 104s 188ms/step - loss: 0.0579 - accuracy: 0.9848 - val_loss: 0.4730 - val_accuracy: 0.8562\n", - "Epoch 8/10\n", - "551/551 [==============================] - 104s 189ms/step - loss: 0.0477 - accuracy: 0.9855 - val_loss: 0.5126 - val_accuracy: 0.8617\n", - "Epoch 9/10\n", - "551/551 [==============================] - 104s 188ms/step - loss: 0.0387 - accuracy: 0.9878 - val_loss: 0.5597 - val_accuracy: 0.8491\n", - "Epoch 10/10\n", - "551/551 [==============================] - 104s 188ms/step - loss: 0.0378 - accuracy: 0.9864 - val_loss: 0.5381 - val_accuracy: 0.8495\n" - ] - } - ], - "source": [ - "resnet_history= res_model.fit(train_ds,validation_data=val_ds,epochs=10)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 468 - }, - "id": "7QLWlY1dvfUU", - "outputId": "5d0f74ca-f30f-47c7-db63-ec68d0c1de13" - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_graph(resnet_history)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WGcdJoLHvjxd" - }, - "source": [ - "# ResNet(Perfect) with Data Augumentaion and Regularizations" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "6kfaFjzVemsj", - "outputId": "6cff68f7-0fe9-460a-955f-97608240456a" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 17605 images belonging to 22 classes.\n", - "Found 2538 images belonging to 22 classes.\n" - ] - } - ], - "source": [ - "# Data augmentaion\n", - "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", - "\n", - "datagen = ImageDataGenerator(\n", - " rotation_range=20,\n", - " width_shift_range=0.2,\n", - " height_shift_range=0.2,\n", - " shear_range=0.2,\n", - " zoom_range=0.2,\n", - " horizontal_flip=True,\n", - " fill_mode='nearest'\n", - ")\n", - "\n", - "train_generator = datagen.flow_from_directory(\n", - " 'filtered_train',\n", - " target_size=(150,150),\n", - " batch_size=32,\n", - " class_mode='categorical'\n", - ")\n", - "\n", - "val_generator = datagen.flow_from_directory(\n", - " 'filtered_val',\n", - " target_size=(150, 150),\n", - " batch_size=32,\n", - " class_mode='categorical'\n", - ")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "id": "AsmRa-MUfsCs" - }, - "outputs": [], - "source": [ - "from tensorflow.keras.applications import ResNet50\n", - "from tensorflow.keras.layers import GlobalAveragePooling2D, Dense, Dropout\n", - "from tensorflow.keras.models import Model\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.regularizers import l2\n", - "from tensorflow.keras.callbacks import EarlyStopping\n" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "h7_q9fbffbgd", - "outputId": "065ed344-07fa-4bfe-de27-62018da4e819" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/5\n", - "551/551 [==============================] - 218s 342ms/step - loss: 16.7730 - accuracy: 0.2183 - val_loss: 15.0907 - val_accuracy: 0.5839\n", - "Epoch 2/5\n", - "551/551 [==============================] - 187s 339ms/step - loss: 15.0545 - accuracy: 0.5087 - val_loss: 14.0818 - val_accuracy: 0.7206\n", - "Epoch 3/5\n", - "551/551 [==============================] - 187s 339ms/step - loss: 14.0944 - accuracy: 0.6330 - val_loss: 13.3983 - val_accuracy: 0.7545\n", - "Epoch 4/5\n", - "551/551 [==============================] - 189s 343ms/step - loss: 13.3619 - accuracy: 0.6914 - val_loss: 12.7492 - val_accuracy: 0.7888\n", - "Epoch 5/5\n", - "551/551 [==============================] - 192s 349ms/step - loss: 12.6688 - accuracy: 0.7296 - val_loss: 12.1332 - val_accuracy: 0.8081\n" - ] - } - ], - "source": [ - "# Load the base model\n", - "base_model = ResNet50(weights='imagenet', include_top=False)\n", - "\n", - "# Make the last 10 layers trainable\n", - "for layer in base_model.layers[-10:]:\n", - " layer.trainable = True\n", - "\n", - "# Add new top layers\n", - "x = base_model.output\n", - "x = GlobalAveragePooling2D()(x)\n", - "x = Dropout(0.5)(x) # Adding dropout for regularization\n", - "x = Dense(1024, activation='relu', kernel_regularizer=l2(0.01))(x)\n", - "x = Dropout(0.5)(x) # Adding another dropout layer for regularization\n", - "predictions = Dense(22, activation='softmax', kernel_regularizer=l2(0.01))(x)\n", - "\n", - "res_model_perfect = Model(inputs=base_model.input, outputs=predictions)\n", - "\n", - "# Re-compile the model\n", - "res_model_perfect.compile(optimizer=Adam(learning_rate=1e-5),\n", - " loss='categorical_crossentropy',\n", - " metrics=['accuracy'])\n", - "\n", - "# Early stopping callback\n", - "early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n", - "\n", - "# Train the model\n", - "res_model_perfect_history = res_model_perfect.fit(train_generator, epochs=5, validation_data=val_generator, callbacks=[early_stopping])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "rottw-JQjOq4", - "outputId": "275497e0-d72c-400c-d5e7-b777665d7d68" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 6/10\n", - "551/551 [==============================] - 189s 344ms/step - loss: 11.9784 - accuracy: 0.7541 - val_loss: 11.4686 - val_accuracy: 0.8282\n", - "Epoch 7/10\n", - "551/551 [==============================] - 189s 342ms/step - loss: 11.3271 - accuracy: 0.7718 - val_loss: 10.8296 - val_accuracy: 0.8294\n", - "Epoch 8/10\n", - "551/551 [==============================] - 191s 347ms/step - loss: 10.6584 - accuracy: 0.7876 - val_loss: 10.2008 - val_accuracy: 0.8373\n", - "Epoch 9/10\n", - "551/551 [==============================] - 192s 347ms/step - loss: 10.0059 - accuracy: 0.8040 - val_loss: 9.6002 - val_accuracy: 0.8408\n", - "Epoch 10/10\n", - "551/551 [==============================] - 194s 352ms/step - loss: 9.3686 - accuracy: 0.8173 - val_loss: 8.9783 - val_accuracy: 0.8455\n" - ] - } - ], - "source": [ - "res_model_perfect_history1 = res_model_perfect.fit(train_generator, initial_epoch=5,epochs=10, validation_data=val_generator, callbacks=[early_stopping])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "lhkIRX0oEpTj" - }, - "source": [ - "# VGG16 Model" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "id": "v93hTELS5Jzl" - }, - "outputs": [], - "source": [ - "from tensorflow.keras.applications import VGG16\n", - "from tensorflow.keras.layers import GlobalAveragePooling2D, Dense, Dropout\n", - "from tensorflow.keras.models import Model\n", - "from tensorflow.keras.optimizers import SGD\n", - "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "TbOkFGjzEw5p", - "outputId": "d30519f5-efa9-465c-b5bb-f43e9f81e484" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n", - "58889256/58889256 [==============================] - 2s 0us/step\n", - "Model: \"model_2\"\n", - "_________________________________________________________________\n", - " Layer (type) Output Shape Param # \n", - "=================================================================\n", - " input_3 (InputLayer) [(None, None, None, 3)] 0 \n", - " \n", - " block1_conv1 (Conv2D) (None, None, None, 64) 1792 \n", - " \n", - " block1_conv2 (Conv2D) (None, None, None, 64) 36928 \n", - " \n", - " block1_pool (MaxPooling2D) (None, None, None, 64) 0 \n", - " \n", - " block2_conv1 (Conv2D) (None, None, None, 128) 73856 \n", - " \n", - " block2_conv2 (Conv2D) (None, None, None, 128) 147584 \n", - " \n", - " block2_pool (MaxPooling2D) (None, None, None, 128) 0 \n", - " \n", - " block3_conv1 (Conv2D) (None, None, None, 256) 295168 \n", - " \n", - " block3_conv2 (Conv2D) (None, None, None, 256) 590080 \n", - " \n", - " block3_conv3 (Conv2D) (None, None, None, 256) 590080 \n", - " \n", - " block3_pool (MaxPooling2D) (None, None, None, 256) 0 \n", - " \n", - " block4_conv1 (Conv2D) (None, None, None, 512) 1180160 \n", - " \n", - " block4_conv2 (Conv2D) (None, None, None, 512) 2359808 \n", - " \n", - " block4_conv3 (Conv2D) (None, None, None, 512) 2359808 \n", - " \n", - " block4_pool (MaxPooling2D) (None, None, None, 512) 0 \n", - " \n", - " block5_conv1 (Conv2D) (None, None, None, 512) 2359808 \n", - " \n", - " block5_conv2 (Conv2D) (None, None, None, 512) 2359808 \n", - " \n", - " block5_conv3 (Conv2D) (None, None, None, 512) 2359808 \n", - " \n", - " block5_pool (MaxPooling2D) (None, None, None, 512) 0 \n", - " \n", - " global_average_pooling2d_2 (None, 512) 0 \n", - " (GlobalAveragePooling2D) \n", - " \n", - " dense_4 (Dense) (None, 1024) 525312 \n", - " \n", - " dense_5 (Dense) (None, 22) 22550 \n", - " \n", - "=================================================================\n", - "Total params: 15262550 (58.22 MB)\n", - "Trainable params: 15262550 (58.22 MB)\n", - "Non-trainable params: 0 (0.00 Byte)\n", - "_________________________________________________________________\n" - ] - } - ], - "source": [ - "\n", - "# Load the base model\n", - "base_model = VGG16(weights='imagenet', include_top=False)\n", - "\n", - "# Make more layers trainable\n", - "for layer in base_model.layers[-10:]:\n", - " layer.trainable = True\n", - "\n", - "# Add new top layers\n", - "x = base_model.output\n", - "x = GlobalAveragePooling2D()(x)\n", - "x = Dense(1024, activation='relu')(x)\n", - "predictions = Dense(22, activation='softmax')(x)\n", - "\n", - "# Create the final model\n", - "vgg_model = Model(inputs=base_model.input, outputs=predictions)\n", - "\n", - "vgg_model.compile(optimizer=Adam(learning_rate=1e-5),\n", - " loss='categorical_crossentropy',\n", - " metrics=['accuracy'])\n", - "\n", - "vgg_model.summary()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Nc8DXxH8MvnG", - "outputId": "babb6905-0dab-4fc2-d615-6e08898524ca" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/10\n", - "551/551 [==============================] - 148s 239ms/step - loss: 1.2364 - accuracy: 0.5985 - val_loss: 0.7437 - val_accuracy: 0.7360\n", - "Epoch 2/10\n", - "551/551 [==============================] - 123s 223ms/step - loss: 0.5993 - accuracy: 0.7848 - val_loss: 0.6208 - val_accuracy: 0.7683\n", - "Epoch 3/10\n", - "551/551 [==============================] - 128s 232ms/step - loss: 0.4672 - accuracy: 0.8295 - val_loss: 0.5241 - val_accuracy: 0.8081\n", - "Epoch 4/10\n", - "551/551 [==============================] - 128s 232ms/step - loss: 0.3806 - accuracy: 0.8620 - val_loss: 0.4916 - val_accuracy: 0.8140\n", - "Epoch 5/10\n", - "551/551 [==============================] - 124s 224ms/step - loss: 0.3119 - accuracy: 0.8891 - val_loss: 0.5329 - val_accuracy: 0.8069\n", - "Epoch 6/10\n", - "551/551 [==============================] - 124s 224ms/step - loss: 0.2599 - accuracy: 0.9063 - val_loss: 0.5281 - val_accuracy: 0.8128\n", - "Epoch 7/10\n", - "551/551 [==============================] - 123s 223ms/step - loss: 0.2171 - accuracy: 0.9225 - val_loss: 0.5258 - val_accuracy: 0.8262\n", - "Epoch 8/10\n", - "551/551 [==============================] - 128s 232ms/step - loss: 0.1756 - accuracy: 0.9380 - val_loss: 0.5114 - val_accuracy: 0.8353\n", - "Epoch 9/10\n", - "551/551 [==============================] - 123s 223ms/step - loss: 0.1501 - accuracy: 0.9474 - val_loss: 0.5555 - val_accuracy: 0.8286\n", - "Epoch 10/10\n", - "551/551 [==============================] - 128s 232ms/step - loss: 0.1284 - accuracy: 0.9558 - val_loss: 0.5891 - val_accuracy: 0.8270\n" - ] - } - ], - "source": [ - "vgg_history= vgg_model.fit(train_ds,epochs=10,validation_data=val_ds)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 468 - }, - "id": "5E-06ul8He9M", - "outputId": "210c701f-518b-4253-86ec-f5754d5798eb" - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_graph(vgg_history)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "RcmcV-PLnCw_" - }, - "source": [ - "# Model Evaluation and Best model Testing" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "P6m-twEGm_AH", - "outputId": "9ecddb0d-3dc8-4cc5-ac29-4f7e77b5aeff" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "157/157 [==============================] - 7s 38ms/step - loss: 1.8495 - accuracy: 0.3898\n", - "ANN Test accuracy: 38.98%\n" - ] - } - ], - "source": [ - "loss_ann, accuracy_ann = ann_model.evaluate(test_ds)\n", - "print(f\"ANN Test accuracy: {accuracy_ann * 100:.2f}%\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "sAs7JCS1MQgL", - "outputId": "19d046a2-6e95-4542-8f60-3aa69acd6ef7" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "157/157 [==============================] - 9s 53ms/step - loss: 0.9678 - accuracy: 0.6992\n", - "CNN Test accuracy: 69.92%\n" - ] - } - ], - "source": [ - "loss_cnn, accuracy_cnn = cnn_model.evaluate(test_ds)\n", - "print(f\"CNN Test accuracy: {accuracy_cnn * 100:.2f}%\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "K2zPnXNaMQP6", - "outputId": "ba65fa34-0993-443c-e39f-71bd03be6a0e" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "158/158 [==============================] - 9s 57ms/step - loss: 0.5548 - accuracy: 0.8562\n", - "ResNet Test accuracy: 85.62%\n" - ] - } - ], - "source": [ - "loss_res, accuracy_res = res_model.evaluate(test_ds)\n", - "print(f\"ResNet Test accuracy: {accuracy_res * 100:.2f}%\")\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "vkawaMLVMP6d", - "outputId": "46a0d654-b34a-47b9-bf61-819e60e6f08e" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "158/158 [==============================] - 11s 70ms/step - loss: 0.5903 - accuracy: 0.8303\n", - "VGG16 Test accuracy: 83.03%\n" - ] - } - ], - "source": [ - "\n", - "loss_vgg16, accuracy_vgg16 = vgg_model.evaluate(test_ds)\n", - "print(f\"VGG16 Test accuracy: {accuracy_vgg16 * 100:.2f}%\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "PLRg-hzH3xCv", - "outputId": "bd18867b-1445-40f0-d001-b63d62339591" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 5027 images belonging to 22 classes.\n" - ] - } - ], - "source": [ - "# Preparing test data for Renset Perfect model\n", - "test_generator = datagen.flow_from_directory(\n", - " 'filtered_test',\n", - " target_size=(150, 150),\n", - " batch_size=32,\n", - " class_mode='categorical'\n", - ")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "9z53Loe7uKVH", - "outputId": "63799035-a5cb-4279-89f5-69188aefb10e" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "158/158 [==============================] - 47s 299ms/step - loss: 8.9857 - accuracy: 0.8472\n", - "ResNet(Perfect) Test accuracy: 84.72%\n" - ] - } - ], - "source": [ - "loss_res_model_perfect, accuracy_res_model_perfect = res_model_perfect.evaluate(test_generator)\n", - "print(f\"ResNet(Perfect) Test accuracy: {accuracy_res_model_perfect * 100:.2f}%\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "swE1qjVO30EQ" - }, - "source": [ - "# Tabulating the Model and it's Accuracy" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "wG_5qUPadj7N" - }, - "source": [ - " | Model Name | Accuracy (%) |\n", - " |-----------------|--------------|\n", - " | ANN | 38.98 |\n", - " | CNN | 69.92 |\n", - " | VGG16 | 83.03 |\n", - " | ResNet | 85.62 |\n", - " | ResNet(Perfect) | 84.58 |" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "2F0_YBHIc1zt" - }, - "source": [ - "- The ResNet(Perfect) model is chosen as the optimal model due to its balance of high accuracy and reduced overfitting, making it the best candidate for deployment. While the standard ResNet model achieved the highest accuracy, its overfitting issues could compromise its performance on new, unseen data. Therefore, for practical applications where generalization is crucial, the ResNet(Perfect) model is the preferred choice\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "_un1ZG_XDvzr" - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "gpuType": "T4", - "provenance": [] - }, - "kaggle": { - "accelerator": "none", - "dataSources": [ - { - "datasetId": 4280832, - "sourceId": 7368427, - "sourceType": "datasetVersion" - } - ], - "dockerImageVersionId": 30699, - "isGpuEnabled": false, - "isInternetEnabled": true, - "language": "python", - "sourceType": "notebook" - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } ->>>>>>> refs/remotes/origin/main:Crop Pest and Disease Detection using DL/Model/Crop_Pest_and_Disease_Detection_using_DL.ipynb - }, - { - "cell_type": "markdown", - "metadata": { - "id": "IRYcu-UwzQAU" - }, - "source": [ - "# Loading Basic Libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "_ryJq3BVUI5F" - }, - "outputs": [], - "source": [ - "import os\n", - "import cv2\n", - "import tensorflow as tf\n", - "from tensorflow.keras import layers, models\n", - "import matplotlib.pyplot as plt\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "Mukt0YWFjy8U" - }, - "outputs": [], - "source": [ - "# Creaking dataset folder to download the dataset\n", - "mkdir dataset\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "OOTLURUXlxxL", - "outputId": "a34cf209-2f49-4270-978f-d51147037fe5" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/content/dataset\n" - ] - } - ], - "source": [ - "# changing current directory path\n", - "cd dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "QieK7HFzidPC", - "outputId": "3c9a5312-d30e-4950-eb6d-1913c3f56a79" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.6.14 / client 1.6.12)\n", - "Dataset URL: https://www.kaggle.com/datasets/nirmalsankalana/crop-pest-and-disease-detection\n", - "License(s): CC0-1.0\n", - "Downloading crop-pest-and-disease-detection.zip to /content/dataset\n", - "100% 1.24G/1.25G [00:46<00:00, 31.4MB/s]\n", - "100% 1.25G/1.25G [00:46<00:00, 29.0MB/s]\n" - ] - } - ], - "source": [ - "# Downloading the dataset\n", - "!kaggle datasets download -d nirmalsankalana/crop-pest-and-disease-detection" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "collapsed": true, - "id": "FhJ3tB5mjVe0", - "outputId": "8b6fff35-753b-441f-ecc9-4095b21eed32" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n", - " inflating: Tomato leaf blight/leaf blight1302_.jpg \n", - " inflating: Tomato leaf blight/leaf blight1303_.jpg \n", - " inflating: Tomato leaf blight/leaf blight1304_.jpg \n", - " inflating: Tomato leaf blight/leaf blight1305_.jpg \n", - " inflating: Tomato leaf blight/leaf blight1306_.jpg \n", - " inflating: Tomato leaf blight/leaf blight1307_.jpg \n", - " inflating: Tomato leaf blight/leaf blight1308_.jpg \n", - " inflating: Tomato leaf blight/leaf blight1309_.jpg \n", - " inflating: Tomato leaf blight/leaf blight130_.jpg \n", - " inflating: Tomato leaf blight/leaf blight1310_.jpg \n", - " inflating: Tomato leaf blight/leaf blight1311_.jpg \n", - " inflating: Tomato leaf blight/leaf blight1312_.jpg \n", - " inflating: Tomato leaf blight/leaf blight1313_.jpg \n", - 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" inflating: Tomato verticulium wilt/verticulium wilt94_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt95_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt96_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt97_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt98_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt99_.jpg \n", - " inflating: Tomato verticulium wilt/verticulium wilt9_.jpg \n" - ] - } - ], - "source": [ - "# Unzipping the dataset\n", - "!unzip \\*.zip\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "4V88wjJT2_30" - }, - "outputs": [], - "source": [ - "# Navigating back to content directory\n", - "cd '../'" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WlsvA2X-UI5K" - }, - "source": [ - "# Some predifined functions" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "id": "27ZBrqgWUI5M" - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "def plot_graph(history):\n", - " fig, ax = plt.subplots(1, 2, figsize=(12, 5)) # Create a figure with 1 row and 2 columns\n", - "\n", - " # Plot training & validation accuracy values\n", - " ax[0].plot(history.history['accuracy'])\n", - " ax[0].plot(history.history['val_accuracy'])\n", - " ax[0].set_title('Model accuracy')\n", - " ax[0].set_ylabel('Accuracy')\n", - " ax[0].set_xlabel('Epoch')\n", - " ax[0].legend(['Train', 'Validation'], loc='upper left')\n", - "\n", - " # Plot training & validation loss values\n", - " ax[1].plot(history.history['loss'])\n", - " ax[1].plot(history.history['val_loss'])\n", - " ax[1].set_title('Model loss')\n", - " ax[1].set_ylabel('Loss')\n", - " ax[1].set_xlabel('Epoch')\n", - " ax[1].legend(['Train', 'Validation'], loc='upper left')\n", - "\n", - " plt.tight_layout()\n", - " plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "LOwqeXYpUI5N" - }, - "source": [ - "# Data Cleaning and filter valid image data" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Lc9naWsczqaA" - }, - "source": [ - "The given dataset contains some invalid images. The below code filters the dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "collapsed": true, - "id": "yj3Dw9mWUI5O", - "outputId": "ad848410-2b0e-4988-a53c-458d917fdfae" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 392 image files in dataset/Cashew gumosis\n", - "Copied 274 images to train/Cashew gumosis\n", - "Copied 78 images to test/Cashew gumosis\n", - "Copied 40 images to val/Cashew gumosis\n", - "Found 673 image files in dataset/Maize grasshoper\n", - "Copied 471 images to train/Maize grasshoper\n", - "Copied 134 images to test/Maize grasshoper\n", - "Copied 68 images to val/Maize grasshoper\n", - "Found 1368 image files in dataset/Cashew healthy\n", - "Copied 957 images to train/Cashew healthy\n", - "Copied 273 images to test/Cashew healthy\n", - "Copied 138 images to val/Cashew healthy\n", - "Found 1015 image files in dataset/Cassava green mite\n", - "Copied 710 images to train/Cassava green mite\n", - "Copied 203 images to test/Cassava green mite\n", - "Copied 102 images to val/Cassava green mite\n", - "Found 948 image files in dataset/Maize leaf beetle\n", - "Copied 663 images to train/Maize leaf beetle\n", - "Copied 189 images to test/Maize leaf beetle\n", - "Copied 96 images to val/Maize leaf beetle\n", - "Found 1205 image files in dataset/Cassava mosaic\n", - "Copied 843 images to train/Cassava mosaic\n", - "Copied 241 images to test/Cassava mosaic\n", - "Copied 121 images to val/Cassava mosaic\n", - "Found 1006 image files in dataset/Maize leaf blight\n", - "Copied 704 images to train/Maize leaf blight\n", - "Copied 201 images to test/Maize leaf blight\n", - "Copied 101 images to val/Maize leaf blight\n", - "Found 979 image files in dataset/Maize streak virus\n", - "Copied 685 images to train/Maize streak virus\n", - "Copied 195 images to test/Maize streak virus\n", - "Copied 99 images to val/Maize streak virus\n", - "Found 1259 image files in dataset/Maize leaf spot\n", - "Copied 881 images to train/Maize leaf spot\n", - "Copied 251 images to test/Maize leaf spot\n", - "Copied 127 images to val/Maize leaf spot\n", - "Found 285 image files in dataset/Maize fall armyworm\n", - "Copied 199 images to train/Maize fall armyworm\n", - "Copied 57 images to test/Maize fall armyworm\n", - "Copied 29 images to val/Maize fall armyworm\n", - "Found 470 image files in dataset/Tomato healthy\n", - "Copied 329 images to train/Tomato healthy\n", - "Copied 94 images to test/Tomato healthy\n", - "Copied 47 images to val/Tomato healthy\n", - "Found 1193 image files in dataset/Cassava healthy\n", - "Copied 835 images to train/Cassava healthy\n", - "Copied 238 images to test/Cassava healthy\n", - "Copied 120 images to val/Cassava healthy\n", - "Found 518 image files in dataset/Tomato leaf curl\n", - "Copied 362 images to train/Tomato leaf curl\n", - "Copied 103 images to test/Tomato leaf curl\n", - "Copied 53 images to val/Tomato leaf curl\n", - "Found 1301 image files in dataset/Tomato leaf blight\n", - "Copied 910 images to train/Tomato leaf blight\n", - "Copied 260 images to test/Tomato leaf blight\n", - "Copied 131 images to val/Tomato leaf blight\n", - "Found 1682 image files in dataset/Cashew red rust\n", - "Copied 1177 images to train/Cashew red rust\n", - "Copied 336 images to test/Cashew red rust\n", - "Copied 169 images to val/Cashew red rust\n", - "Found 1481 image files in dataset/Cassava brown spot\n", - "Copied 1036 images to train/Cassava brown spot\n", - "Copied 296 images to test/Cassava brown spot\n", - "Copied 149 images to val/Cassava brown spot\n", - "Found 773 image files in dataset/Tomato verticulium wilt\n", - "Copied 541 images to train/Tomato verticulium wilt\n", - "Copied 154 images to test/Tomato verticulium wilt\n", - "Copied 78 images to val/Tomato verticulium wilt\n", - "Found 1729 image files in dataset/Cashew anthracnose\n", - "Copied 1210 images to train/Cashew anthracnose\n", - "Copied 345 images to test/Cashew anthracnose\n", - "Copied 174 images to val/Cashew anthracnose\n", - "Found 2743 image files in dataset/Tomato septoria leaf spot\n", - "Copied 1920 images to train/Tomato septoria leaf spot\n", - "Copied 548 images to test/Tomato septoria leaf spot\n", - "Copied 275 images to val/Tomato septoria leaf spot\n", - "Found 1378 image files in dataset/Cashew leaf miner\n", - "Copied 964 images to train/Cashew leaf miner\n", - "Copied 275 images to test/Cashew leaf miner\n", - "Copied 139 images to val/Cashew leaf miner\n", - "Found 208 image files in dataset/Maize healthy\n", - "Copied 145 images to train/Maize healthy\n", - "Copied 41 images to test/Maize healthy\n", - "Copied 22 images to val/Maize healthy\n", - "Found 2614 image files in dataset/Cassava bacterial blight\n", - "Copied 1829 images to train/Cassava bacterial blight\n", - "Copied 522 images to test/Cassava bacterial blight\n", - "Copied 263 images to val/Cassava bacterial blight\n" - ] - } - ], - "source": [ - "import os\n", - "import random\n", - "import shutil\n", - "\n", - "# Set the source directory\n", - "src_dir = 'dataset'\n", - "\n", - "# Set the destination directories\n", - "train_dir = 'train'\n", - "test_dir = 'test'\n", - "val_dir = 'val'\n", - "\n", - "# Set the split ratios\n", - "train_ratio = 0.7\n", - "test_ratio = 0.2\n", - "val_ratio = 0.1\n", - "\n", - "# Create the destination directories if they don't exist\n", - "os.makedirs(train_dir, exist_ok=True)\n", - "os.makedirs(test_dir, exist_ok=True)\n", - "os.makedirs(val_dir, exist_ok=True)\n", - "\n", - "# Get a list of all subdirectories in the source directory\n", - "subdirs = [d for d in os.listdir(src_dir) if os.path.isdir(os.path.join(src_dir, d))]\n", - "\n", - "for subdir in subdirs:\n", - " subdir_path = os.path.join(src_dir, subdir)\n", - " image_files = [f for f in os.listdir(subdir_path) if f.endswith('.jpg') or f.endswith('.png')]\n", - "\n", - " print(f\"Found {len(image_files)} image files in {subdir_path}\")\n", - "\n", - " # If no image files found in the subdirectory, skip it\n", - " if not image_files:\n", - " print(f\"No image files found in {subdir_path}. Skipping...\")\n", - " continue\n", - "\n", - " # Shuffle the list of image files\n", - " random.shuffle(image_files)\n", - "\n", - " # Calculate the number of images for each set\n", - " num_images = len(image_files)\n", - " num_train = int(num_images * train_ratio)\n", - " num_test = int(num_images * test_ratio)\n", - " num_val = num_images - num_train - num_test\n", - "\n", - " # Create subdirectories for train, test, and val sets\n", - " train_subdir = os.path.join(train_dir, subdir)\n", - " test_subdir = os.path.join(test_dir, subdir)\n", - " val_subdir = os.path.join(val_dir, subdir)\n", - "\n", - " os.makedirs(train_subdir, exist_ok=True)\n", - " os.makedirs(test_subdir, exist_ok=True)\n", - " os.makedirs(val_subdir, exist_ok=True)\n", - "\n", - " # Copy the images to their respective sets\n", - " train_files = image_files[:num_train]\n", - " test_files = image_files[num_train:num_train+num_test]\n", - " val_files = image_files[num_train+num_test:]\n", - "\n", - " for file in train_files:\n", - " src_path = os.path.join(subdir_path, file)\n", - " dst_path = os.path.join(train_subdir, file)\n", - " shutil.copy(src_path, dst_path)\n", - "\n", - " for file in test_files:\n", - " src_path = os.path.join(subdir_path, file)\n", - " dst_path = os.path.join(test_subdir, file)\n", - " shutil.copy(src_path, dst_path)\n", - "\n", - " for file in val_files:\n", - " src_path = os.path.join(subdir_path, file)\n", - " dst_path = os.path.join(val_subdir, file)\n", - " shutil.copy(src_path, dst_path)\n", - "\n", - " print(f\"Copied {num_train} images to {train_subdir}\")\n", - " print(f\"Copied {num_test} images to {test_subdir}\")\n", - " print(f\"Copied {num_val} images to {val_subdir}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true, - "id": "KwzUAq8xUI5Q" - }, - "outputs": [], - "source": [ - "# Function to check if an image is valid using OpenCV\n", - "def is_image_valid(image_path):\n", - " try:\n", - " img = cv2.imread(image_path)\n", - " if img is None:\n", - " return False\n", - " return True\n", - " except:\n", - " return False\n", - "\n", - "# Function to filter out corrupted images\n", - "def filter_and_save_valid_images(source_dir, target_dir):\n", - " if not os.path.exists(target_dir):\n", - " os.makedirs(target_dir)\n", - "\n", - " for root, _, files in os.walk(source_dir):\n", - " for file in files:\n", - " file_path = os.path.join(root, file)\n", - " if is_image_valid(file_path):\n", - " relative_path = os.path.relpath(file_path, source_dir)\n", - " target_path = os.path.join(target_dir, relative_path)\n", - " target_subdir = os.path.dirname(target_path)\n", - " if not os.path.exists(target_subdir):\n", - " os.makedirs(target_subdir)\n", - " cv2.imwrite(target_path, cv2.imread(file_path))\n", - "\n", - "# Specify the source and target directories\n", - "train_source_dir = 'train'\n", - "train_target_dir = 'filtered_train'\n", - "val_source_dir = 'val'\n", - "val_target_dir = 'filtered_val'\n", - "test_source_dir = 'test'\n", - "test_target_dir = 'filtered_test'\n", - "\n", - "# Filter and save valid images\n", - "filter_and_save_valid_images(train_source_dir, train_target_dir)\n", - "filter_and_save_valid_images(val_source_dir, val_target_dir)\n", - "filter_and_save_valid_images(test_source_dir, test_target_dir)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "aKljc92wz7Es" - }, - "source": [ - "# Loading the dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "kIbRzkJ5UI5R", - "outputId": "a0820fa1-f15a-4f26-a72e-6c8350757de6" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 17605 files belonging to 22 classes.\n", - "Found 2538 files belonging to 22 classes.\n" - ] - } - ], - "source": [ - "\n", - "# Function to load and preprocess the dataset\n", - "def load_and_preprocess_dataset(directory, image_size=(150, 150), batch_size=32):\n", - " dataset = tf.keras.utils.image_dataset_from_directory(\n", - " directory=directory,\n", - " labels='inferred',\n", - " label_mode='categorical',\n", - " batch_size=batch_size,\n", - " image_size=image_size)\n", - "\n", - " class_names = dataset.class_names\n", - "\n", - " # Define a preprocessing function to scale the images\n", - " def preprocess(image, label):\n", - " # Rescale the image pixel values from [0, 255] to [0, 1]\n", - " image = tf.cast(image, tf.float32) / 255.0\n", - " return image, label\n", - "\n", - " # Apply the preprocessing function to the dataset\n", - " dataset = dataset.map(preprocess, num_parallel_calls=tf.data.AUTOTUNE)\n", - " return dataset, class_names\n", - "\n", - "# Load the datasets\n", - "train_ds, train_class_names = load_and_preprocess_dataset(train_target_dir)\n", - "val_ds, val_class_names = load_and_preprocess_dataset(val_target_dir)\n", - "\n", - "# Ensure the class names are consistent\n", - "assert train_class_names == val_class_names, \"Class names in training and validation datasets do not match.\"\n", - "\n", - "# Prefetching to improve performance\n", - "train_ds = train_ds.prefetch(buffer_size=tf.data.AUTOTUNE)\n", - "val_ds = val_ds.prefetch(buffer_size=tf.data.AUTOTUNE)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "3SStCzWbUI5S", - "outputId": "32ff7f88-d132-427a-d79a-40ae71c41ea9" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 5027 files belonging to 22 classes.\n" - ] - } - ], - "source": [ - "test_ds, test_class_names = load_and_preprocess_dataset(test_target_dir)\n", - "test_ds = test_ds.prefetch(buffer_size=tf.data.AUTOTUNE)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "GOC_HjrYUI5U" - }, - "source": [ - "# ANN model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "MvkyNR5CUI5V", - "outputId": "d44f1c8f-194e-40b6-ccd6-df8c1f68712c" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"sequential\"\n", - "_________________________________________________________________\n", - " Layer (type) Output Shape Param # \n", - "=================================================================\n", - " flatten (Flatten) (None, 67500) 0 \n", - " \n", - " dense (Dense) (None, 512) 34560512 \n", - " \n", - " dense_1 (Dense) (None, 256) 131328 \n", - " \n", - " dense_2 (Dense) (None, 22) 5654 \n", - " \n", - "=================================================================\n", - "Total params: 34697494 (132.36 MB)\n", - "Trainable params: 34697494 (132.36 MB)\n", - "Non-trainable params: 0 (0.00 Byte)\n", - "_________________________________________________________________\n" - ] - } - ], - "source": [ - "import tensorflow as tf\n", - "from tensorflow.keras import layers, models\n", - "ann_model = models.Sequential([\n", - " layers.Flatten(input_shape=(150,150,3)), # Input layer flattens the image\n", - " layers.Dense(512, activation='relu'), # Hidden layer with 512 units and ReLU activation\n", - " layers.Dense(256, activation='relu'), # Hidden layer with 256 units and ReLU activation\n", - " layers.Dense(22, activation='softmax') # Output layer with softmax activation for classification\n", - "])\n", - "\n", - "# Compile the model\n", - "ann_model.compile(optimizer='adam',\n", - " loss='categorical_crossentropy',\n", - " metrics=['accuracy'])\n", - "\n", - "ann_model.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "cWbWa40UUI5V", - "outputId": "f5c99b23-20d6-4298-e480-a68e572dad6c" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/10\n", - "551/551 [==============================] - 32s 56ms/step - loss: 2.5090 - accuracy: 0.2331 - val_loss: 2.4573 - val_accuracy: 0.2368\n", - "Epoch 2/10\n", - "551/551 [==============================] - 35s 62ms/step - loss: 2.2748 - accuracy: 0.2769 - val_loss: 2.1671 - val_accuracy: 0.3073\n", - "Epoch 3/10\n", - "551/551 [==============================] - 31s 55ms/step - loss: 2.1548 - accuracy: 0.3088 - val_loss: 2.1161 - val_accuracy: 0.3180\n", - "Epoch 4/10\n", - "551/551 [==============================] - 29s 51ms/step - loss: 2.0701 - accuracy: 0.3264 - val_loss: 2.0176 - val_accuracy: 0.3416\n", - "Epoch 5/10\n", - "551/551 [==============================] - 29s 52ms/step - loss: 2.0042 - accuracy: 0.3417 - val_loss: 2.0083 - val_accuracy: 0.3424\n", - "Epoch 6/10\n", - "551/551 [==============================] - 28s 51ms/step - loss: 1.9771 - accuracy: 0.3518 - val_loss: 1.9500 - val_accuracy: 0.3660\n", - "Epoch 7/10\n", - "551/551 [==============================] - 29s 51ms/step - loss: 1.9136 - accuracy: 0.3694 - val_loss: 1.8914 - val_accuracy: 0.3881\n", - "Epoch 8/10\n", - "551/551 [==============================] - 31s 56ms/step - loss: 1.8748 - accuracy: 0.3789 - val_loss: 2.0321 - val_accuracy: 0.3416\n", - "Epoch 9/10\n", - "551/551 [==============================] - 31s 56ms/step - loss: 1.8413 - accuracy: 0.3881 - val_loss: 1.9444 - val_accuracy: 0.3751\n", - "Epoch 10/10\n", - "551/551 [==============================] - 29s 51ms/step - loss: 1.8477 - accuracy: 0.3902 - val_loss: 1.8945 - val_accuracy: 0.3822\n" - ] - } - ], - "source": [ - "ann_history= ann_model.fit(train_ds,validation_data=val_ds,epochs=10)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 507 - }, - "id": "N1G8-LeCBU8d", - "outputId": "6d6717f7-e10d-4757-e619-71b63f3e2338" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_graph(ann_history)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "dlI2JELTUI5W" - }, - "source": [ - "# Basic CNN model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "WmkAduHdUI5W" - }, - "outputs": [], - "source": [ - "from keras.optimizers import Adam" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "O9R68g_lUI5W", - "outputId": "21d1cdb3-56ea-480d-e662-375c82003429" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"sequential_1\"\n", - "_________________________________________________________________\n", - " Layer (type) Output Shape Param # \n", - "=================================================================\n", - " conv2d (Conv2D) (None, 148, 148, 32) 896 \n", - " \n", - " batch_normalization (Batch (None, 148, 148, 32) 128 \n", - " Normalization) \n", - " \n", - " max_pooling2d (MaxPooling2 (None, 74, 74, 32) 0 \n", - " D) \n", - " \n", - " conv2d_1 (Conv2D) (None, 72, 72, 64) 18496 \n", - " \n", - " batch_normalization_1 (Bat (None, 72, 72, 64) 256 \n", - " chNormalization) \n", - " \n", - " conv2d_2 (Conv2D) (None, 70, 70, 64) 36928 \n", - " \n", - " batch_normalization_2 (Bat (None, 70, 70, 64) 256 \n", - " chNormalization) \n", - " \n", - " max_pooling2d_1 (MaxPoolin (None, 35, 35, 64) 0 \n", - " g2D) \n", - " \n", - " conv2d_3 (Conv2D) (None, 33, 33, 128) 73856 \n", - " \n", - " batch_normalization_3 (Bat (None, 33, 33, 128) 512 \n", - " chNormalization) \n", - " \n", - " max_pooling2d_2 (MaxPoolin (None, 16, 16, 128) 0 \n", - " g2D) \n", - " \n", - " conv2d_4 (Conv2D) (None, 14, 14, 128) 147584 \n", - " \n", - " batch_normalization_4 (Bat (None, 14, 14, 128) 512 \n", - " chNormalization) \n", - " \n", - " conv2d_5 (Conv2D) (None, 12, 12, 128) 147584 \n", - " \n", - " batch_normalization_5 (Bat (None, 12, 12, 128) 512 \n", - " chNormalization) \n", - " \n", - " max_pooling2d_3 (MaxPoolin (None, 6, 6, 128) 0 \n", - " g2D) \n", - " \n", - " flatten_1 (Flatten) (None, 4608) 0 \n", - " \n", - " dense_3 (Dense) (None, 1024) 4719616 \n", - " \n", - " batch_normalization_6 (Bat (None, 1024) 4096 \n", - " chNormalization) \n", - " \n", - " dropout (Dropout) (None, 1024) 0 \n", - " \n", - " dense_4 (Dense) (None, 22) 22550 \n", - " \n", - "=================================================================\n", - "Total params: 5173782 (19.74 MB)\n", - "Trainable params: 5170646 (19.72 MB)\n", - "Non-trainable params: 3136 (12.25 KB)\n", - "_________________________________________________________________\n" - ] - } - ], - "source": [ - "import tensorflow as tf\n", - "from tensorflow.keras import layers, models\n", - "\n", - "# Define the CNN model with batch normalization\n", - "cnn_model = models.Sequential([\n", - " layers.Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)),\n", - " layers.BatchNormalization(),\n", - " layers.MaxPooling2D((2, 2)),\n", - " layers.Conv2D(64, (3, 3), activation='relu'),\n", - " layers.BatchNormalization(),\n", - " layers.Conv2D(64, (3, 3), activation='relu'),\n", - " layers.BatchNormalization(),\n", - " layers.MaxPooling2D((2, 2)),\n", - " layers.Conv2D(128, (3, 3), activation='relu'),\n", - " layers.BatchNormalization(),\n", - " layers.MaxPooling2D((2, 2)),\n", - " layers.Conv2D(128, (3, 3), activation='relu'),\n", - " layers.BatchNormalization(),\n", - " layers.Conv2D(128, (3, 3), activation='relu'),\n", - " layers.BatchNormalization(),\n", - " layers.MaxPooling2D((2, 2)),\n", - " layers.Flatten(),\n", - " layers.Dense(1024, activation='relu'),\n", - " layers.BatchNormalization(),\n", - " layers.Dropout(0.75),\n", - " layers.Dense(22, activation='softmax')\n", - "])\n", - "\n", - "\n", - "# Compile the model\n", - "cnn_model.compile(optimizer='adam',\n", - " loss='categorical_crossentropy',\n", - " metrics=['accuracy'])\n", - "\n", - "# Display the model summary\n", - "cnn_model.summary()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "QjOA_sUCUI5W", - "outputId": "0b72dc7c-2df8-485f-8746-c999f31cb301" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/10\n", - "551/551 [==============================] - 46s 64ms/step - loss: 3.2072 - accuracy: 0.2810 - val_loss: 2.7506 - val_accuracy: 0.2116\n", - "Epoch 2/10\n", - "551/551 [==============================] - 35s 63ms/step - loss: 2.0666 - accuracy: 0.4187 - val_loss: 1.9861 - val_accuracy: 0.4283\n", - "Epoch 3/10\n", - "551/551 [==============================] - 36s 65ms/step - loss: 1.5801 - accuracy: 0.5168 - val_loss: 1.7026 - val_accuracy: 0.4803\n", - "Epoch 4/10\n", - "551/551 [==============================] - 37s 67ms/step - loss: 1.3267 - accuracy: 0.5757 - val_loss: 1.1292 - val_accuracy: 0.6340\n", - "Epoch 5/10\n", - "551/551 [==============================] - 38s 69ms/step - loss: 1.1433 - accuracy: 0.6254 - val_loss: 1.0762 - val_accuracy: 0.6466\n", - "Epoch 6/10\n", - "551/551 [==============================] - 35s 64ms/step - loss: 1.0291 - accuracy: 0.6573 - val_loss: 1.1473 - val_accuracy: 0.6399\n", - "Epoch 7/10\n", - "551/551 [==============================] - 35s 62ms/step - loss: 0.9183 - accuracy: 0.6904 - val_loss: 0.9613 - val_accuracy: 0.6848\n", - "Epoch 8/10\n", - "551/551 [==============================] - 36s 65ms/step - loss: 0.8195 - accuracy: 0.7247 - val_loss: 1.1618 - val_accuracy: 0.6584\n", - "Epoch 9/10\n", - "551/551 [==============================] - 36s 65ms/step - loss: 0.7272 - accuracy: 0.7481 - val_loss: 0.9426 - val_accuracy: 0.7104\n", - "Epoch 10/10\n", - "551/551 [==============================] - 36s 65ms/step - loss: 0.6606 - accuracy: 0.7719 - val_loss: 1.0112 - val_accuracy: 0.6899\n" - ] - } - ], - "source": [ - "cnn_history = cnn_model.fit(\n", - " train_ds,\n", - " validation_data=val_ds,\n", - "\n", - " epochs=10 # Adjust the number of epochs based on your requirements\n", - ")\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 507 - }, - "id": "09A0ihQVUI5X", - "outputId": "29a4f519-ff28-41f9-b2e6-6de784815841" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_graph(cnn_history)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "edlmW0YVUI5b" - }, - "source": [ - "# Resnet(Basic)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "id": "Ggk2xWRSsy-3" - }, - "outputs": [], - "source": [ - "import tensorflow as tf\n", - "from tensorflow.keras.applications import ResNet50\n", - "from tensorflow.keras.models import Model\n", - "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D , Dropout\n", - "from tensorflow.keras.optimizers import Adam\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "bYBGWOAPsL1k", - "outputId": "5ff7de24-5683-44dc-a959-e4b69a9b11eb" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n", - "94765736/94765736 [==============================] - 3s 0us/step\n" - ] - } - ], - "source": [ - "base_model = ResNet50(weights='imagenet', include_top=False)\n", - "\n", - "for layer in base_model.layers[-10:]:\n", - " layer.trainable = True\n", - "\n", - "# Add new top layers\n", - "x = base_model.output\n", - "x = GlobalAveragePooling2D()(x)\n", - "x = Dense(1024, activation='relu')(x)\n", - "predictions = Dense(22, activation='softmax')(x)\n", - "\n", - "res_model = Model(inputs=base_model.input, outputs=predictions)\n", - "\n", - "\n", - "# Re-compile the model\n", - "res_model.compile(optimizer=Adam(learning_rate=1e-5),\n", - " loss='categorical_crossentropy',\n", - " metrics=['accuracy'])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "6ANqeRP8tWV4", - "outputId": "5091ac68-6b19-4668-8584-3b046dd64b7e" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/10\n", - "551/551 [==============================] - 150s 201ms/step - loss: 1.3461 - accuracy: 0.6010 - val_loss: 3.0756 - val_accuracy: 0.0867\n", - "Epoch 2/10\n", - "551/551 [==============================] - 104s 188ms/step - loss: 0.5116 - accuracy: 0.8258 - val_loss: 0.5763 - val_accuracy: 0.8042\n", - "Epoch 3/10\n", - "551/551 [==============================] - 110s 200ms/step - loss: 0.3162 - accuracy: 0.8965 - val_loss: 0.4449 - val_accuracy: 0.8479\n", - "Epoch 4/10\n", - "551/551 [==============================] - 104s 189ms/step - loss: 0.2023 - accuracy: 0.9386 - val_loss: 0.4379 - val_accuracy: 0.8522\n", - "Epoch 5/10\n", - "551/551 [==============================] - 104s 187ms/step - loss: 0.1295 - accuracy: 0.9632 - val_loss: 0.4506 - val_accuracy: 0.8542\n", - "Epoch 6/10\n", - "551/551 [==============================] - 104s 187ms/step - loss: 0.0870 - accuracy: 0.9776 - val_loss: 0.4571 - val_accuracy: 0.8601\n", - "Epoch 7/10\n", - "551/551 [==============================] - 104s 188ms/step - loss: 0.0579 - accuracy: 0.9848 - val_loss: 0.4730 - val_accuracy: 0.8562\n", - "Epoch 8/10\n", - "551/551 [==============================] - 104s 189ms/step - loss: 0.0477 - accuracy: 0.9855 - val_loss: 0.5126 - val_accuracy: 0.8617\n", - "Epoch 9/10\n", - "551/551 [==============================] - 104s 188ms/step - loss: 0.0387 - accuracy: 0.9878 - val_loss: 0.5597 - val_accuracy: 0.8491\n", - "Epoch 10/10\n", - "551/551 [==============================] - 104s 188ms/step - loss: 0.0378 - accuracy: 0.9864 - val_loss: 0.5381 - val_accuracy: 0.8495\n" - ] - } - ], - "source": [ - "resnet_history= res_model.fit(train_ds,validation_data=val_ds,epochs=10)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 468 - }, - "id": "7QLWlY1dvfUU", - "outputId": "5d0f74ca-f30f-47c7-db63-ec68d0c1de13" - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_graph(resnet_history)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WGcdJoLHvjxd" - }, - "source": [ - "# ResNet(Perfect) with Data Augumentaion and Regularizations" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "6kfaFjzVemsj", - "outputId": "6cff68f7-0fe9-460a-955f-97608240456a" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 17605 images belonging to 22 classes.\n", - "Found 2538 images belonging to 22 classes.\n" - ] - } - ], - "source": [ - "# Data augmentaion\n", - "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", - "\n", - "datagen = ImageDataGenerator(\n", - " rotation_range=20,\n", - " width_shift_range=0.2,\n", - " height_shift_range=0.2,\n", - " shear_range=0.2,\n", - " zoom_range=0.2,\n", - " horizontal_flip=True,\n", - " fill_mode='nearest'\n", - ")\n", - "\n", - "train_generator = datagen.flow_from_directory(\n", - " 'filtered_train',\n", - " target_size=(150,150),\n", - " batch_size=32,\n", - " class_mode='categorical'\n", - ")\n", - "\n", - "val_generator = datagen.flow_from_directory(\n", - " 'filtered_val',\n", - " target_size=(150, 150),\n", - " batch_size=32,\n", - " class_mode='categorical'\n", - ")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "id": "AsmRa-MUfsCs" - }, - "outputs": [], - "source": [ - "from tensorflow.keras.applications import ResNet50\n", - "from tensorflow.keras.layers import GlobalAveragePooling2D, Dense, Dropout\n", - "from tensorflow.keras.models import Model\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.regularizers import l2\n", - "from tensorflow.keras.callbacks import EarlyStopping\n" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "h7_q9fbffbgd", - "outputId": "065ed344-07fa-4bfe-de27-62018da4e819" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/5\n", - "551/551 [==============================] - 218s 342ms/step - loss: 16.7730 - accuracy: 0.2183 - val_loss: 15.0907 - val_accuracy: 0.5839\n", - "Epoch 2/5\n", - "551/551 [==============================] - 187s 339ms/step - loss: 15.0545 - accuracy: 0.5087 - val_loss: 14.0818 - val_accuracy: 0.7206\n", - "Epoch 3/5\n", - "551/551 [==============================] - 187s 339ms/step - loss: 14.0944 - accuracy: 0.6330 - val_loss: 13.3983 - val_accuracy: 0.7545\n", - "Epoch 4/5\n", - "551/551 [==============================] - 189s 343ms/step - loss: 13.3619 - accuracy: 0.6914 - val_loss: 12.7492 - val_accuracy: 0.7888\n", - "Epoch 5/5\n", - "551/551 [==============================] - 192s 349ms/step - loss: 12.6688 - accuracy: 0.7296 - val_loss: 12.1332 - val_accuracy: 0.8081\n" - ] - } - ], - "source": [ - "# Load the base model\n", - "base_model = ResNet50(weights='imagenet', include_top=False)\n", - "\n", - "# Make the last 10 layers trainable\n", - "for layer in base_model.layers[-10:]:\n", - " layer.trainable = True\n", - "\n", - "# Add new top layers\n", - "x = base_model.output\n", - "x = GlobalAveragePooling2D()(x)\n", - "x = Dropout(0.5)(x) # Adding dropout for regularization\n", - "x = Dense(1024, activation='relu', kernel_regularizer=l2(0.01))(x)\n", - "x = Dropout(0.5)(x) # Adding another dropout layer for regularization\n", - "predictions = Dense(22, activation='softmax', kernel_regularizer=l2(0.01))(x)\n", - "\n", - "res_model_perfect = Model(inputs=base_model.input, outputs=predictions)\n", - "\n", - "# Re-compile the model\n", - "res_model_perfect.compile(optimizer=Adam(learning_rate=1e-5),\n", - " loss='categorical_crossentropy',\n", - " metrics=['accuracy'])\n", - "\n", - "# Early stopping callback\n", - "early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n", - "\n", - "# Train the model\n", - "res_model_perfect_history = res_model_perfect.fit(train_generator, epochs=5, validation_data=val_generator, callbacks=[early_stopping])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "rottw-JQjOq4", - "outputId": "275497e0-d72c-400c-d5e7-b777665d7d68" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 6/10\n", - "551/551 [==============================] - 189s 344ms/step - loss: 11.9784 - accuracy: 0.7541 - val_loss: 11.4686 - val_accuracy: 0.8282\n", - "Epoch 7/10\n", - "551/551 [==============================] - 189s 342ms/step - loss: 11.3271 - accuracy: 0.7718 - val_loss: 10.8296 - val_accuracy: 0.8294\n", - "Epoch 8/10\n", - "551/551 [==============================] - 191s 347ms/step - loss: 10.6584 - accuracy: 0.7876 - val_loss: 10.2008 - val_accuracy: 0.8373\n", - "Epoch 9/10\n", - "551/551 [==============================] - 192s 347ms/step - loss: 10.0059 - accuracy: 0.8040 - val_loss: 9.6002 - val_accuracy: 0.8408\n", - "Epoch 10/10\n", - "551/551 [==============================] - 194s 352ms/step - loss: 9.3686 - accuracy: 0.8173 - val_loss: 8.9783 - val_accuracy: 0.8455\n" - ] - } - ], - "source": [ - "res_model_perfect_history1 = res_model_perfect.fit(train_generator, initial_epoch=5,epochs=10, validation_data=val_generator, callbacks=[early_stopping])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "lhkIRX0oEpTj" - }, - "source": [ - "# VGG16 Model" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "id": "v93hTELS5Jzl" - }, - "outputs": [], - "source": [ - "from tensorflow.keras.applications import VGG16\n", - "from tensorflow.keras.layers import GlobalAveragePooling2D, Dense, Dropout\n", - "from tensorflow.keras.models import Model\n", - "from tensorflow.keras.optimizers import SGD\n", - "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "TbOkFGjzEw5p", - "outputId": "d30519f5-efa9-465c-b5bb-f43e9f81e484" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n", - "58889256/58889256 [==============================] - 2s 0us/step\n", - "Model: \"model_2\"\n", - "_________________________________________________________________\n", - " Layer (type) Output Shape Param # \n", - "=================================================================\n", - " input_3 (InputLayer) [(None, None, None, 3)] 0 \n", - " \n", - " block1_conv1 (Conv2D) (None, None, None, 64) 1792 \n", - " \n", - " block1_conv2 (Conv2D) (None, None, None, 64) 36928 \n", - " \n", - " block1_pool (MaxPooling2D) (None, None, None, 64) 0 \n", - " \n", - " block2_conv1 (Conv2D) (None, None, None, 128) 73856 \n", - " \n", - " block2_conv2 (Conv2D) (None, None, None, 128) 147584 \n", - " \n", - " block2_pool (MaxPooling2D) (None, None, None, 128) 0 \n", - " \n", - " block3_conv1 (Conv2D) (None, None, None, 256) 295168 \n", - " \n", - " block3_conv2 (Conv2D) (None, None, None, 256) 590080 \n", - " \n", - " block3_conv3 (Conv2D) (None, None, None, 256) 590080 \n", - " \n", - " block3_pool (MaxPooling2D) (None, None, None, 256) 0 \n", - " \n", - " block4_conv1 (Conv2D) (None, None, None, 512) 1180160 \n", - " \n", - " block4_conv2 (Conv2D) (None, None, None, 512) 2359808 \n", - " \n", - " block4_conv3 (Conv2D) (None, None, None, 512) 2359808 \n", - " \n", - " block4_pool (MaxPooling2D) (None, None, None, 512) 0 \n", - " \n", - " block5_conv1 (Conv2D) (None, None, None, 512) 2359808 \n", - " \n", - " block5_conv2 (Conv2D) (None, None, None, 512) 2359808 \n", - " \n", - " block5_conv3 (Conv2D) (None, None, None, 512) 2359808 \n", - " \n", - " block5_pool (MaxPooling2D) (None, None, None, 512) 0 \n", - " \n", - " global_average_pooling2d_2 (None, 512) 0 \n", - " (GlobalAveragePooling2D) \n", - " \n", - " dense_4 (Dense) (None, 1024) 525312 \n", - " \n", - " dense_5 (Dense) (None, 22) 22550 \n", - " \n", - "=================================================================\n", - "Total params: 15262550 (58.22 MB)\n", - "Trainable params: 15262550 (58.22 MB)\n", - "Non-trainable params: 0 (0.00 Byte)\n", - "_________________________________________________________________\n" - ] - } - ], - "source": [ - "\n", - "# Load the base model\n", - "base_model = VGG16(weights='imagenet', include_top=False)\n", - "\n", - "# Make more layers trainable\n", - "for layer in base_model.layers[-10:]:\n", - " layer.trainable = True\n", - "\n", - "# Add new top layers\n", - "x = base_model.output\n", - "x = GlobalAveragePooling2D()(x)\n", - "x = Dense(1024, activation='relu')(x)\n", - "predictions = Dense(22, activation='softmax')(x)\n", - "\n", - "# Create the final model\n", - "vgg_model = Model(inputs=base_model.input, outputs=predictions)\n", - "\n", - "vgg_model.compile(optimizer=Adam(learning_rate=1e-5),\n", - " loss='categorical_crossentropy',\n", - " metrics=['accuracy'])\n", - "\n", - "vgg_model.summary()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Nc8DXxH8MvnG", - "outputId": "babb6905-0dab-4fc2-d615-6e08898524ca" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/10\n", - "551/551 [==============================] - 148s 239ms/step - loss: 1.2364 - accuracy: 0.5985 - val_loss: 0.7437 - val_accuracy: 0.7360\n", - "Epoch 2/10\n", - "551/551 [==============================] - 123s 223ms/step - loss: 0.5993 - accuracy: 0.7848 - val_loss: 0.6208 - val_accuracy: 0.7683\n", - "Epoch 3/10\n", - "551/551 [==============================] - 128s 232ms/step - loss: 0.4672 - accuracy: 0.8295 - val_loss: 0.5241 - val_accuracy: 0.8081\n", - "Epoch 4/10\n", - "551/551 [==============================] - 128s 232ms/step - loss: 0.3806 - accuracy: 0.8620 - val_loss: 0.4916 - val_accuracy: 0.8140\n", - "Epoch 5/10\n", - "551/551 [==============================] - 124s 224ms/step - loss: 0.3119 - accuracy: 0.8891 - val_loss: 0.5329 - val_accuracy: 0.8069\n", - "Epoch 6/10\n", - "551/551 [==============================] - 124s 224ms/step - loss: 0.2599 - accuracy: 0.9063 - val_loss: 0.5281 - val_accuracy: 0.8128\n", - "Epoch 7/10\n", - "551/551 [==============================] - 123s 223ms/step - loss: 0.2171 - accuracy: 0.9225 - val_loss: 0.5258 - val_accuracy: 0.8262\n", - "Epoch 8/10\n", - "551/551 [==============================] - 128s 232ms/step - loss: 0.1756 - accuracy: 0.9380 - val_loss: 0.5114 - val_accuracy: 0.8353\n", - "Epoch 9/10\n", - "551/551 [==============================] - 123s 223ms/step - loss: 0.1501 - accuracy: 0.9474 - val_loss: 0.5555 - val_accuracy: 0.8286\n", - "Epoch 10/10\n", - "551/551 [==============================] - 128s 232ms/step - loss: 0.1284 - accuracy: 0.9558 - val_loss: 0.5891 - val_accuracy: 0.8270\n" - ] - } - ], - "source": [ - "vgg_history= vgg_model.fit(train_ds,epochs=10,validation_data=val_ds)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 468 - }, - "id": "5E-06ul8He9M", - "outputId": "210c701f-518b-4253-86ec-f5754d5798eb" - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_graph(vgg_history)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "RcmcV-PLnCw_" - }, - "source": [ - "# Model Evaluation and Best model Testing" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "P6m-twEGm_AH", - "outputId": "9ecddb0d-3dc8-4cc5-ac29-4f7e77b5aeff" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "157/157 [==============================] - 7s 38ms/step - loss: 1.8495 - accuracy: 0.3898\n", - "ANN Test accuracy: 38.98%\n" - ] - } - ], - "source": [ - "loss_ann, accuracy_ann = ann_model.evaluate(test_ds)\n", - "print(f\"ANN Test accuracy: {accuracy_ann * 100:.2f}%\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "sAs7JCS1MQgL", - "outputId": "19d046a2-6e95-4542-8f60-3aa69acd6ef7" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "157/157 [==============================] - 9s 53ms/step - loss: 0.9678 - accuracy: 0.6992\n", - "CNN Test accuracy: 69.92%\n" - ] - } - ], - "source": [ - "loss_cnn, accuracy_cnn = cnn_model.evaluate(test_ds)\n", - "print(f\"CNN Test accuracy: {accuracy_cnn * 100:.2f}%\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "K2zPnXNaMQP6", - "outputId": "ba65fa34-0993-443c-e39f-71bd03be6a0e" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "158/158 [==============================] - 9s 57ms/step - loss: 0.5548 - accuracy: 0.8562\n", - "ResNet Test accuracy: 85.62%\n" - ] - } - ], - "source": [ - "loss_res, accuracy_res = res_model.evaluate(test_ds)\n", - "print(f\"ResNet Test accuracy: {accuracy_res * 100:.2f}%\")\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "vkawaMLVMP6d", - "outputId": "46a0d654-b34a-47b9-bf61-819e60e6f08e" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "158/158 [==============================] - 11s 70ms/step - loss: 0.5903 - accuracy: 0.8303\n", - "VGG16 Test accuracy: 83.03%\n" - ] - } - ], - "source": [ - "\n", - "loss_vgg16, accuracy_vgg16 = vgg_model.evaluate(test_ds)\n", - "print(f\"VGG16 Test accuracy: {accuracy_vgg16 * 100:.2f}%\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "PLRg-hzH3xCv", - "outputId": "bd18867b-1445-40f0-d001-b63d62339591" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 5027 images belonging to 22 classes.\n" - ] - } - ], - "source": [ - "# Preparing test data for Renset Perfect model\n", - "test_generator = datagen.flow_from_directory(\n", - " 'filtered_test',\n", - " target_size=(150, 150),\n", - " batch_size=32,\n", - " class_mode='categorical'\n", - ")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "9z53Loe7uKVH", - "outputId": "63799035-a5cb-4279-89f5-69188aefb10e" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "158/158 [==============================] - 47s 299ms/step - loss: 8.9857 - accuracy: 0.8472\n", - "ResNet(Perfect) Test accuracy: 84.72%\n" - ] - } - ], - "source": [ - "loss_res_model_perfect, accuracy_res_model_perfect = res_model_perfect.evaluate(test_generator)\n", - "print(f\"ResNet(Perfect) Test accuracy: {accuracy_res_model_perfect * 100:.2f}%\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "swE1qjVO30EQ" - }, - "source": [ - "# Tabulating the Model and it's Accuracy" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "wG_5qUPadj7N" - }, - "source": [ - " | Model Name | Accuracy (%) |\n", - " |-----------------|--------------|\n", - " | ANN | 38.98 |\n", - " | CNN | 69.92 |\n", - " | VGG16 | 83.03 |\n", - " | ResNet | 85.62 |\n", - " | ResNet(Perfect) | 84.58 |" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "2F0_YBHIc1zt" - }, - "source": [ - "- The ResNet(Perfect) model is chosen as the optimal model due to its balance of high accuracy and reduced overfitting, making it the best candidate for deployment. While the standard ResNet model achieved the highest accuracy, its overfitting issues could compromise its performance on new, unseen data. Therefore, for practical applications where generalization is crucial, the ResNet(Perfect) model is the preferred choice\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "_un1ZG_XDvzr" - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "gpuType": "T4", - "provenance": [] - }, - "kaggle": { - "accelerator": "none", - "dataSources": [ - { - "datasetId": 4280832, - "sourceId": 7368427, - "sourceType": "datasetVersion" - } - ], - "dockerImageVersionId": 30699, - "isGpuEnabled": false, - "isInternetEnabled": true, - "language": "python", - "sourceType": "notebook" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.4" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} From e9302370e53dd8942c5e499d3c3d9aa05536a247 Mon Sep 17 00:00:00 2001 From: Karthik110505 Date: Wed, 22 May 2024 21:57:20 +0530 Subject: [PATCH 4/6] Revert "Last_minute Changes" This reverts commit b2678e569e9a3840bd56645b2619a95b087cb864. --- ..._Pest_and_Disease_Detection_using_DL.ipynb | 13203 ++++++++++++++++ 1 file changed, 13203 insertions(+) create mode 100644 Crop Pest and Disease Detection using DL/Model/Crop_Pest_and_Disease_Detection_using_DL.ipynb diff --git a/Crop Pest and Disease Detection using DL/Model/Crop_Pest_and_Disease_Detection_using_DL.ipynb b/Crop Pest and Disease Detection using DL/Model/Crop_Pest_and_Disease_Detection_using_DL.ipynb new file mode 100644 index 000000000..04cafd22c --- /dev/null +++ b/Crop Pest and Disease Detection using DL/Model/Crop_Pest_and_Disease_Detection_using_DL.ipynb @@ -0,0 +1,13203 @@ +{ +<<<<<<< HEAD:Crop Pest and Disease Detection using DL/Model/Crop_Pest_and_Disease_Detection_using_DL_.ipynb + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Note: This notebook is prepared in Google collab" + ] +======= + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Note: This notebook is prepared in Google collab" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IRYcu-UwzQAU" + }, + "source": [ + "# Loading Basic Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "_ryJq3BVUI5F" + }, + "outputs": [], + "source": [ + "import os\n", + "import cv2\n", + "import tensorflow as tf\n", + "from tensorflow.keras import layers, models\n", + "import matplotlib.pyplot as plt\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "Mukt0YWFjy8U" + }, + "outputs": [], + "source": [ + "# Creating dataset folder to download the dataset\n", + "mkdir dataset\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "OOTLURUXlxxL", + "outputId": "a34cf209-2f49-4270-978f-d51147037fe5" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/content/dataset\n" + ] + } + ], + "source": [ + "# changing current directory path\n", + "cd dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QieK7HFzidPC", + "outputId": "3c9a5312-d30e-4950-eb6d-1913c3f56a79" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.6.14 / client 1.6.12)\n", + "Dataset URL: https://www.kaggle.com/datasets/nirmalsankalana/crop-pest-and-disease-detection\n", + "License(s): CC0-1.0\n", + "Downloading crop-pest-and-disease-detection.zip to /content/dataset\n", + "100% 1.24G/1.25G [00:46<00:00, 31.4MB/s]\n", + "100% 1.25G/1.25G [00:46<00:00, 29.0MB/s]\n" + ] + } + ], + "source": [ + "# Downloading the dataset\n", + "!kaggle datasets download -d nirmalsankalana/crop-pest-and-disease-detection" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "collapsed": true, + "id": "FhJ3tB5mjVe0", + "outputId": "8b6fff35-753b-441f-ecc9-4095b21eed32" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n", + " inflating: Tomato leaf blight/leaf blight1302_.jpg \n", + " inflating: Tomato leaf blight/leaf blight1303_.jpg \n", + " inflating: Tomato leaf blight/leaf blight1304_.jpg \n", + " inflating: Tomato leaf blight/leaf blight1305_.jpg \n", + " inflating: Tomato leaf blight/leaf blight1306_.jpg \n", + " inflating: Tomato leaf blight/leaf blight1307_.jpg \n", + " inflating: Tomato leaf 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+ " inflating: Tomato verticulium wilt/verticulium wilt746_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt747_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt748_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt749_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt74_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt750_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt751_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt752_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt753_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt754_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt755_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt756_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt757_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt758_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt759_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt75_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt760_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt76_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt77_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt78_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt79_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt7_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt80_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt81_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt82_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt83_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt84_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt85_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt86_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt87_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt88_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt89_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt8_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt90_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt91_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt92_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt93_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt94_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt95_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt96_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt97_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt98_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt99_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt9_.jpg \n" + ] + } + ], + "source": [ + "# Unzipping the dataset\n", + "!unzip \\*.zip\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4V88wjJT2_30" + }, + "outputs": [], + "source": [ + "# Navigating back to content directory\n", + "cd '../'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WlsvA2X-UI5K" + }, + "source": [ + "# Some predifined functions" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "27ZBrqgWUI5M" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "def plot_graph(history):\n", + " fig, ax = plt.subplots(1, 2, figsize=(12, 5)) # Create a figure with 1 row and 2 columns\n", + "\n", + " # Plot training & validation accuracy values\n", + " ax[0].plot(history.history['accuracy'])\n", + " ax[0].plot(history.history['val_accuracy'])\n", + " ax[0].set_title('Model accuracy')\n", + " ax[0].set_ylabel('Accuracy')\n", + " ax[0].set_xlabel('Epoch')\n", + " ax[0].legend(['Train', 'Validation'], loc='upper left')\n", + "\n", + " # Plot training & validation loss values\n", + " ax[1].plot(history.history['loss'])\n", + " ax[1].plot(history.history['val_loss'])\n", + " ax[1].set_title('Model loss')\n", + " ax[1].set_ylabel('Loss')\n", + " ax[1].set_xlabel('Epoch')\n", + " ax[1].legend(['Train', 'Validation'], loc='upper left')\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LOwqeXYpUI5N" + }, + "source": [ + "# Data Cleaning and filter valid image data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Lc9naWsczqaA" + }, + "source": [ + "The given dataset contains some invalid images. The below code filters the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "collapsed": true, + "id": "yj3Dw9mWUI5O", + "outputId": "ad848410-2b0e-4988-a53c-458d917fdfae" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 392 image files in dataset/Cashew gumosis\n", + "Copied 274 images to train/Cashew gumosis\n", + "Copied 78 images to test/Cashew gumosis\n", + "Copied 40 images to val/Cashew gumosis\n", + "Found 673 image files in dataset/Maize grasshoper\n", + "Copied 471 images to train/Maize grasshoper\n", + "Copied 134 images to test/Maize grasshoper\n", + "Copied 68 images to val/Maize grasshoper\n", + "Found 1368 image files in dataset/Cashew healthy\n", + "Copied 957 images to train/Cashew healthy\n", + "Copied 273 images to test/Cashew healthy\n", + "Copied 138 images to val/Cashew healthy\n", + "Found 1015 image files in dataset/Cassava green mite\n", + "Copied 710 images to train/Cassava green mite\n", + "Copied 203 images to test/Cassava green mite\n", + "Copied 102 images to val/Cassava green mite\n", + "Found 948 image files in dataset/Maize leaf beetle\n", + "Copied 663 images to train/Maize leaf beetle\n", + "Copied 189 images to test/Maize leaf beetle\n", + "Copied 96 images to val/Maize leaf beetle\n", + "Found 1205 image files in dataset/Cassava mosaic\n", + "Copied 843 images to train/Cassava mosaic\n", + "Copied 241 images to test/Cassava mosaic\n", + "Copied 121 images to val/Cassava mosaic\n", + "Found 1006 image files in dataset/Maize leaf blight\n", + "Copied 704 images to train/Maize leaf blight\n", + "Copied 201 images to test/Maize leaf blight\n", + "Copied 101 images to val/Maize leaf blight\n", + "Found 979 image files in dataset/Maize streak virus\n", + "Copied 685 images to train/Maize streak virus\n", + "Copied 195 images to test/Maize streak virus\n", + "Copied 99 images to val/Maize streak virus\n", + "Found 1259 image files in dataset/Maize leaf spot\n", + "Copied 881 images to train/Maize leaf spot\n", + "Copied 251 images to test/Maize leaf spot\n", + "Copied 127 images to val/Maize leaf spot\n", + "Found 285 image files in dataset/Maize fall armyworm\n", + "Copied 199 images to train/Maize fall armyworm\n", + "Copied 57 images to test/Maize fall armyworm\n", + "Copied 29 images to val/Maize fall armyworm\n", + "Found 470 image files in dataset/Tomato healthy\n", + "Copied 329 images to train/Tomato healthy\n", + "Copied 94 images to test/Tomato healthy\n", + "Copied 47 images to val/Tomato healthy\n", + "Found 1193 image files in dataset/Cassava healthy\n", + "Copied 835 images to train/Cassava healthy\n", + "Copied 238 images to test/Cassava healthy\n", + "Copied 120 images to val/Cassava healthy\n", + "Found 518 image files in dataset/Tomato leaf curl\n", + "Copied 362 images to train/Tomato leaf curl\n", + "Copied 103 images to test/Tomato leaf curl\n", + "Copied 53 images to val/Tomato leaf curl\n", + "Found 1301 image files in dataset/Tomato leaf blight\n", + "Copied 910 images to train/Tomato leaf blight\n", + "Copied 260 images to test/Tomato leaf blight\n", + "Copied 131 images to val/Tomato leaf blight\n", + "Found 1682 image files in dataset/Cashew red rust\n", + "Copied 1177 images to train/Cashew red rust\n", + "Copied 336 images to test/Cashew red rust\n", + "Copied 169 images to val/Cashew red rust\n", + "Found 1481 image files in dataset/Cassava brown spot\n", + "Copied 1036 images to train/Cassava brown spot\n", + "Copied 296 images to test/Cassava brown spot\n", + "Copied 149 images to val/Cassava brown spot\n", + "Found 773 image files in dataset/Tomato verticulium wilt\n", + "Copied 541 images to train/Tomato verticulium wilt\n", + "Copied 154 images to test/Tomato verticulium wilt\n", + "Copied 78 images to val/Tomato verticulium wilt\n", + "Found 1729 image files in dataset/Cashew anthracnose\n", + "Copied 1210 images to train/Cashew anthracnose\n", + "Copied 345 images to test/Cashew anthracnose\n", + "Copied 174 images to val/Cashew anthracnose\n", + "Found 2743 image files in dataset/Tomato septoria leaf spot\n", + "Copied 1920 images to train/Tomato septoria leaf spot\n", + "Copied 548 images to test/Tomato septoria leaf spot\n", + "Copied 275 images to val/Tomato septoria leaf spot\n", + "Found 1378 image files in dataset/Cashew leaf miner\n", + "Copied 964 images to train/Cashew leaf miner\n", + "Copied 275 images to test/Cashew leaf miner\n", + "Copied 139 images to val/Cashew leaf miner\n", + "Found 208 image files in dataset/Maize healthy\n", + "Copied 145 images to train/Maize healthy\n", + "Copied 41 images to test/Maize healthy\n", + "Copied 22 images to val/Maize healthy\n", + "Found 2614 image files in dataset/Cassava bacterial blight\n", + "Copied 1829 images to train/Cassava bacterial blight\n", + "Copied 522 images to test/Cassava bacterial blight\n", + "Copied 263 images to val/Cassava bacterial blight\n" + ] + } + ], + "source": [ + "import random\n", + "import shutil\n", + "\n", + "# Set the source directory\n", + "src_dir = 'dataset'\n", + "\n", + "# Set the destination directories\n", + "train_dir = 'train'\n", + "test_dir = 'test'\n", + "val_dir = 'val'\n", + "\n", + "# Set the split ratios\n", + "train_ratio = 0.7\n", + "test_ratio = 0.2\n", + "val_ratio = 0.1\n", + "\n", + "# Create the destination directories if they don't exist\n", + "os.makedirs(train_dir, exist_ok=True)\n", + "os.makedirs(test_dir, exist_ok=True)\n", + "os.makedirs(val_dir, exist_ok=True)\n", + "\n", + "# Get a list of all subdirectories in the source directory\n", + "subdirs = [d for d in os.listdir(src_dir) if os.path.isdir(os.path.join(src_dir, d))]\n", + "\n", + "for subdir in subdirs:\n", + " subdir_path = os.path.join(src_dir, subdir)\n", + " image_files = [f for f in os.listdir(subdir_path) if f.endswith('.jpg') or f.endswith('.png')]\n", + "\n", + " print(f\"Found {len(image_files)} image files in {subdir_path}\")\n", + "\n", + " # If no image files found in the subdirectory, skip it\n", + " if not image_files:\n", + " print(f\"No image files found in {subdir_path}. Skipping...\")\n", + " continue\n", + "\n", + " # Shuffle the list of image files\n", + " random.shuffle(image_files)\n", + "\n", + " # Calculate the number of images for each set\n", + " num_images = len(image_files)\n", + " num_train = int(num_images * train_ratio)\n", + " num_test = int(num_images * test_ratio)\n", + " num_val = num_images - num_train - num_test\n", + "\n", + " # Create subdirectories for train, test, and val sets\n", + " train_subdir = os.path.join(train_dir, subdir)\n", + " test_subdir = os.path.join(test_dir, subdir)\n", + " val_subdir = os.path.join(val_dir, subdir)\n", + "\n", + " os.makedirs(train_subdir, exist_ok=True)\n", + " os.makedirs(test_subdir, exist_ok=True)\n", + " os.makedirs(val_subdir, exist_ok=True)\n", + "\n", + " # Copy the images to their respective sets\n", + " train_files = image_files[:num_train]\n", + " test_files = image_files[num_train:num_train+num_test]\n", + " val_files = image_files[num_train+num_test:]\n", + "\n", + " for file in train_files:\n", + " src_path = os.path.join(subdir_path, file)\n", + " dst_path = os.path.join(train_subdir, file)\n", + " shutil.copy(src_path, dst_path)\n", + "\n", + " for file in test_files:\n", + " src_path = os.path.join(subdir_path, file)\n", + " dst_path = os.path.join(test_subdir, file)\n", + " shutil.copy(src_path, dst_path)\n", + "\n", + " for file in val_files:\n", + " src_path = os.path.join(subdir_path, file)\n", + " dst_path = os.path.join(val_subdir, file)\n", + " shutil.copy(src_path, dst_path)\n", + "\n", + " print(f\"Copied {num_train} images to {train_subdir}\")\n", + " print(f\"Copied {num_test} images to {test_subdir}\")\n", + " print(f\"Copied {num_val} images to {val_subdir}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true, + "id": "KwzUAq8xUI5Q" + }, + "outputs": [], + "source": [ + "# Function to check if an image is valid using OpenCV\n", + "def is_image_valid(image_path):\n", + " try:\n", + " img = cv2.imread(image_path)\n", + " if img is None:\n", + " return False\n", + " return True\n", + " except:\n", + " return False\n", + "\n", + "# Function to filter out corrupted images\n", + "def filter_and_save_valid_images(source_dir, target_dir):\n", + " if not os.path.exists(target_dir):\n", + " os.makedirs(target_dir)\n", + "\n", + " for root, _, files in os.walk(source_dir):\n", + " for file in files:\n", + " file_path = os.path.join(root, file)\n", + " if is_image_valid(file_path):\n", + " relative_path = os.path.relpath(file_path, source_dir)\n", + " target_path = os.path.join(target_dir, relative_path)\n", + " target_subdir = os.path.dirname(target_path)\n", + " if not os.path.exists(target_subdir):\n", + " os.makedirs(target_subdir)\n", + " cv2.imwrite(target_path, cv2.imread(file_path))\n", + "\n", + "# Specify the source and target directories\n", + "train_source_dir = 'train'\n", + "train_target_dir = 'filtered_train'\n", + "val_source_dir = 'val'\n", + "val_target_dir = 'filtered_val'\n", + "test_source_dir = 'test'\n", + "test_target_dir = 'filtered_test'\n", + "\n", + "# Filter and save valid images\n", + "filter_and_save_valid_images(train_source_dir, train_target_dir)\n", + "filter_and_save_valid_images(val_source_dir, val_target_dir)\n", + "filter_and_save_valid_images(test_source_dir, test_target_dir)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aKljc92wz7Es" + }, + "source": [ + "# Loading the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kIbRzkJ5UI5R", + "outputId": "a0820fa1-f15a-4f26-a72e-6c8350757de6" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 17605 files belonging to 22 classes.\n", + "Found 2538 files belonging to 22 classes.\n" + ] + } + ], + "source": [ + "\n", + "# Function to load and preprocess the dataset\n", + "def load_and_preprocess_dataset(directory, image_size=(150, 150), batch_size=32):\n", + " dataset = tf.keras.utils.image_dataset_from_directory(\n", + " directory=directory,\n", + " labels='inferred',\n", + " label_mode='categorical',\n", + " batch_size=batch_size,\n", + " image_size=image_size)\n", + "\n", + " class_names = dataset.class_names\n", + "\n", + " # Define a preprocessing function to scale the images\n", + " def preprocess(image, label):\n", + " # Rescale the image pixel values from [0, 255] to [0, 1]\n", + " image = tf.cast(image, tf.float32) / 255.0\n", + " return image, label\n", + "\n", + " # Apply the preprocessing function to the dataset\n", + " dataset = dataset.map(preprocess, num_parallel_calls=tf.data.AUTOTUNE)\n", + " return dataset, class_names\n", + "\n", + "# Load the datasets\n", + "train_ds, train_class_names = load_and_preprocess_dataset(train_target_dir)\n", + "val_ds, val_class_names = load_and_preprocess_dataset(val_target_dir)\n", + "\n", + "# Ensure the class names are consistent\n", + "assert train_class_names == val_class_names, \"Class names in training and validation datasets do not match.\"\n", + "\n", + "# Prefetching to improve performance\n", + "train_ds = train_ds.prefetch(buffer_size=tf.data.AUTOTUNE)\n", + "val_ds = val_ds.prefetch(buffer_size=tf.data.AUTOTUNE)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3SStCzWbUI5S", + "outputId": "32ff7f88-d132-427a-d79a-40ae71c41ea9" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 5027 files belonging to 22 classes.\n" + ] + } + ], + "source": [ + "test_ds, test_class_names = load_and_preprocess_dataset(test_target_dir)\n", + "test_ds = test_ds.prefetch(buffer_size=tf.data.AUTOTUNE)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GOC_HjrYUI5U" + }, + "source": [ + "# ANN model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MvkyNR5CUI5V", + "outputId": "d44f1c8f-194e-40b6-ccd6-df8c1f68712c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " flatten (Flatten) (None, 67500) 0 \n", + " \n", + " dense (Dense) (None, 512) 34560512 \n", + " \n", + " dense_1 (Dense) (None, 256) 131328 \n", + " \n", + " dense_2 (Dense) (None, 22) 5654 \n", + " \n", + "=================================================================\n", + "Total params: 34697494 (132.36 MB)\n", + "Trainable params: 34697494 (132.36 MB)\n", + "Non-trainable params: 0 (0.00 Byte)\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "from tensorflow.keras import layers, models\n", + "ann_model = models.Sequential([\n", + " layers.Flatten(input_shape=(150,150,3)), # Input layer flattens the image\n", + " layers.Dense(512, activation='relu'), # Hidden layer with 512 units and ReLU activation\n", + " layers.Dense(256, activation='relu'), # Hidden layer with 256 units and ReLU activation\n", + " layers.Dense(22, activation='softmax') # Output layer with softmax activation for classification\n", + "])\n", + "\n", + "# Compile the model\n", + "ann_model.compile(optimizer='adam',\n", + " loss='categorical_crossentropy',\n", + " metrics=['accuracy'])\n", + "\n", + "ann_model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "cWbWa40UUI5V", + "outputId": "f5c99b23-20d6-4298-e480-a68e572dad6c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "551/551 [==============================] - 32s 56ms/step - loss: 2.5090 - accuracy: 0.2331 - val_loss: 2.4573 - val_accuracy: 0.2368\n", + "Epoch 2/10\n", + "551/551 [==============================] - 35s 62ms/step - loss: 2.2748 - accuracy: 0.2769 - val_loss: 2.1671 - val_accuracy: 0.3073\n", + "Epoch 3/10\n", + "551/551 [==============================] - 31s 55ms/step - loss: 2.1548 - accuracy: 0.3088 - val_loss: 2.1161 - val_accuracy: 0.3180\n", + "Epoch 4/10\n", + "551/551 [==============================] - 29s 51ms/step - loss: 2.0701 - accuracy: 0.3264 - val_loss: 2.0176 - val_accuracy: 0.3416\n", + "Epoch 5/10\n", + "551/551 [==============================] - 29s 52ms/step - loss: 2.0042 - accuracy: 0.3417 - val_loss: 2.0083 - val_accuracy: 0.3424\n", + "Epoch 6/10\n", + "551/551 [==============================] - 28s 51ms/step - loss: 1.9771 - accuracy: 0.3518 - val_loss: 1.9500 - val_accuracy: 0.3660\n", + "Epoch 7/10\n", + "551/551 [==============================] - 29s 51ms/step - loss: 1.9136 - accuracy: 0.3694 - val_loss: 1.8914 - val_accuracy: 0.3881\n", + "Epoch 8/10\n", + "551/551 [==============================] - 31s 56ms/step - loss: 1.8748 - accuracy: 0.3789 - val_loss: 2.0321 - val_accuracy: 0.3416\n", + "Epoch 9/10\n", + "551/551 [==============================] - 31s 56ms/step - loss: 1.8413 - accuracy: 0.3881 - val_loss: 1.9444 - val_accuracy: 0.3751\n", + "Epoch 10/10\n", + "551/551 [==============================] - 29s 51ms/step - loss: 1.8477 - accuracy: 0.3902 - val_loss: 1.8945 - val_accuracy: 0.3822\n" + ] + } + ], + "source": [ + "ann_history= ann_model.fit(train_ds,validation_data=val_ds,epochs=10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 507 + }, + "id": "N1G8-LeCBU8d", + "outputId": "6d6717f7-e10d-4757-e619-71b63f3e2338" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_graph(ann_history)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dlI2JELTUI5W" + }, + "source": [ + "# Basic CNN model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "WmkAduHdUI5W" + }, + "outputs": [], + "source": [ + "from keras.optimizers import Adam" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "O9R68g_lUI5W", + "outputId": "21d1cdb3-56ea-480d-e662-375c82003429" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_1\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " conv2d (Conv2D) (None, 148, 148, 32) 896 \n", + " \n", + " batch_normalization (Batch (None, 148, 148, 32) 128 \n", + " Normalization) \n", + " \n", + " max_pooling2d (MaxPooling2 (None, 74, 74, 32) 0 \n", + " D) \n", + " \n", + " conv2d_1 (Conv2D) (None, 72, 72, 64) 18496 \n", + " \n", + " batch_normalization_1 (Bat (None, 72, 72, 64) 256 \n", + " chNormalization) \n", + " \n", + " conv2d_2 (Conv2D) (None, 70, 70, 64) 36928 \n", + " \n", + " batch_normalization_2 (Bat (None, 70, 70, 64) 256 \n", + " chNormalization) \n", + " \n", + " max_pooling2d_1 (MaxPoolin (None, 35, 35, 64) 0 \n", + " g2D) \n", + " \n", + " conv2d_3 (Conv2D) (None, 33, 33, 128) 73856 \n", + " \n", + " batch_normalization_3 (Bat (None, 33, 33, 128) 512 \n", + " chNormalization) \n", + " \n", + " max_pooling2d_2 (MaxPoolin (None, 16, 16, 128) 0 \n", + " g2D) \n", + " \n", + " conv2d_4 (Conv2D) (None, 14, 14, 128) 147584 \n", + " \n", + " batch_normalization_4 (Bat (None, 14, 14, 128) 512 \n", + " chNormalization) \n", + " \n", + " conv2d_5 (Conv2D) (None, 12, 12, 128) 147584 \n", + " \n", + " batch_normalization_5 (Bat (None, 12, 12, 128) 512 \n", + " chNormalization) \n", + " \n", + " max_pooling2d_3 (MaxPoolin (None, 6, 6, 128) 0 \n", + " g2D) \n", + " \n", + " flatten_1 (Flatten) (None, 4608) 0 \n", + " \n", + " dense_3 (Dense) (None, 1024) 4719616 \n", + " \n", + " batch_normalization_6 (Bat (None, 1024) 4096 \n", + " chNormalization) \n", + " \n", + " dropout (Dropout) (None, 1024) 0 \n", + " \n", + " dense_4 (Dense) (None, 22) 22550 \n", + " \n", + "=================================================================\n", + "Total params: 5173782 (19.74 MB)\n", + "Trainable params: 5170646 (19.72 MB)\n", + "Non-trainable params: 3136 (12.25 KB)\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "from tensorflow.keras import layers, models\n", + "\n", + "# Define the CNN model with batch normalization\n", + "cnn_model = models.Sequential([\n", + " layers.Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)),\n", + " layers.BatchNormalization(),\n", + " layers.MaxPooling2D((2, 2)),\n", + " layers.Conv2D(64, (3, 3), activation='relu'),\n", + " layers.BatchNormalization(),\n", + " layers.Conv2D(64, (3, 3), activation='relu'),\n", + " layers.BatchNormalization(),\n", + " layers.MaxPooling2D((2, 2)),\n", + " layers.Conv2D(128, (3, 3), activation='relu'),\n", + " layers.BatchNormalization(),\n", + " layers.MaxPooling2D((2, 2)),\n", + " layers.Conv2D(128, (3, 3), activation='relu'),\n", + " layers.BatchNormalization(),\n", + " layers.Conv2D(128, (3, 3), activation='relu'),\n", + " layers.BatchNormalization(),\n", + " layers.MaxPooling2D((2, 2)),\n", + " layers.Flatten(),\n", + " layers.Dense(1024, activation='relu'),\n", + " layers.BatchNormalization(),\n", + " layers.Dropout(0.75),\n", + " layers.Dense(22, activation='softmax')\n", + "])\n", + "\n", + "\n", + "# Compile the model\n", + "cnn_model.compile(optimizer='adam',\n", + " loss='categorical_crossentropy',\n", + " metrics=['accuracy'])\n", + "\n", + "# Display the model summary\n", + "cnn_model.summary()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QjOA_sUCUI5W", + "outputId": "0b72dc7c-2df8-485f-8746-c999f31cb301" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "551/551 [==============================] - 46s 64ms/step - loss: 3.2072 - accuracy: 0.2810 - val_loss: 2.7506 - val_accuracy: 0.2116\n", + "Epoch 2/10\n", + "551/551 [==============================] - 35s 63ms/step - loss: 2.0666 - accuracy: 0.4187 - val_loss: 1.9861 - val_accuracy: 0.4283\n", + "Epoch 3/10\n", + "551/551 [==============================] - 36s 65ms/step - loss: 1.5801 - accuracy: 0.5168 - val_loss: 1.7026 - val_accuracy: 0.4803\n", + "Epoch 4/10\n", + "551/551 [==============================] - 37s 67ms/step - loss: 1.3267 - accuracy: 0.5757 - val_loss: 1.1292 - val_accuracy: 0.6340\n", + "Epoch 5/10\n", + "551/551 [==============================] - 38s 69ms/step - loss: 1.1433 - accuracy: 0.6254 - val_loss: 1.0762 - val_accuracy: 0.6466\n", + "Epoch 6/10\n", + "551/551 [==============================] - 35s 64ms/step - loss: 1.0291 - accuracy: 0.6573 - val_loss: 1.1473 - val_accuracy: 0.6399\n", + "Epoch 7/10\n", + "551/551 [==============================] - 35s 62ms/step - loss: 0.9183 - accuracy: 0.6904 - val_loss: 0.9613 - val_accuracy: 0.6848\n", + "Epoch 8/10\n", + "551/551 [==============================] - 36s 65ms/step - loss: 0.8195 - accuracy: 0.7247 - val_loss: 1.1618 - val_accuracy: 0.6584\n", + "Epoch 9/10\n", + "551/551 [==============================] - 36s 65ms/step - loss: 0.7272 - accuracy: 0.7481 - val_loss: 0.9426 - val_accuracy: 0.7104\n", + "Epoch 10/10\n", + "551/551 [==============================] - 36s 65ms/step - loss: 0.6606 - accuracy: 0.7719 - val_loss: 1.0112 - val_accuracy: 0.6899\n" + ] + } + ], + "source": [ + "cnn_history = cnn_model.fit(\n", + " train_ds,\n", + " validation_data=val_ds,\n", + "\n", + " epochs=10 \n", + ")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 507 + }, + "id": "09A0ihQVUI5X", + "outputId": "29a4f519-ff28-41f9-b2e6-6de784815841" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_graph(cnn_history)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "edlmW0YVUI5b" + }, + "source": [ + "# Resnet(Basic)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "Ggk2xWRSsy-3" + }, + "outputs": [], + "source": [ + "from tensorflow.keras.applications import ResNet50\n", + "from tensorflow.keras.models import Model\n", + "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D , Dropout\n", + "from tensorflow.keras.optimizers import Adam\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "bYBGWOAPsL1k", + "outputId": "5ff7de24-5683-44dc-a959-e4b69a9b11eb" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n", + "94765736/94765736 [==============================] - 3s 0us/step\n" + ] + } + ], + "source": [ + "base_model = ResNet50(weights='imagenet', include_top=False)\n", + "\n", + "for layer in base_model.layers[-10:]:\n", + " layer.trainable = True\n", + "\n", + "# Add new top layers\n", + "x = base_model.output\n", + "x = GlobalAveragePooling2D()(x)\n", + "x = Dense(1024, activation='relu')(x)\n", + "predictions = Dense(22, activation='softmax')(x)\n", + "\n", + "res_model = Model(inputs=base_model.input, outputs=predictions)\n", + "\n", + "\n", + "# Compile the model\n", + "res_model.compile(optimizer=Adam(learning_rate=1e-5),\n", + " loss='categorical_crossentropy',\n", + " metrics=['accuracy'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6ANqeRP8tWV4", + "outputId": "5091ac68-6b19-4668-8584-3b046dd64b7e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "551/551 [==============================] - 150s 201ms/step - loss: 1.3461 - accuracy: 0.6010 - val_loss: 3.0756 - val_accuracy: 0.0867\n", + "Epoch 2/10\n", + "551/551 [==============================] - 104s 188ms/step - loss: 0.5116 - accuracy: 0.8258 - val_loss: 0.5763 - val_accuracy: 0.8042\n", + "Epoch 3/10\n", + "551/551 [==============================] - 110s 200ms/step - loss: 0.3162 - accuracy: 0.8965 - val_loss: 0.4449 - val_accuracy: 0.8479\n", + "Epoch 4/10\n", + "551/551 [==============================] - 104s 189ms/step - loss: 0.2023 - accuracy: 0.9386 - val_loss: 0.4379 - val_accuracy: 0.8522\n", + "Epoch 5/10\n", + "551/551 [==============================] - 104s 187ms/step - loss: 0.1295 - accuracy: 0.9632 - val_loss: 0.4506 - val_accuracy: 0.8542\n", + "Epoch 6/10\n", + "551/551 [==============================] - 104s 187ms/step - loss: 0.0870 - accuracy: 0.9776 - val_loss: 0.4571 - val_accuracy: 0.8601\n", + "Epoch 7/10\n", + "551/551 [==============================] - 104s 188ms/step - loss: 0.0579 - accuracy: 0.9848 - val_loss: 0.4730 - val_accuracy: 0.8562\n", + "Epoch 8/10\n", + "551/551 [==============================] - 104s 189ms/step - loss: 0.0477 - accuracy: 0.9855 - val_loss: 0.5126 - val_accuracy: 0.8617\n", + "Epoch 9/10\n", + "551/551 [==============================] - 104s 188ms/step - loss: 0.0387 - accuracy: 0.9878 - val_loss: 0.5597 - val_accuracy: 0.8491\n", + "Epoch 10/10\n", + "551/551 [==============================] - 104s 188ms/step - loss: 0.0378 - accuracy: 0.9864 - val_loss: 0.5381 - val_accuracy: 0.8495\n" + ] + } + ], + "source": [ + "resnet_history= res_model.fit(train_ds,validation_data=val_ds,epochs=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 468 + }, + "id": "7QLWlY1dvfUU", + "outputId": "5d0f74ca-f30f-47c7-db63-ec68d0c1de13" + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_graph(resnet_history)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WGcdJoLHvjxd" + }, + "source": [ + "# ResNet(Perfect) with Data Augumentaion and Regularizations" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6kfaFjzVemsj", + "outputId": "6cff68f7-0fe9-460a-955f-97608240456a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 17605 images belonging to 22 classes.\n", + "Found 2538 images belonging to 22 classes.\n" + ] + } + ], + "source": [ + "# Data augmentaion\n", + "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", + "\n", + "datagen = ImageDataGenerator(\n", + " rotation_range=20,\n", + " width_shift_range=0.2,\n", + " height_shift_range=0.2,\n", + " shear_range=0.2,\n", + " zoom_range=0.2,\n", + " horizontal_flip=True,\n", + " fill_mode='nearest'\n", + ")\n", + "\n", + "train_generator = datagen.flow_from_directory(\n", + " 'filtered_train',\n", + " target_size=(150,150),\n", + " batch_size=32,\n", + " class_mode='categorical'\n", + ")\n", + "\n", + "val_generator = datagen.flow_from_directory(\n", + " 'filtered_val',\n", + " target_size=(150, 150),\n", + " batch_size=32,\n", + " class_mode='categorical'\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "id": "AsmRa-MUfsCs" + }, + "outputs": [], + "source": [ + "from tensorflow.keras.applications import ResNet50\n", + "from tensorflow.keras.layers import GlobalAveragePooling2D, Dense, Dropout\n", + "from tensorflow.keras.models import Model\n", + "from tensorflow.keras.optimizers import Adam\n", + "from tensorflow.keras.regularizers import l2\n", + "from tensorflow.keras.callbacks import EarlyStopping\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "h7_q9fbffbgd", + "outputId": "065ed344-07fa-4bfe-de27-62018da4e819" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/5\n", + "551/551 [==============================] - 218s 342ms/step - loss: 16.7730 - accuracy: 0.2183 - val_loss: 15.0907 - val_accuracy: 0.5839\n", + "Epoch 2/5\n", + "551/551 [==============================] - 187s 339ms/step - loss: 15.0545 - accuracy: 0.5087 - val_loss: 14.0818 - val_accuracy: 0.7206\n", + "Epoch 3/5\n", + "551/551 [==============================] - 187s 339ms/step - loss: 14.0944 - accuracy: 0.6330 - val_loss: 13.3983 - val_accuracy: 0.7545\n", + "Epoch 4/5\n", + "551/551 [==============================] - 189s 343ms/step - loss: 13.3619 - accuracy: 0.6914 - val_loss: 12.7492 - val_accuracy: 0.7888\n", + "Epoch 5/5\n", + "551/551 [==============================] - 192s 349ms/step - loss: 12.6688 - accuracy: 0.7296 - val_loss: 12.1332 - val_accuracy: 0.8081\n" + ] + } + ], + "source": [ + "# Load the base model\n", + "base_model = ResNet50(weights='imagenet', include_top=False)\n", + "\n", + "# Make the last 10 layers trainable\n", + "for layer in base_model.layers[-10:]:\n", + " layer.trainable = True\n", + "\n", + "# Add new top layers\n", + "x = base_model.output\n", + "x = GlobalAveragePooling2D()(x)\n", + "x = Dropout(0.5)(x) # Adding dropout for regularization\n", + "x = Dense(1024, activation='relu', kernel_regularizer=l2(0.01))(x)\n", + "x = Dropout(0.5)(x) # Adding another dropout layer for regularization\n", + "predictions = Dense(22, activation='softmax', kernel_regularizer=l2(0.01))(x)\n", + "\n", + "res_model_perfect = Model(inputs=base_model.input, outputs=predictions)\n", + "\n", + "# Re-compile the model\n", + "res_model_perfect.compile(optimizer=Adam(learning_rate=1e-5),\n", + " loss='categorical_crossentropy',\n", + " metrics=['accuracy'])\n", + "\n", + "# Early stopping callback\n", + "early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n", + "\n", + "# Train the model\n", + "res_model_perfect_history = res_model_perfect.fit(train_generator, epochs=5, validation_data=val_generator, callbacks=[early_stopping])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "rottw-JQjOq4", + "outputId": "275497e0-d72c-400c-d5e7-b777665d7d68" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 6/10\n", + "551/551 [==============================] - 189s 344ms/step - loss: 11.9784 - accuracy: 0.7541 - val_loss: 11.4686 - val_accuracy: 0.8282\n", + "Epoch 7/10\n", + "551/551 [==============================] - 189s 342ms/step - loss: 11.3271 - accuracy: 0.7718 - val_loss: 10.8296 - val_accuracy: 0.8294\n", + "Epoch 8/10\n", + "551/551 [==============================] - 191s 347ms/step - loss: 10.6584 - accuracy: 0.7876 - val_loss: 10.2008 - val_accuracy: 0.8373\n", + "Epoch 9/10\n", + "551/551 [==============================] - 192s 347ms/step - loss: 10.0059 - accuracy: 0.8040 - val_loss: 9.6002 - val_accuracy: 0.8408\n", + "Epoch 10/10\n", + "551/551 [==============================] - 194s 352ms/step - loss: 9.3686 - accuracy: 0.8173 - val_loss: 8.9783 - val_accuracy: 0.8455\n" + ] + } + ], + "source": [ + "res_model_perfect_history1 = res_model_perfect.fit(train_generator, initial_epoch=5,epochs=10, validation_data=val_generator, callbacks=[early_stopping])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lhkIRX0oEpTj" + }, + "source": [ + "# VGG16 Model" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "v93hTELS5Jzl" + }, + "outputs": [], + "source": [ + "from tensorflow.keras.applications import VGG16\n", + "from tensorflow.keras.layers import GlobalAveragePooling2D, Dense, Dropout\n", + "from tensorflow.keras.models import Model\n", + "from tensorflow.keras.optimizers import SGD\n", + "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "TbOkFGjzEw5p", + "outputId": "d30519f5-efa9-465c-b5bb-f43e9f81e484" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n", + "58889256/58889256 [==============================] - 2s 0us/step\n", + "Model: \"model_2\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " input_3 (InputLayer) [(None, None, None, 3)] 0 \n", + " \n", + " block1_conv1 (Conv2D) (None, None, None, 64) 1792 \n", + " \n", + " block1_conv2 (Conv2D) (None, None, None, 64) 36928 \n", + " \n", + " block1_pool (MaxPooling2D) (None, None, None, 64) 0 \n", + " \n", + " block2_conv1 (Conv2D) (None, None, None, 128) 73856 \n", + " \n", + " block2_conv2 (Conv2D) (None, None, None, 128) 147584 \n", + " \n", + " block2_pool (MaxPooling2D) (None, None, None, 128) 0 \n", + " \n", + " block3_conv1 (Conv2D) (None, None, None, 256) 295168 \n", + " \n", + " block3_conv2 (Conv2D) (None, None, None, 256) 590080 \n", + " \n", + " block3_conv3 (Conv2D) (None, None, None, 256) 590080 \n", + " \n", + " block3_pool (MaxPooling2D) (None, None, None, 256) 0 \n", + " \n", + " block4_conv1 (Conv2D) (None, None, None, 512) 1180160 \n", + " \n", + " block4_conv2 (Conv2D) (None, None, None, 512) 2359808 \n", + " \n", + " block4_conv3 (Conv2D) (None, None, None, 512) 2359808 \n", + " \n", + " block4_pool (MaxPooling2D) (None, None, None, 512) 0 \n", + " \n", + " block5_conv1 (Conv2D) (None, None, None, 512) 2359808 \n", + " \n", + " block5_conv2 (Conv2D) (None, None, None, 512) 2359808 \n", + " \n", + " block5_conv3 (Conv2D) (None, None, None, 512) 2359808 \n", + " \n", + " block5_pool (MaxPooling2D) (None, None, None, 512) 0 \n", + " \n", + " global_average_pooling2d_2 (None, 512) 0 \n", + " (GlobalAveragePooling2D) \n", + " \n", + " dense_4 (Dense) (None, 1024) 525312 \n", + " \n", + " dense_5 (Dense) (None, 22) 22550 \n", + " \n", + "=================================================================\n", + "Total params: 15262550 (58.22 MB)\n", + "Trainable params: 15262550 (58.22 MB)\n", + "Non-trainable params: 0 (0.00 Byte)\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "\n", + "# Load the base model\n", + "base_model = VGG16(weights='imagenet', include_top=False)\n", + "\n", + "# Make more layers trainable\n", + "for layer in base_model.layers[-10:]:\n", + " layer.trainable = True\n", + "\n", + "# Add new top layers\n", + "x = base_model.output\n", + "x = GlobalAveragePooling2D()(x)\n", + "x = Dense(1024, activation='relu')(x)\n", + "predictions = Dense(22, activation='softmax')(x)\n", + "\n", + "# Create the final model\n", + "vgg_model = Model(inputs=base_model.input, outputs=predictions)\n", + "\n", + "vgg_model.compile(optimizer=Adam(learning_rate=1e-5),\n", + " loss='categorical_crossentropy',\n", + " metrics=['accuracy'])\n", + "\n", + "vgg_model.summary()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Nc8DXxH8MvnG", + "outputId": "babb6905-0dab-4fc2-d615-6e08898524ca" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "551/551 [==============================] - 148s 239ms/step - loss: 1.2364 - accuracy: 0.5985 - val_loss: 0.7437 - val_accuracy: 0.7360\n", + "Epoch 2/10\n", + "551/551 [==============================] - 123s 223ms/step - loss: 0.5993 - accuracy: 0.7848 - val_loss: 0.6208 - val_accuracy: 0.7683\n", + "Epoch 3/10\n", + "551/551 [==============================] - 128s 232ms/step - loss: 0.4672 - accuracy: 0.8295 - val_loss: 0.5241 - val_accuracy: 0.8081\n", + "Epoch 4/10\n", + "551/551 [==============================] - 128s 232ms/step - loss: 0.3806 - accuracy: 0.8620 - val_loss: 0.4916 - val_accuracy: 0.8140\n", + "Epoch 5/10\n", + "551/551 [==============================] - 124s 224ms/step - loss: 0.3119 - accuracy: 0.8891 - val_loss: 0.5329 - val_accuracy: 0.8069\n", + "Epoch 6/10\n", + "551/551 [==============================] - 124s 224ms/step - loss: 0.2599 - accuracy: 0.9063 - val_loss: 0.5281 - val_accuracy: 0.8128\n", + "Epoch 7/10\n", + "551/551 [==============================] - 123s 223ms/step - loss: 0.2171 - accuracy: 0.9225 - val_loss: 0.5258 - val_accuracy: 0.8262\n", + "Epoch 8/10\n", + "551/551 [==============================] - 128s 232ms/step - loss: 0.1756 - accuracy: 0.9380 - val_loss: 0.5114 - val_accuracy: 0.8353\n", + "Epoch 9/10\n", + "551/551 [==============================] - 123s 223ms/step - loss: 0.1501 - accuracy: 0.9474 - val_loss: 0.5555 - val_accuracy: 0.8286\n", + "Epoch 10/10\n", + "551/551 [==============================] - 128s 232ms/step - loss: 0.1284 - accuracy: 0.9558 - val_loss: 0.5891 - val_accuracy: 0.8270\n" + ] + } + ], + "source": [ + "vgg_history= vgg_model.fit(train_ds,epochs=10,validation_data=val_ds)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 468 + }, + "id": "5E-06ul8He9M", + "outputId": "210c701f-518b-4253-86ec-f5754d5798eb" + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_graph(vgg_history)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RcmcV-PLnCw_" + }, + "source": [ + "# Model Evaluation and Best model Testing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "P6m-twEGm_AH", + "outputId": "9ecddb0d-3dc8-4cc5-ac29-4f7e77b5aeff" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "157/157 [==============================] - 7s 38ms/step - loss: 1.8495 - accuracy: 0.3898\n", + "ANN Test accuracy: 38.98%\n" + ] + } + ], + "source": [ + "loss_ann, accuracy_ann = ann_model.evaluate(test_ds)\n", + "print(f\"ANN Test accuracy: {accuracy_ann * 100:.2f}%\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "sAs7JCS1MQgL", + "outputId": "19d046a2-6e95-4542-8f60-3aa69acd6ef7" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "157/157 [==============================] - 9s 53ms/step - loss: 0.9678 - accuracy: 0.6992\n", + "CNN Test accuracy: 69.92%\n" + ] + } + ], + "source": [ + "loss_cnn, accuracy_cnn = cnn_model.evaluate(test_ds)\n", + "print(f\"CNN Test accuracy: {accuracy_cnn * 100:.2f}%\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "K2zPnXNaMQP6", + "outputId": "ba65fa34-0993-443c-e39f-71bd03be6a0e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "158/158 [==============================] - 9s 57ms/step - loss: 0.5548 - accuracy: 0.8562\n", + "ResNet Test accuracy: 85.62%\n" + ] + } + ], + "source": [ + "loss_res, accuracy_res = res_model.evaluate(test_ds)\n", + "print(f\"ResNet Test accuracy: {accuracy_res * 100:.2f}%\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vkawaMLVMP6d", + "outputId": "46a0d654-b34a-47b9-bf61-819e60e6f08e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "158/158 [==============================] - 11s 70ms/step - loss: 0.5903 - accuracy: 0.8303\n", + "VGG16 Test accuracy: 83.03%\n" + ] + } + ], + "source": [ + "\n", + "loss_vgg16, accuracy_vgg16 = vgg_model.evaluate(test_ds)\n", + "print(f\"VGG16 Test accuracy: {accuracy_vgg16 * 100:.2f}%\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "PLRg-hzH3xCv", + "outputId": "bd18867b-1445-40f0-d001-b63d62339591" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 5027 images belonging to 22 classes.\n" + ] + } + ], + "source": [ + "# Preparing test data for Renset Perfect model\n", + "test_generator = datagen.flow_from_directory(\n", + " 'filtered_test',\n", + " target_size=(150, 150),\n", + " batch_size=32,\n", + " class_mode='categorical'\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9z53Loe7uKVH", + "outputId": "63799035-a5cb-4279-89f5-69188aefb10e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "158/158 [==============================] - 47s 299ms/step - loss: 8.9857 - accuracy: 0.8472\n", + "ResNet(Perfect) Test accuracy: 84.72%\n" + ] + } + ], + "source": [ + "loss_res_model_perfect, accuracy_res_model_perfect = res_model_perfect.evaluate(test_generator)\n", + "print(f\"ResNet(Perfect) Test accuracy: {accuracy_res_model_perfect * 100:.2f}%\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "swE1qjVO30EQ" + }, + "source": [ + "# Tabulating the Model and it's Accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wG_5qUPadj7N" + }, + "source": [ + " | Model Name | Accuracy (%) |\n", + " |-----------------|--------------|\n", + " | ANN | 38.98 |\n", + " | CNN | 69.92 |\n", + " | VGG16 | 83.03 |\n", + " | ResNet | 85.62 |\n", + " | ResNet(Perfect) | 84.58 |" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2F0_YBHIc1zt" + }, + "source": [ + "- The ResNet(Perfect) model is chosen as the optimal model due to its balance of high accuracy and reduced overfitting, making it the best candidate for deployment. While the standard ResNet model achieved the highest accuracy, its overfitting issues could compromise its performance on new, unseen data. Therefore, for practical applications where generalization is crucial, the ResNet(Perfect) model is the preferred choice\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "_un1ZG_XDvzr" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kaggle": { + "accelerator": "none", + "dataSources": [ + { + "datasetId": 4280832, + "sourceId": 7368427, + "sourceType": "datasetVersion" + } + ], + "dockerImageVersionId": 30699, + "isGpuEnabled": false, + "isInternetEnabled": true, + "language": "python", + "sourceType": "notebook" + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } +>>>>>>> refs/remotes/origin/main:Crop Pest and Disease Detection using DL/Model/Crop_Pest_and_Disease_Detection_using_DL.ipynb + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IRYcu-UwzQAU" + }, + "source": [ + "# Loading Basic Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "_ryJq3BVUI5F" + }, + "outputs": [], + "source": [ + "import os\n", + "import cv2\n", + "import tensorflow as tf\n", + "from tensorflow.keras import layers, models\n", + "import matplotlib.pyplot as plt\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "Mukt0YWFjy8U" + }, + "outputs": [], + "source": [ + "# Creaking dataset folder to download the dataset\n", + "mkdir dataset\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "OOTLURUXlxxL", + "outputId": "a34cf209-2f49-4270-978f-d51147037fe5" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/content/dataset\n" + ] + } + ], + "source": [ + "# changing current directory path\n", + "cd dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QieK7HFzidPC", + "outputId": "3c9a5312-d30e-4950-eb6d-1913c3f56a79" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.6.14 / client 1.6.12)\n", + "Dataset URL: https://www.kaggle.com/datasets/nirmalsankalana/crop-pest-and-disease-detection\n", + "License(s): CC0-1.0\n", + "Downloading crop-pest-and-disease-detection.zip to /content/dataset\n", + "100% 1.24G/1.25G [00:46<00:00, 31.4MB/s]\n", + "100% 1.25G/1.25G [00:46<00:00, 29.0MB/s]\n" + ] + } + ], + "source": [ + "# Downloading the dataset\n", + "!kaggle datasets download -d nirmalsankalana/crop-pest-and-disease-detection" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "collapsed": true, + "id": "FhJ3tB5mjVe0", + "outputId": "8b6fff35-753b-441f-ecc9-4095b21eed32" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n", + " inflating: Tomato leaf blight/leaf blight1302_.jpg \n", + " inflating: Tomato leaf blight/leaf blight1303_.jpg \n", + " inflating: Tomato leaf blight/leaf blight1304_.jpg \n", + " inflating: Tomato leaf blight/leaf blight1305_.jpg \n", + " inflating: Tomato leaf blight/leaf blight1306_.jpg \n", + " inflating: Tomato leaf blight/leaf blight1307_.jpg \n", + " inflating: Tomato leaf blight/leaf blight1308_.jpg \n", + " inflating: Tomato leaf blight/leaf blight1309_.jpg \n", + " inflating: Tomato leaf blight/leaf blight130_.jpg \n", + " inflating: Tomato leaf blight/leaf blight1310_.jpg \n", + " 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Tomato verticulium wilt/verticulium wilt76_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt77_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt78_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt79_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt7_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt80_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt81_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt82_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt83_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt84_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt85_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt86_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt87_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt88_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt89_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt8_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt90_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt91_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt92_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt93_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt94_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt95_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt96_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt97_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt98_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt99_.jpg \n", + " inflating: Tomato verticulium wilt/verticulium wilt9_.jpg \n" + ] + } + ], + "source": [ + "# Unzipping the dataset\n", + "!unzip \\*.zip\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4V88wjJT2_30" + }, + "outputs": [], + "source": [ + "# Navigating back to content directory\n", + "cd '../'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WlsvA2X-UI5K" + }, + "source": [ + "# Some predifined functions" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "27ZBrqgWUI5M" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "def plot_graph(history):\n", + " fig, ax = plt.subplots(1, 2, figsize=(12, 5)) # Create a figure with 1 row and 2 columns\n", + "\n", + " # Plot training & validation accuracy values\n", + " ax[0].plot(history.history['accuracy'])\n", + " ax[0].plot(history.history['val_accuracy'])\n", + " ax[0].set_title('Model accuracy')\n", + " ax[0].set_ylabel('Accuracy')\n", + " ax[0].set_xlabel('Epoch')\n", + " ax[0].legend(['Train', 'Validation'], loc='upper left')\n", + "\n", + " # Plot training & validation loss values\n", + " ax[1].plot(history.history['loss'])\n", + " ax[1].plot(history.history['val_loss'])\n", + " ax[1].set_title('Model loss')\n", + " ax[1].set_ylabel('Loss')\n", + " ax[1].set_xlabel('Epoch')\n", + " ax[1].legend(['Train', 'Validation'], loc='upper left')\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LOwqeXYpUI5N" + }, + "source": [ + "# Data Cleaning and filter valid image data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Lc9naWsczqaA" + }, + "source": [ + "The given dataset contains some invalid images. The below code filters the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "collapsed": true, + "id": "yj3Dw9mWUI5O", + "outputId": "ad848410-2b0e-4988-a53c-458d917fdfae" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 392 image files in dataset/Cashew gumosis\n", + "Copied 274 images to train/Cashew gumosis\n", + "Copied 78 images to test/Cashew gumosis\n", + "Copied 40 images to val/Cashew gumosis\n", + "Found 673 image files in dataset/Maize grasshoper\n", + "Copied 471 images to train/Maize grasshoper\n", + "Copied 134 images to test/Maize grasshoper\n", + "Copied 68 images to val/Maize grasshoper\n", + "Found 1368 image files in dataset/Cashew healthy\n", + "Copied 957 images to train/Cashew healthy\n", + "Copied 273 images to test/Cashew healthy\n", + "Copied 138 images to val/Cashew healthy\n", + "Found 1015 image files in dataset/Cassava green mite\n", + "Copied 710 images to train/Cassava green mite\n", + "Copied 203 images to test/Cassava green mite\n", + "Copied 102 images to val/Cassava green mite\n", + "Found 948 image files in dataset/Maize leaf beetle\n", + "Copied 663 images to train/Maize leaf beetle\n", + "Copied 189 images to test/Maize leaf beetle\n", + "Copied 96 images to val/Maize leaf beetle\n", + "Found 1205 image files in dataset/Cassava mosaic\n", + "Copied 843 images to train/Cassava mosaic\n", + "Copied 241 images to test/Cassava mosaic\n", + "Copied 121 images to val/Cassava mosaic\n", + "Found 1006 image files in dataset/Maize leaf blight\n", + "Copied 704 images to train/Maize leaf blight\n", + "Copied 201 images to test/Maize leaf blight\n", + "Copied 101 images to val/Maize leaf blight\n", + "Found 979 image files in dataset/Maize streak virus\n", + "Copied 685 images to train/Maize streak virus\n", + "Copied 195 images to test/Maize streak virus\n", + "Copied 99 images to val/Maize streak virus\n", + "Found 1259 image files in dataset/Maize leaf spot\n", + "Copied 881 images to train/Maize leaf spot\n", + "Copied 251 images to test/Maize leaf spot\n", + "Copied 127 images to val/Maize leaf spot\n", + "Found 285 image files in dataset/Maize fall armyworm\n", + "Copied 199 images to train/Maize fall armyworm\n", + "Copied 57 images to test/Maize fall armyworm\n", + "Copied 29 images to val/Maize fall armyworm\n", + "Found 470 image files in dataset/Tomato healthy\n", + "Copied 329 images to train/Tomato healthy\n", + "Copied 94 images to test/Tomato healthy\n", + "Copied 47 images to val/Tomato healthy\n", + "Found 1193 image files in dataset/Cassava healthy\n", + "Copied 835 images to train/Cassava healthy\n", + "Copied 238 images to test/Cassava healthy\n", + "Copied 120 images to val/Cassava healthy\n", + "Found 518 image files in dataset/Tomato leaf curl\n", + "Copied 362 images to train/Tomato leaf curl\n", + "Copied 103 images to test/Tomato leaf curl\n", + "Copied 53 images to val/Tomato leaf curl\n", + "Found 1301 image files in dataset/Tomato leaf blight\n", + "Copied 910 images to train/Tomato leaf blight\n", + "Copied 260 images to test/Tomato leaf blight\n", + "Copied 131 images to val/Tomato leaf blight\n", + "Found 1682 image files in dataset/Cashew red rust\n", + "Copied 1177 images to train/Cashew red rust\n", + "Copied 336 images to test/Cashew red rust\n", + "Copied 169 images to val/Cashew red rust\n", + "Found 1481 image files in dataset/Cassava brown spot\n", + "Copied 1036 images to train/Cassava brown spot\n", + "Copied 296 images to test/Cassava brown spot\n", + "Copied 149 images to val/Cassava brown spot\n", + "Found 773 image files in dataset/Tomato verticulium wilt\n", + "Copied 541 images to train/Tomato verticulium wilt\n", + "Copied 154 images to test/Tomato verticulium wilt\n", + "Copied 78 images to val/Tomato verticulium wilt\n", + "Found 1729 image files in dataset/Cashew anthracnose\n", + "Copied 1210 images to train/Cashew anthracnose\n", + "Copied 345 images to test/Cashew anthracnose\n", + "Copied 174 images to val/Cashew anthracnose\n", + "Found 2743 image files in dataset/Tomato septoria leaf spot\n", + "Copied 1920 images to train/Tomato septoria leaf spot\n", + "Copied 548 images to test/Tomato septoria leaf spot\n", + "Copied 275 images to val/Tomato septoria leaf spot\n", + "Found 1378 image files in dataset/Cashew leaf miner\n", + "Copied 964 images to train/Cashew leaf miner\n", + "Copied 275 images to test/Cashew leaf miner\n", + "Copied 139 images to val/Cashew leaf miner\n", + "Found 208 image files in dataset/Maize healthy\n", + "Copied 145 images to train/Maize healthy\n", + "Copied 41 images to test/Maize healthy\n", + "Copied 22 images to val/Maize healthy\n", + "Found 2614 image files in dataset/Cassava bacterial blight\n", + "Copied 1829 images to train/Cassava bacterial blight\n", + "Copied 522 images to test/Cassava bacterial blight\n", + "Copied 263 images to val/Cassava bacterial blight\n" + ] + } + ], + "source": [ + "import os\n", + "import random\n", + "import shutil\n", + "\n", + "# Set the source directory\n", + "src_dir = 'dataset'\n", + "\n", + "# Set the destination directories\n", + "train_dir = 'train'\n", + "test_dir = 'test'\n", + "val_dir = 'val'\n", + "\n", + "# Set the split ratios\n", + "train_ratio = 0.7\n", + "test_ratio = 0.2\n", + "val_ratio = 0.1\n", + "\n", + "# Create the destination directories if they don't exist\n", + "os.makedirs(train_dir, exist_ok=True)\n", + "os.makedirs(test_dir, exist_ok=True)\n", + "os.makedirs(val_dir, exist_ok=True)\n", + "\n", + "# Get a list of all subdirectories in the source directory\n", + "subdirs = [d for d in os.listdir(src_dir) if os.path.isdir(os.path.join(src_dir, d))]\n", + "\n", + "for subdir in subdirs:\n", + " subdir_path = os.path.join(src_dir, subdir)\n", + " image_files = [f for f in os.listdir(subdir_path) if f.endswith('.jpg') or f.endswith('.png')]\n", + "\n", + " print(f\"Found {len(image_files)} image files in {subdir_path}\")\n", + "\n", + " # If no image files found in the subdirectory, skip it\n", + " if not image_files:\n", + " print(f\"No image files found in {subdir_path}. Skipping...\")\n", + " continue\n", + "\n", + " # Shuffle the list of image files\n", + " random.shuffle(image_files)\n", + "\n", + " # Calculate the number of images for each set\n", + " num_images = len(image_files)\n", + " num_train = int(num_images * train_ratio)\n", + " num_test = int(num_images * test_ratio)\n", + " num_val = num_images - num_train - num_test\n", + "\n", + " # Create subdirectories for train, test, and val sets\n", + " train_subdir = os.path.join(train_dir, subdir)\n", + " test_subdir = os.path.join(test_dir, subdir)\n", + " val_subdir = os.path.join(val_dir, subdir)\n", + "\n", + " os.makedirs(train_subdir, exist_ok=True)\n", + " os.makedirs(test_subdir, exist_ok=True)\n", + " os.makedirs(val_subdir, exist_ok=True)\n", + "\n", + " # Copy the images to their respective sets\n", + " train_files = image_files[:num_train]\n", + " test_files = image_files[num_train:num_train+num_test]\n", + " val_files = image_files[num_train+num_test:]\n", + "\n", + " for file in train_files:\n", + " src_path = os.path.join(subdir_path, file)\n", + " dst_path = os.path.join(train_subdir, file)\n", + " shutil.copy(src_path, dst_path)\n", + "\n", + " for file in test_files:\n", + " src_path = os.path.join(subdir_path, file)\n", + " dst_path = os.path.join(test_subdir, file)\n", + " shutil.copy(src_path, dst_path)\n", + "\n", + " for file in val_files:\n", + " src_path = os.path.join(subdir_path, file)\n", + " dst_path = os.path.join(val_subdir, file)\n", + " shutil.copy(src_path, dst_path)\n", + "\n", + " print(f\"Copied {num_train} images to {train_subdir}\")\n", + " print(f\"Copied {num_test} images to {test_subdir}\")\n", + " print(f\"Copied {num_val} images to {val_subdir}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true, + "id": "KwzUAq8xUI5Q" + }, + "outputs": [], + "source": [ + "# Function to check if an image is valid using OpenCV\n", + "def is_image_valid(image_path):\n", + " try:\n", + " img = cv2.imread(image_path)\n", + " if img is None:\n", + " return False\n", + " return True\n", + " except:\n", + " return False\n", + "\n", + "# Function to filter out corrupted images\n", + "def filter_and_save_valid_images(source_dir, target_dir):\n", + " if not os.path.exists(target_dir):\n", + " os.makedirs(target_dir)\n", + "\n", + " for root, _, files in os.walk(source_dir):\n", + " for file in files:\n", + " file_path = os.path.join(root, file)\n", + " if is_image_valid(file_path):\n", + " relative_path = os.path.relpath(file_path, source_dir)\n", + " target_path = os.path.join(target_dir, relative_path)\n", + " target_subdir = os.path.dirname(target_path)\n", + " if not os.path.exists(target_subdir):\n", + " os.makedirs(target_subdir)\n", + " cv2.imwrite(target_path, cv2.imread(file_path))\n", + "\n", + "# Specify the source and target directories\n", + "train_source_dir = 'train'\n", + "train_target_dir = 'filtered_train'\n", + "val_source_dir = 'val'\n", + "val_target_dir = 'filtered_val'\n", + "test_source_dir = 'test'\n", + "test_target_dir = 'filtered_test'\n", + "\n", + "# Filter and save valid images\n", + "filter_and_save_valid_images(train_source_dir, train_target_dir)\n", + "filter_and_save_valid_images(val_source_dir, val_target_dir)\n", + "filter_and_save_valid_images(test_source_dir, test_target_dir)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aKljc92wz7Es" + }, + "source": [ + "# Loading the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kIbRzkJ5UI5R", + "outputId": "a0820fa1-f15a-4f26-a72e-6c8350757de6" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 17605 files belonging to 22 classes.\n", + "Found 2538 files belonging to 22 classes.\n" + ] + } + ], + "source": [ + "\n", + "# Function to load and preprocess the dataset\n", + "def load_and_preprocess_dataset(directory, image_size=(150, 150), batch_size=32):\n", + " dataset = tf.keras.utils.image_dataset_from_directory(\n", + " directory=directory,\n", + " labels='inferred',\n", + " label_mode='categorical',\n", + " batch_size=batch_size,\n", + " image_size=image_size)\n", + "\n", + " class_names = dataset.class_names\n", + "\n", + " # Define a preprocessing function to scale the images\n", + " def preprocess(image, label):\n", + " # Rescale the image pixel values from [0, 255] to [0, 1]\n", + " image = tf.cast(image, tf.float32) / 255.0\n", + " return image, label\n", + "\n", + " # Apply the preprocessing function to the dataset\n", + " dataset = dataset.map(preprocess, num_parallel_calls=tf.data.AUTOTUNE)\n", + " return dataset, class_names\n", + "\n", + "# Load the datasets\n", + "train_ds, train_class_names = load_and_preprocess_dataset(train_target_dir)\n", + "val_ds, val_class_names = load_and_preprocess_dataset(val_target_dir)\n", + "\n", + "# Ensure the class names are consistent\n", + "assert train_class_names == val_class_names, \"Class names in training and validation datasets do not match.\"\n", + "\n", + "# Prefetching to improve performance\n", + "train_ds = train_ds.prefetch(buffer_size=tf.data.AUTOTUNE)\n", + "val_ds = val_ds.prefetch(buffer_size=tf.data.AUTOTUNE)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3SStCzWbUI5S", + "outputId": "32ff7f88-d132-427a-d79a-40ae71c41ea9" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 5027 files belonging to 22 classes.\n" + ] + } + ], + "source": [ + "test_ds, test_class_names = load_and_preprocess_dataset(test_target_dir)\n", + "test_ds = test_ds.prefetch(buffer_size=tf.data.AUTOTUNE)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GOC_HjrYUI5U" + }, + "source": [ + "# ANN model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MvkyNR5CUI5V", + "outputId": "d44f1c8f-194e-40b6-ccd6-df8c1f68712c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " flatten (Flatten) (None, 67500) 0 \n", + " \n", + " dense (Dense) (None, 512) 34560512 \n", + " \n", + " dense_1 (Dense) (None, 256) 131328 \n", + " \n", + " dense_2 (Dense) (None, 22) 5654 \n", + " \n", + "=================================================================\n", + "Total params: 34697494 (132.36 MB)\n", + "Trainable params: 34697494 (132.36 MB)\n", + "Non-trainable params: 0 (0.00 Byte)\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "from tensorflow.keras import layers, models\n", + "ann_model = models.Sequential([\n", + " layers.Flatten(input_shape=(150,150,3)), # Input layer flattens the image\n", + " layers.Dense(512, activation='relu'), # Hidden layer with 512 units and ReLU activation\n", + " layers.Dense(256, activation='relu'), # Hidden layer with 256 units and ReLU activation\n", + " layers.Dense(22, activation='softmax') # Output layer with softmax activation for classification\n", + "])\n", + "\n", + "# Compile the model\n", + "ann_model.compile(optimizer='adam',\n", + " loss='categorical_crossentropy',\n", + " metrics=['accuracy'])\n", + "\n", + "ann_model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "cWbWa40UUI5V", + "outputId": "f5c99b23-20d6-4298-e480-a68e572dad6c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "551/551 [==============================] - 32s 56ms/step - loss: 2.5090 - accuracy: 0.2331 - val_loss: 2.4573 - val_accuracy: 0.2368\n", + "Epoch 2/10\n", + "551/551 [==============================] - 35s 62ms/step - loss: 2.2748 - accuracy: 0.2769 - val_loss: 2.1671 - val_accuracy: 0.3073\n", + "Epoch 3/10\n", + "551/551 [==============================] - 31s 55ms/step - loss: 2.1548 - accuracy: 0.3088 - val_loss: 2.1161 - val_accuracy: 0.3180\n", + "Epoch 4/10\n", + "551/551 [==============================] - 29s 51ms/step - loss: 2.0701 - accuracy: 0.3264 - val_loss: 2.0176 - val_accuracy: 0.3416\n", + "Epoch 5/10\n", + "551/551 [==============================] - 29s 52ms/step - loss: 2.0042 - accuracy: 0.3417 - val_loss: 2.0083 - val_accuracy: 0.3424\n", + "Epoch 6/10\n", + "551/551 [==============================] - 28s 51ms/step - loss: 1.9771 - accuracy: 0.3518 - val_loss: 1.9500 - val_accuracy: 0.3660\n", + "Epoch 7/10\n", + "551/551 [==============================] - 29s 51ms/step - loss: 1.9136 - accuracy: 0.3694 - val_loss: 1.8914 - val_accuracy: 0.3881\n", + "Epoch 8/10\n", + "551/551 [==============================] - 31s 56ms/step - loss: 1.8748 - accuracy: 0.3789 - val_loss: 2.0321 - val_accuracy: 0.3416\n", + "Epoch 9/10\n", + "551/551 [==============================] - 31s 56ms/step - loss: 1.8413 - accuracy: 0.3881 - val_loss: 1.9444 - val_accuracy: 0.3751\n", + "Epoch 10/10\n", + "551/551 [==============================] - 29s 51ms/step - loss: 1.8477 - accuracy: 0.3902 - val_loss: 1.8945 - val_accuracy: 0.3822\n" + ] + } + ], + "source": [ + "ann_history= ann_model.fit(train_ds,validation_data=val_ds,epochs=10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 507 + }, + "id": "N1G8-LeCBU8d", + "outputId": "6d6717f7-e10d-4757-e619-71b63f3e2338" + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_graph(ann_history)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dlI2JELTUI5W" + }, + "source": [ + "# Basic CNN model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "WmkAduHdUI5W" + }, + "outputs": [], + "source": [ + "from keras.optimizers import Adam" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "O9R68g_lUI5W", + "outputId": "21d1cdb3-56ea-480d-e662-375c82003429" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_1\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " conv2d (Conv2D) (None, 148, 148, 32) 896 \n", + " \n", + " batch_normalization (Batch (None, 148, 148, 32) 128 \n", + " Normalization) \n", + " \n", + " max_pooling2d (MaxPooling2 (None, 74, 74, 32) 0 \n", + " D) \n", + " \n", + " conv2d_1 (Conv2D) (None, 72, 72, 64) 18496 \n", + " \n", + " batch_normalization_1 (Bat (None, 72, 72, 64) 256 \n", + " chNormalization) \n", + " \n", + " conv2d_2 (Conv2D) (None, 70, 70, 64) 36928 \n", + " \n", + " batch_normalization_2 (Bat (None, 70, 70, 64) 256 \n", + " chNormalization) \n", + " \n", + " max_pooling2d_1 (MaxPoolin (None, 35, 35, 64) 0 \n", + " g2D) \n", + " \n", + " conv2d_3 (Conv2D) (None, 33, 33, 128) 73856 \n", + " \n", + " batch_normalization_3 (Bat (None, 33, 33, 128) 512 \n", + " chNormalization) \n", + " \n", + " max_pooling2d_2 (MaxPoolin (None, 16, 16, 128) 0 \n", + " g2D) \n", + " \n", + " conv2d_4 (Conv2D) (None, 14, 14, 128) 147584 \n", + " \n", + " batch_normalization_4 (Bat (None, 14, 14, 128) 512 \n", + " chNormalization) \n", + " \n", + " conv2d_5 (Conv2D) (None, 12, 12, 128) 147584 \n", + " \n", + " batch_normalization_5 (Bat (None, 12, 12, 128) 512 \n", + " chNormalization) \n", + " \n", + " max_pooling2d_3 (MaxPoolin (None, 6, 6, 128) 0 \n", + " g2D) \n", + " \n", + " flatten_1 (Flatten) (None, 4608) 0 \n", + " \n", + " dense_3 (Dense) (None, 1024) 4719616 \n", + " \n", + " batch_normalization_6 (Bat (None, 1024) 4096 \n", + " chNormalization) \n", + " \n", + " dropout (Dropout) (None, 1024) 0 \n", + " \n", + " dense_4 (Dense) (None, 22) 22550 \n", + " \n", + "=================================================================\n", + "Total params: 5173782 (19.74 MB)\n", + "Trainable params: 5170646 (19.72 MB)\n", + "Non-trainable params: 3136 (12.25 KB)\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "from tensorflow.keras import layers, models\n", + "\n", + "# Define the CNN model with batch normalization\n", + "cnn_model = models.Sequential([\n", + " layers.Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)),\n", + " layers.BatchNormalization(),\n", + " layers.MaxPooling2D((2, 2)),\n", + " layers.Conv2D(64, (3, 3), activation='relu'),\n", + " layers.BatchNormalization(),\n", + " layers.Conv2D(64, (3, 3), activation='relu'),\n", + " layers.BatchNormalization(),\n", + " layers.MaxPooling2D((2, 2)),\n", + " layers.Conv2D(128, (3, 3), activation='relu'),\n", + " layers.BatchNormalization(),\n", + " layers.MaxPooling2D((2, 2)),\n", + " layers.Conv2D(128, (3, 3), activation='relu'),\n", + " layers.BatchNormalization(),\n", + " layers.Conv2D(128, (3, 3), activation='relu'),\n", + " layers.BatchNormalization(),\n", + " layers.MaxPooling2D((2, 2)),\n", + " layers.Flatten(),\n", + " layers.Dense(1024, activation='relu'),\n", + " layers.BatchNormalization(),\n", + " layers.Dropout(0.75),\n", + " layers.Dense(22, activation='softmax')\n", + "])\n", + "\n", + "\n", + "# Compile the model\n", + "cnn_model.compile(optimizer='adam',\n", + " loss='categorical_crossentropy',\n", + " metrics=['accuracy'])\n", + "\n", + "# Display the model summary\n", + "cnn_model.summary()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QjOA_sUCUI5W", + "outputId": "0b72dc7c-2df8-485f-8746-c999f31cb301" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "551/551 [==============================] - 46s 64ms/step - loss: 3.2072 - accuracy: 0.2810 - val_loss: 2.7506 - val_accuracy: 0.2116\n", + "Epoch 2/10\n", + "551/551 [==============================] - 35s 63ms/step - loss: 2.0666 - accuracy: 0.4187 - val_loss: 1.9861 - val_accuracy: 0.4283\n", + "Epoch 3/10\n", + "551/551 [==============================] - 36s 65ms/step - loss: 1.5801 - accuracy: 0.5168 - val_loss: 1.7026 - val_accuracy: 0.4803\n", + "Epoch 4/10\n", + "551/551 [==============================] - 37s 67ms/step - loss: 1.3267 - accuracy: 0.5757 - val_loss: 1.1292 - val_accuracy: 0.6340\n", + "Epoch 5/10\n", + "551/551 [==============================] - 38s 69ms/step - loss: 1.1433 - accuracy: 0.6254 - val_loss: 1.0762 - val_accuracy: 0.6466\n", + "Epoch 6/10\n", + "551/551 [==============================] - 35s 64ms/step - loss: 1.0291 - accuracy: 0.6573 - val_loss: 1.1473 - val_accuracy: 0.6399\n", + "Epoch 7/10\n", + "551/551 [==============================] - 35s 62ms/step - loss: 0.9183 - accuracy: 0.6904 - val_loss: 0.9613 - val_accuracy: 0.6848\n", + "Epoch 8/10\n", + "551/551 [==============================] - 36s 65ms/step - loss: 0.8195 - accuracy: 0.7247 - val_loss: 1.1618 - val_accuracy: 0.6584\n", + "Epoch 9/10\n", + "551/551 [==============================] - 36s 65ms/step - loss: 0.7272 - accuracy: 0.7481 - val_loss: 0.9426 - val_accuracy: 0.7104\n", + "Epoch 10/10\n", + "551/551 [==============================] - 36s 65ms/step - loss: 0.6606 - accuracy: 0.7719 - val_loss: 1.0112 - val_accuracy: 0.6899\n" + ] + } + ], + "source": [ + "cnn_history = cnn_model.fit(\n", + " train_ds,\n", + " validation_data=val_ds,\n", + "\n", + " epochs=10 # Adjust the number of epochs based on your requirements\n", + ")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 507 + }, + "id": "09A0ihQVUI5X", + "outputId": "29a4f519-ff28-41f9-b2e6-6de784815841" + }, + "outputs": [ + { + "data": { + "image/png": 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HCAgIMNq3b2/88MMPeW22bt1qDBkyxKhevbrh4eFhBAcHGzfffLOxefPmy8YkIs5hMYx/jG0UEZEyr2XLllSqVIklS5Y4OxQRERERESmnVFNKRKSc2bx5M9u3b2fYsGHODkVERERERMoxjZQSESkndu/ezZYtW5g4cSIJCQkcOXIET09PZ4clIiIiIiLllEZKiYiUE7Nnz2bEiBHk5OQwffp0JaRERERERMSpNFJKRERERERERESKnUZKiYiIiIiIiIhIsVNSSkREREREREREip2rswMobna7nVOnTuHn54fFYnF2OCIiIlLCGYZBSkoKVapUwWotv5/nqQ8lIiIiBVXQ/lO5S0qdOnWK8PBwZ4chIiIipczx48epVq2as8NwGvWhREREpLCu1H8qd0kpPz8/wPzG+Pv7OzkaERERKemSk5MJDw/P60OUV+pDiYiISEEVtP9U7pJS54ab+/v7q0MlIiIiBVbep6ypDyUiIiKFdaX+U/ktjCAiIiIiIiIiIk6jpJSIiIiIiIiIiBQ7JaVERERERERERKTYlbuaUgVls9nIyclxdhjiAG5ubri4uDg7DBERkXJBfaiyQ30oEREpakpK/YNhGMTExHD27FlnhyIOVKFCBUJDQ8t9kVoREZGioj5U2aQ+lIiIFCUlpf7hXGcqODgYb29v/QIu5QzDID09nbi4OADCwsKcHJGIiEjZpD5U2aI+lIiIFAclpf7GZrPldaYqV67s7HDEQby8vACIi4sjODhYw9BFREQcTH2oskl9KBERKWoqdP435+ofeHt7OzkScbRzf6aqcSEiIuJ46kOVXepDiYhIUVJS6iI03Lzs0Z+piIhI0dPv27JHf6YiIlKUlJQSEREREREREZFip6SUXFLNmjV5//33nR2GiIiISKmh/pOIiEjBKSlVBlgslstuL7/88lU9d9OmTTz88MOODVZERESkBFD/SURExPm0+l4ZEB0dnbc/c+ZMXnrpJfbv3593ztfXN2/fMAxsNhuurlf+ow8KCnJsoCIiIiIlhPpPIiIizqeRUmVAaGho3hYQEIDFYsk73rdvH35+fsyfP5/WrVvj4eHB6tWrOXz4MAMHDiQkJARfX1/atm3LH3/8ke+5/xx+brFY+OKLL7jlllvw9vamXr16zJs3r5jfrYiIiMi1U/9JRETE+ZSUugLDMEjPznXKZhiGw97Hv//9b9544w327t1Ls2bNSE1N5cYbb2TJkiVs27aNvn370r9/f6Kioi77nFdeeYU777yTnTt3cuONNzJ06FASExMdFqeIiEhhnE3P5uftJ9l54qyzQ5G/KWz/KTUzh+ikDOKSM9R/EhERKUc0fe8KMnJsNHppoVNe+8/xffB2d8wf0fjx47n++uvzjitVqkTz5s3zjl999VXmzJnDvHnzePzxxy/5nPvuu48hQ4YA8PrrrzNp0iQ2btxI3759HRKniIjI5RiGwf7YFJbui2Pp3ji2Rp3BbsCQduE0q1bB2eHJX9R/yk/9JxERkYtTUqqcaNOmTb7j1NRUXn75ZX777Teio6PJzc0lIyPjip/0NWvWLG/fx8cHf39/4uLiiiRmERERgIxsG2sPJ7B0XxzL9sVxKikz3/UGIX7UrOzjpOikLFP/SUREpGgpKXUFXm4u/Dm+j9Ne21F8fPJ31p955hkWL17MO++8Q926dfHy8uL2228nOzv7ss9xc3PLd2yxWLDb7Q6LU0REBODEmXSW7Ytj6b441h4+TVbu+d81Hq5WOtWpTM+GIfRoEES1it5OjFQu5mr6TwfjUsnKsVGtgjcVfNyufMNlXttR1H8SEREpWkpKXYHFYnHYEPCSZM2aNdx3333ccsstgPnJ37Fjx5wblIiIlFu5Njtbo86yZF8sy/bFcSA2Nd/1qhW86BERRM+IYDrWDsTL3XGJB3G8q+k/hfp7EpucSbbNXmL7Xuo/iYiIOFbJ/I0vRa5evXr89NNP9O/fH4vFwosvvqhP7EREpFglpmWz4kAcS/fFs/JAPEkZOXnXrBZoXaMiPSKC6RkRTIMQPywWixOjlaLm7+VGbHImqVm52OwGLtaS9+et/pOIiIhjKSlVTr377rvcf//9dOrUicDAQP71r3+RnJzs7LBERKQMMwyDvdEpLNsfx5K9sWw/fhb73xZKq+DtRvf6QfSICKZb/SAqeLs7L1gpdp6uVjxcrWTl2knJzCmRf/7qP4mIiDiWxXDkurmlQHJyMgEBASQlJeHv75/vWmZmJkePHqVWrVp4eno6KUIpCvqzFRFxjvTsXNYcOs3SfXEs3x9H9D+KlEeE+tHzr9FQLatXLJGjYy7XdyhPiqMPFZ2UQXxKFhW83Kiu4vUlgvpQIiJyNQraf9JIKREREXGo44npLP2rSPm6I6fJ/luRck83K53rBNKzYTA9GgRTpYKXEyOVkibAy434lCySM3Ox2w2sJTBJKSIiIo6jpJSIiIhckxybnS2RZ/ISUYfi8hcpr1bRi54RwfSICKZj7cp4OnB1NClbvNxccHOxkmOzk5qVi7/X1a/CJyIiIiWfklIiIiJSaKdTs1i+P56l++NYeSCelMzcvGsuVguta1SkZ0QwvSKCqRvsqyLlUiAWi4UALzcSUrNIyshRUkpERKSMU1JKRERErsgwDPacSmbZvjiW7o9j+/Gz/L0qZSUf97wi5V3rBRHgrWSCXB3/v5JSyZk52A0DqxKaIiIiZZaSUiIiInJRaVm5rD6UwLJ9cSzbH0dscla+643C/POm5bUIr1Aii5RL6ePj7oKr1Uqu3U5aVi5+nkpwioiIlFVKSomIiEieyNNpebWhNhxJJNt2vki5l5sL19ULNBNRDYIJDdBKXOJ4FosFfy9XEtOyScrIUVJKRESkDFNSSkREpBzLsdnZdCyRZfviWLIvjiPxafmuV6/knTcaqn2tSipSLsUiwMuNxLRskjNyMSoYqkkmIiJSRikpJSIiUs7Ep2SxfL85JW/VgQRSss4XKXe1WmhT0yxS3jMihDpBPkoISLHz8XDFxWoxp/Bl2/D1UJdVRESkLNJveBERkTLObjfYdTKJZfvjWLY/np0n8hcpr+zjTvcGwfSMCKZL/UD8NV1KnMxqseDv6caZ9GySM3KUlBIRESmjrM4OQEqG7t27M3r06LzjmjVr8v7771/2HovFwty5c6/5tR31HBEROe9sejbzdpxizMzttH3tDwZ+tIb3/zjIjr9WzWtS1Z8netZlzmOd2PRCbybe2ZybmoUpISUlRoCX+XcxKSMH4+9Z1BJE/ScREZFro4+dyoD+/fuTk5PDggULLri2atUqunbtyo4dO2jWrFmBn7lp0yZ8fHwcGSYvv/wyc+fOZfv27fnOR0dHU7FiRYe+lohIeWMYBntOJbN8fxzL98ezNeoM9r/9P97Xw5Uu9QLp3iCI7g2CCfFXkXIp2Xw9XLFaLOTY7GTk2PB2d2y3Vf0nERER51NSqgx44IEHuO222zhx4gTVqlXLd+3rr7+mTZs2hepQAQQFBTkyxMsKDQ0tttcSESlLkjNzWH0wIS8RFZeSle96gxC/vCRU6xoVcXfVAGkpPaxWC36eriRl5JCUkePwpJT6TyIiIs6n3mkZcPPNNxMUFMSUKVPynU9NTWXWrFkMGjSIIUOGULVqVby9vWnatCnTp0+/7DP/Ofz84MGDdO3aFU9PTxo1asTixYsvuOdf//oX9evXx9vbm9q1a/Piiy+Sk5MDwJQpU3jllVfYsWMHFosFi8WSF+8/h5/v2rWLnj174uXlReXKlXn44YdJTU3Nu37fffcxaNAg3nnnHcLCwqhcuTIjR47Mey0RkbLKMAz2x6Tw6YrD3PXZOlqNX8xjU7fyw+YTxKVk4e3uQu+GIbx2SxPW/LsnC5/qytgbG9KxTmUlpKRUOjeFL7kIpvCp/6T+k4iIOJ9GSl2JYUBOunNe280bCrDikaurK8OGDWPKlCm88MILeaskzZo1C5vNxj333MOsWbP417/+hb+/P7/99hv33nsvderUoV27dld8vt1u59ZbbyUkJIQNGzaQlJSUr37COX5+fkyZMoUqVaqwa9cuHnroIfz8/Hjuuee466672L17NwsWLOCPP/4AICAg4IJnpKWl0adPHzp27MimTZuIi4vjwQcf5PHHH8/XaVy2bBlhYWEsW7aMQ4cOcdddd9GiRQseeuihK74fEZHSJC0rlzWHEli2P57l++OITsrMd712kA89GgTTo0EwbWtVxMPVxUmRivyNg/pPflYDa24G2TkGmeng5VaAv9/qP6n/JCIipYaSUleSkw6vV3HOaz9/CtwLVpfg/vvv5+2332bFihV0794dMIee33bbbdSoUYNnnnkmr+2oUaNYuHAhP/zwQ4E6VX/88Qf79u1j4cKFVKlifi9ef/11+vXrl6/df/7zn7z9mjVr8swzzzBjxgyee+45vLy88PX1xdXV9bLDzadNm0ZmZibffvttXk2GyZMn079/f958801CQkIAqFixIpMnT8bFxYWIiAhuuukmlixZok6ViJR6hmFwOD6N5fvjWLY/jo1HE8mxnR8h4uFqpVOdyvSICKZ7/WCqV/Z2YrQil+Cg/pML0KSwN6n/BKj/JCIipYOSUmVEREQEnTp14quvvqJ79+4cOnSIVatWMX78eGw2G6+//jo//PADJ0+eJDs7m6ysLLy9C/Yfmb179xIeHp7XoQLo2LHjBe1mzpzJpEmTOHz4MKmpqeTm5uLv71+o97F3716aN2+er0ho586dsdvt7N+/P69T1bhxY1xczn9aGhYWxq5duwr1WiIiJUVGto11RxJYti+eZfvjOHEmI9/16pW86RkRTPcGQXSoXRnPgowWEZErUv9J/ScREXEuJaWuxM3b/MTNWa9dCA888ACjRo3io48+4uuvv6ZOnTp069aNN998kw8++ID333+fpk2b4uPjw+jRo8nOznZYqOvWrWPo0KG88sor9OnTh4CAAGbMmMHEiRMd9hp/5+aWf8lyi8WC3W4vktcSESkKxxLSWPZXgfJ1R06TnXv+Z5i7i5X2tSvRo4GZiKoV6JM3tUikVHBg/ynXZmdfTCoGBvWDffG4UlJW/adLUv9JRERKGiWlrsRiKfAQcGe78847efLJJ5k2bRrffvstjz76KBaLhTVr1jBw4EDuuecewKxxcODAARo1alSg5zZs2JDjx48THR1NWFgYAOvXr8/XZu3atdSoUYMXXngh71xkZGS+Nu7u7thstiu+1pQpU0hLS8v7tG/NmjVYrVYaNGhQoHhFREqizBwbG44msmxfHCsOxHM0IS3f9aoVvOgREUT3+sF0qlvZ4SuNiRQrB/afXAFvXwupWbkk2d0Jdvd0yHPPUf9JRETEedTjLUN8fX256667GDt2LMnJydx3330A1KtXj9mzZ7N27VoqVqzIu+++S2xsbIE7Vb1796Z+/foMHz6ct99+m+Tk5Hydp3OvERUVxYwZM2jbti2//fYbc+bMydemZs2aHD16lO3bt1OtWjX8/Pzw8PDI12bo0KGMGzeO4cOH8/LLLxMfH8+oUaO4995784aei4iUFscT01n+12ioNYcTyMw5PyLB1WqhXa1KdG8QRI8GwdQN9tVoKJFLCPByM5NSGTkE+zk2KaX+k4iIiPNofegy5oEHHuDMmTP06dMnr4bBf/7zH1q1akWfPn3o3r07oaGhDBo0qMDPtFqtzJkzh4yMDNq1a8eDDz7Ia6+9lq/NgAEDeOqpp3j88cdp0aIFa9eu5cUXX8zX5rbbbqNv37706NGDoKCgiy6r7O3tzcKFC0lMTKRt27bcfvvt9OrVi8mTJxf+myEiUsyyc+2sOZTAa7/9Se93V9DlrWW8+PMeluyLIzPHTqi/J4PbhvPpPa3Z9tL1THuoAw93rUO9ED8lpKTAPvnkE5o1a4a/vz/+/v507NiR+fPnX/aeWbNmERERgaenJ02bNuX3338vpmgdw9/LnHaWkW3LN9XVUdR/EhERcQ6LYRjGlZuVHcnJyQQEBJCUlHRBEcnMzEyOHj1KrVq18PR07Kdw4lz6sxWRohKdlMHy/fEs2xfHmkMJpGWfn2bjYrXQunpFukeYo6EiQpV8Ko0u13dwhl9++QUXFxfq1auHYRh88803vP3222zbto3GjRtf0H7t2rV07dqVCRMmcPPNNzNt2jTefPNNtm7dSpMmBV/bztl9qMPxqaRl5RIW4EWQn8eVbxCHUB9KRESuRkH7T5q+JyIiUgg5NjtbI8+wbH88y/fHsS8mJd/1QF+PvCl519ULJMDL7RJPErk6/fv3z3f82muv8cknn7B+/fqLJqU++OAD+vbty7PPPgvAq6++yuLFi5k8eTKffvppscTsCAFebqRl5ZKckaOklIiISBmhpJSIiMgVxCVnsvyAmYRadSCBlKzcvGsWC7QMr0CPBsH0iAimUZg/VqtGQ0nxsNlszJo1i7S0NDp27HjRNuvWrWPMmDH5zvXp04e5c+cWQ4SO4+/pxikySMvOJcdmx81FVShERERKOyWlRERELuJAbArztp9i2f449pxKznetko873eoH0b1BEF3rBVHRx91JUUp5tWvXLjp27EhmZia+vr7MmTPnkgW4Y2JiLih2HRISQkxMzGVfIysri6ysrLzj5OTky7Queu6uVrzdXUnPNkdLVfbVaCkREZHSTkkpERGRv+Ta7PyxN5Ypa4+x/khivmvNqwXQvUEw3RsE0axaBVw0GkqcqEGDBmzfvp2kpCRmz57N8OHDWbFiRYFXhiuICRMm8MorrzjseY7g72UmpZKUlBIRESkTlJQSEZFyLzEtm+kbo5i6PpJTSZmAWaS8V0QwfRqH0rV+kGrYSIni7u5O3bp1AWjdujWbNm3igw8+4LPPPrugbWhoKLGxsfnOxcbGEhoaetnXGDt2bL5pf8nJyYSHhzsg+qsX4OlGTFImaVk2cm12XDWFT0REpFRTUuoi7HbHLzUszqU/UxG5mF0nkpiy9hi/7DyVt8x8JR93hrQLZ2j7GlSp4OXkCEUKxm6355tq93cdO3ZkyZIljB49Ou/c4sWLL1mD6hwPDw88PAqXjC3q37cebi54urmQmWMjOTOXSpo6W+TUhxIRkaKkpNTfuLu7Y7VaOXXqFEFBQbi7u2vp7lLOMAyys7OJj4/HarXi7q7Oq0h5l51rZ/7uaKasPca2qLN555tVC2B4x5rc1CwMTzcX5wUocgVjx46lX79+VK9enZSUFKZNm8by5ctZuHAhAMOGDaNq1apMmDABgCeffJJu3boxceJEbrrpJmbMmMHmzZv5/PPPHRZTcfahvF3sZGRkcybZjreLd5G8hqgPJSIixUNJqb+xWq3UqlWL6OhoTp065exwxIG8vb2pXr06VquG+YuUV7HJmUzdEMW0DVEkpJojStxcLNzUNIzhnWrSsnpFJ0coUjBxcXEMGzaM6OhoAgICaNasGQsXLuT6668HICoqKt/vu06dOjFt2jT+85//8Pzzz1OvXj3mzp1LkyZNHBZTcfahcmx24pKziLdA5hlPrPoAsUipDyUiIkXJYhiG4ewgilNycjIBAQEkJSXh7+9/0TaGYZCbm4vNZivm6KQouLi44OrqqlFvIuWQYRhsjjzDN2uPsWB3DLl281deiL8HQ9vXYEi76qoVJVdUkL5DeVBS+lCGYXDf15s4cSadF25qRM+I4CJ7rfJOfSgREblaBe0/aaTURVgsFtzc3HBzc3N2KCIichUyc2zM236KKWuP8Wf0+WXs29asyPBONenTOBQ3FUgWcbji6kO1rh3MhuWH+W1PAje2qF6kryUiIiJFR0kpEREpM44npvP9hkhmbjrO2fQcADxcrQxqUZVhnWrQuEqAkyMUEUfo2ySUj5cfZtn+ODJzbKoDJyIiUkopKSUiIqWaYRisOXSab9YdY8neWP6aoUe1il7c26EGd7UNp4K3CvSKlCVNqwZQtYIXJ89msOJAPH0ahzo7JBEREbkKSkqJiEiplJqVy09bT/DN2mMcjk/LO39d3UCGd6pJz4hgXKyqg5In4wwsHgd7f4HA+lCrC9TsAuHtwM3L2dGJFIrFYqFvk1C+XH2UhbtjlJQSEREppZSUEhGRUuVIfCrfrotk9pYTpGblAuDj7sJtrasxrGMN6gb7OTnCEujPn+H3ZyE11jw+vt7cVr4NLu5Qra2ZoKrVBaq2ATdP58YrUgDnklKL98aSnWvH3VV14kREREobJaVERKTEs9kNlu+P45t1kaw8EJ93vnagD8M61uC21tXw89TiFBdIjobfn4F9v5rHlevBDa9CWjwcWw1HV0HKKYhcY24r3gBXTzNJVaurmaiq2hpcNf1RSp7W1SsS5OdBfEoWaw8n0L2BVuETEREpbUpEUuqjjz7i7bffJiYmhubNm/Phhx/Srl27i7bt3r07K1asuOD8jTfeyG+//VbUoYqISDFKSs/hh83H+W59JFGJ6QBYLNCzQTDDO9XkurqBWDVF70J2O2z7Fha9BFlJYHWF656CLs+cHwXVahgYBiQegaMrzSTVsVXmaKpjq8wNwNULqrf/ayRVV6jSElyUABTns1ot3NAohKkboli4J0ZJKRERkVLI6UmpmTNnMmbMGD799FPat2/P+++/T58+fdi/fz/BwRd2Ln766Seys7Pzjk+fPk3z5s254447ijNsEREpQnujk/l23THmbDtJZo4dAH9PV+5qG869HWpSvbK3kyMswRIOwS9PQuRq87hqaxjwIYQ0vrCtxQKV65hbmxFmkirhIBxbeX4kVXoCHFlubgBuPlC9w/maVGEtwMXp3Qkpp/o1CWPqhigW7Ynlv4MM1ZETEREpZSyGYRjODKB9+/a0bduWyZMnA2C32wkPD2fUqFH8+9//vuL977//Pi+99BLR0dH4+PhcsX1ycjIBAQEkJSXh7+9/zfGLiIhj5NrsLPozlilrj7HxaGLe+YhQP4Z3qsmgFlXxctey75dky4G1k2D5m2DLAjdv6PkitH8ErFf5fTMMiN9nJqeOrTITVRmJ+du4+0GNjudrUoU2u/rXK6HUdzCVxO9Djs1O29f+4Gx6DtMf6kDHOpWdHZKIiIhQ8H6DUz/azM7OZsuWLYwdOzbvnNVqpXfv3qxbt65Az/jyyy8ZPHhwgRJSIiJS8iSkZjFjYxRTN0QRnZQJgIvVQp/GIQzvWJN2tSphsWj0w2Wd3ArznoDYXeZxnZ5w8/tQsca1PddigeCG5tb+YXNaYNyf5xNUx1ZD5lk4uMjcADwCoEan8yOpQpqAVQWopWi4uVjp3TCE2VtOsHBPjJJSIiIipYxTk1IJCQnYbDZCQkLynQ8JCWHfvn1XvH/jxo3s3r2bL7/88pJtsrKyyMrKyjtOTk6++oBFRMRhdhw/yzdrj/HrzmiybeYUvco+7gxpV52hHaoTFuDl5AhLgex0WP46rPsIDDt4VYS+b0Czu8yEkqNZrRDaxNw6PAp2G8Tu/msk1WqzWHpWEhyYb24AnhWg5nXnR1IFNVSSShyqX5NQZm85wYLdMbx0cyPVmRMRESlFSnURiC+//JKmTZtesig6wIQJE3jllVeKMSoREbmUrFwbv++KZsraSHYcP5t3vnl4BYZ3rMFNzcLwcC1bU7+KzOFl8OtoOHPMPG5yu5mQ8g0qvhisLhDW3Nw6PW4mqaJ3mCOpjq6CqHXmSKp9v55fAdC7MtTofH51v6AGRZNAk3Kjc91AfD1ciUnOZMeJs7SsXtHZIYmIiEgBOTUpFRgYiIuLC7GxsfnOx8bGEhoaetl709LSmDFjBuPHj79su7FjxzJmzJi84+TkZMLDw68+aBERKbSYpEymbohk+sYoElLNxSrcXazc3CyMYZ1q0iK8gnMDLE3SE2HRi7D9e/PYvxrc/C7U7+PcuMBMUlVtZW6dnwRbLkRv/2t1v1UQtR7ST8PeeeYG4BN0fiRVzS4QWE9JKikUTzcXekQE88uOUyzYHaOklIiISCni1KSUu7s7rVu3ZsmSJQwaNAgwC50vWbKExx9//LL3zpo1i6ysLO65557LtvPw8MDDw8NRIYuISAEZhsGmY2f4Zu0xFuyJwWY319UI9fdkaPvqDG5XnSA//XwuMMOAP+fC789BWhxggXYPQa+XwMPP2dFdnIsrVGtjbl3GmMXYT241V/c7ugqOb4C0eNgzx9wAfEPNJNW5mlSVaitJJVfUr0kov+w4xfzdMfy7X4Tq0ImIiJQSTp++N2bMGIYPH06bNm1o164d77//PmlpaYwYMQKAYcOGUbVqVSZMmJDvvi+//JJBgwZRubIKWoqIlCQZ2TZ+3n6Sb9ZFsjf6fB2/drUqMbxjTW5oHIKbi2oKFUrSSfj9Gdj/u3kc2AAGfAjV2zs3rsJycTNjrt4euj4LuVlwcsv51f2Ob4TUGNg929wA/KqcT1DVvA4q1lSSSi7QvUEQHq5WohLT2RudQqMqJWN1QBEREbk8pyel7rrrLuLj43nppZeIiYmhRYsWLFiwIK/4eVRUFNZ/FETdv38/q1evZtGiRc4IWURELiLqdDrfb4hk5qbjJGXkAODpZuWWllW5t0NN/SfxatjtsOUrWPwyZKeA1Q26PG2OOnItA6PMXD3MlfpqdAL+BTmZcGLT+ZpUJzZByinYOdPcAALCzxdNr9kFKmhKvoC3uyvd6gex6M9YFuyO1s8bERGRUsJiGIbh7CCKU3JyMgEBASQlJeHvrw6LiMi1sNkNVh6I5/v1kSzdH8e53yjhlbwY1qEmd7SpRgVvd+cGWVrFH4BfnjCLhQNUa2uOjgpu6Ny4ilN2OpzYeH4k1cktYM/N36ZCDTNBVb8fNLy5SMJQ38FU0r8Pc7ad4KmZO6gX7MviMd2cHY6IiEi5VtB+g9NHSomISOkTn5LFD5uPM21DFCfPZuSd71IvkPs61aR7g2BctCz71cnNhrUfwIq3wJYNbj7Qexy0fdAsJF6euHtD7e7mBpCdZhZLPzeS6tQ2OBsJ2yIhJ6PIklJSOvSMCMHNxcLBuFQOxaVSN9jX2SGJiIjIFSgpJSIiBWIYBuuPJPL9hkgW7o4h96/C5QFebtzeuhp3t69OnSD9J/CanNgC80ZB3B7zuO715sp6Fao7N66Swt0H6vYyN4CsFDNJdXQlVO/g3NjE6QK83OhUJ5AVB+JZuCeGusF1nR2SiIiIXIGSUiIicllJ6TnM3nqCqRsiORKflne+ZfUKDG1fg5ubheFpZEF2KqCk1FXJToOlr8GGT8Cwg3dl6PsmNL1dRb0vx8MP6l1vbiKYq/CtOBDP/N3RjOyhpJSIiEhJp6SUiIhcwDAMdpxI4vv1kfyy4xRZuXYAvN1dGNSyKne3q06TqgFmYeqNH8HKdyArCcKaQ8MB0GggBNZz8rsoJQ4tgV9Hw9ko87jZXdBnAvhodVmRwrq+UQjPz9nF7pPJHE9MJ7ySt7NDEhERkctQUkpERPKkZeUyb8cpvl8fyZ5TyXnnI0L9GNqhBoNaVMHP0w0MA3bNhiWvnE+mAETvMLelr0Jwo/MJquCGGvHzT+mJsPB52DHdPA4Ih5vfh3q9nRqWSGlW2deDdrUqsf5IIgv3xPBgl9rODklEREQuQ0kpERFhf0wK36+PZM62k6RmmaububtaublpGEM7VKdV9YpYziWVItfCov+YK6EB+IVBz/9AvRtg/3z482c4ugLi/jS3FW9A5bpmcqrhAHM0VXlOUBkG7P4R5v8L0hMAC7T/P/N76KHpjyLXql+TMNYfSWT+biWlRERESjolpUREyqnMHBsLdsfw/fpINkeeyTtfs7I3Q9vX4LbW1ajk437+hoRD8Mc42PereezmA9c9BR0fMwtQA7Qebm4ZZ2D/AjNBdXgpnD4EqyaaW4Ua0GgANBwIVVuD1VqM79rJzh6H356GgwvN46CGMOBDCG/r3LhEypA+jUMZN28PWyLPEJecSbC/p7NDEhERkUtQUkpEpJw5lpDG9I1R/LD5OGfScwBwsVq4oVEIQ9vXoFOdylitfxvJlHYaVrwJm78Eey5YrNBqOHQfC34hF38Rr4rQYoi5ZSbDwUVmgurgYjgbCWs/NDf/qtCwvzmKKrw9WF2K4TvgBHY7bPrCnO6YnQou7tD1Weg8Glzdr3i7iBRcaIAnLatXYFvUWRbuieHejjWdHZKIiIhcgpJSIiLlQK7Nzh9745i6IZJVBxPyzocFeDKkXXXuahtOyD9HE+RkwoZPzdFNWX/Vl6rXB65/xawRVVCe/uYqck1vN1eZO/QH/DkPDiyA5JPma2z4FHyCoeHNZoKqxnXgUkZ+RcXtg1+egOMbzOPw9tB/EgRHODcukTKsX5NQtkWdZYGSUiIiIiVaGenxi4jIxUQnZTBj43FmbIoiNjkLMMs5da0XxD0datCjQRCuLv+YPme3mzWPlrwCScfNc6FN4Yb/Qu3u1xaQu4+ZdGo00Ex6HVlmjqDa/zukxcHmr8zNqxJE3GS2q9WtdI4mys2G1e/BqnfAlg3uvtD7ZWjzQPmasijiBH0bh/H67/tYfySRM2nZVPQphT9DREREygElpUREyhi73WDVoQSmro9kyb44bHYDgMo+7tzZNpwhbatTvfIllkk/ttosYn5qm3nsVwV6vQTN7nJ8IsXNExr0M7fcbDi20kxQ7fsN0k/Dtu/MzSPAbNNoANTpCW5ejo2jKBzfCPOegPi95nG9PnDzuxBQzblxiZQT1St70yjMnz+jk1n8Zyx3tg13dkgiIiJyEUpKiYiUEadTs5i15QTTNkQRlZied75drUrc06EGfRqH4OF6iZpNCQdh8TjY/5t57O5rFjHv8Bi4XyKB5Uiu7lC3t7nd9B5EroG982DvL5AaCztnmJu7r7nKX6OBUO/68wXWS4qsVFgyHjZ+DhjgHQj93oQmt5XvFQdFriQ7DbZMgaAIqNvLIY/s2ySUP6OTWbAnRkkpERGREkpJKRGRUswwDDYdO8PUDZHM3xVDts0OgJ+nK7e1qsbQ9tWpF+J36QekxsOKN2Dz12DYwOICre+D7v8G3+DieRP/5OIKtbuZW7+3zFFHe+eZdaiST8Cen8zN1Qvq9TZX8avfx6xd5UwHF8OvT52f8tj8bujzGnhXcm5cIqXBmknmz6IqLc0RkQ5I4vZrEsq7iw+w+mACKZk5+Hm6OSBQERERcSQlpURESqHkzBzmbD3J1A2RHIhNzTvfrFoAQ9tXp3/zKni7X+ZHfE4GrP8YVr0H2Snmufr9zCLmQQ2KOPpCsLpAjY7m1ud1OLkV/pxrJqnOHDNHUu39xVzNrk5PaDjAnOpXnImgtARYMBZ2/WAeV6gO/T8w4xGRgmn3EKz5wJw6fHipQ0ZL1Qvxo06QD4fj01i6L46BLao6IFARERFxJCWlRERKkd0nk/h+fSQ/bz9FRo4NAE83KwObV2Voh+o0q1bh8g+w283kyZJXzVFHAGHNzSLmtboWbfDXymKBaq3N7frxELPLrEH1589w+qC5mt+BBWB1Nd9LwwEQcTP4BhVNPIYBO3+ABf+GjESwWM3pjj2eL3nTCkVKOp9AaDPCTJavfMehU/g+WnaYBbtjlJQSEREpgSyGYRjODqI4JScnExAQQFJSEv7+Tp7qISJSABnZNn7ZcYqpGyLZcSIp73y9YF+Gtq/OLa2qEeBVgGkpR1eaRcyjd5jH/tXMIuZN7yjdq8EZBsTvM6f3/fkzxO05f81ihRqdzRpUETeDf5hjXvNslDlV79Af5nFwYxjwoZkwkzJHfQdTkX8fkk/BB83N1SpHzIcana75kbtPJnHzh6vxcnNh64vX4+V+ibp6IiIi4lAF7TdopJSISAl1KC6F79dH8ePWE6Rk5gLg5mKhX5Mw7ulQg7Y1K2IpSN2V+P2w+CVzFBGAux90GQMdHi0dK9ldicUCwQ3Nrfu/4PTh8yOoorfDsVXm9vuzEN7eXMWv4QCocBWFj+022Pg/s5h5Thq4eEC356Dzk+CiejUi18S/CrS42yx4vvIduPena35k4yr+VKvoxYkzGaw4EEffJg5KTIuIiIhDKCklIlKCZOfaWbgnhu/XR7LhaGLe+fBKXtzdrgZ3tKlGoK9HwR6WGgfLJ8CWb84XMW9zv1nE3CewiN5BCVC5jpl06zIGzkSeL5J+YiMcX29uC5+HKq3OJ6gq17nyc2P/hHmj4ORm87h6JxgwCQLrFe37ESlPOo+Grd/B4SVmDbmqra7pcRaLhb6NQ/li9VEW7I5RUkpERKSEUVJKRKQEOJ6YzvSNUfyw+TgJqdkAWC3Qq2EIQ9tXp2u9IKzWAq5GlZ0O6z+C1e9D9l9F0BvcZBYxL28JlIo1oNMoc0s6Cft+NRNUkWvg1FZz++NlCGlqTvFrNODCQu+5WbBqIqx6F+w55kiz61+B1iNK97RHkZKoUi1zSvHOGea/u8FTr/mR/ZqaSakle+PIyrXh4aopfCIiIiWFklIiIk5isxss2xfH9xsiWXEgnnMV/oL9PBjcrjqD24ZTpUIhptfZ7eZ/5Ja8CimnzHNVWsINr0HNzo5/A6VNQFVo/4i5pcb9laD6GY6ugthd5rbsvxAUYY6eajQAstNg3hOQsN98RoMb4aaJ5jQjESkaXcbAzpnmv9HYPyGk0TU9rmV4RYL9PIhLyWLtodP0iAh2UKAiIiJyrZSUEhEpZnHJmczcdJzpG6M4lZSZd75LvUCGtq9Or4YhuLkUcgTOkeVmEfOYXeZxQHXoPQ4a36rRPBfjG2xOZWxzP6Qnwr7fzGl+h5eZRdPj98HKt8639wmGG982R1MVpI6XiFy9oAbQsL/5b3L1u3DbF9f0OKvVQp/GoXy3PpIFu2OUlBIRESlBlJQSESkGNrvBygPxTN8YxZJ9cdjs5rCoit5u3NEmnCHtqlMr0KfwD47baxYxP7jIPPYIgK5PQ7tHwM3Tge+gDPOuBK3uNbeMs3Bgofmf4UN/QG4mtLwHrn/VbCcixaPrM+a/w90/QvexBav7dhn9mphJqUV/xvCarQmuhU38i4iISJFQUkpEpAidPJvBD5uOM2vz8XyjolrXqMg9HarTr0kYnm5XUd8kJRaWvw5bvwXDDlZXaPsgdH0OfCo78B2UM14VoPld5paVaialynJReJGSKqw51LvBTLivfg8GTr6mx7WrVYmK3m6cSc9h47FEOtXRv2sREZGSQEkpEREHy7HZWbI3jhmbovLViqrg7catLasxuF049UP8ru7h2Wmw7q8i5jlp5rmG/aH3K9c8kkD+wcPX3ETEObo8YyaldsyAbv+CCuFX/ShXFyvXNwrhh80nWLA7RkkpERGREkJJKRERB4k8ncaMTceZveUE8SlZeec71q7M4Hbh9GkcenWjogDsNtg+DZa9BinR5rmqrc0i5jU6OiB6EZESpnp7qNkFjq2CtZPMum7XoF+TsLyk1Mv9Gxd8RVMREREpMkpKiYhcg6xcGwv3xDJjYxRrD5/OOx/o68HtratxV9vwq6sV9XeHl8KiFyF2t3lcoTr0ftksYq6i2yJSlnV9xkxKbf0Wuj5rLlJwlTrVrYyfhytxKVlsO36W1jUqOjBQERERuRpKSomIXIVDcSlM33icn7ae4Ex6DmDmh7rWC2JIu/CrW0Hvn2L3mEXMD/1hHnsGmP8pa/cwuHpc4zsQESkFanWDqm3g5GZYNxmuH3/Vj/JwdaFnw2B+3n6KBbujlZQSEREpAZSUEpGSyzAgPdHM9nhWAKtzV0vKyLbx265oZmyMYnPkmbzzYQGe3NEmnDvbVKNaRe9rf6HkaHOa3vapfxUxd4N2D5kJKa0AJyLlicVijpaaPhg2fQmdR1/Tz8F+TUL5efsp5u+O4fkbG2LRaFMRERGnUlJKRJzPlgNnjkHCQUg48LevByDzrNnG4gLelc2V0HwCwfvvXyv/9TXo/Dmvig5LYu05lcSMjceZu/0kKZm5ALhYLfSMCGZIu3C61Q/GxRG1SbJSYe2HZu2UnHTzXKOB0GucipiLSPlVvy+ENIXYXbDhM+gx9qof1bV+EJ5uVk6cyWDPqWSaVA1wYKAiIiJSWEpKiUjxyTgLpw+dTzidSz4lHgF77uXvNWyQFmduBWGxglelvyWqKudPZv0zseVdCazni5CnZuUyb/spZmyKYueJpLzz4ZW8GNy2Ore3rkaIv+dVfBMuwm6Dbd+bo6NSY81z1dqaRcyrt3fMa4iIlFYWC3QZA7NHwIZPodPj4HF1K5h6u7vSvX4wC/bEsGB3jJJSIiIiTqaklIg4lt0OScfzj3Y6t3+5hJKbN1SuC4H1/9rqmV8r1zETTGkJkJ7w19fT5te0+L/Onf7btQTITDKnvaX/dRxfkMAtGN6VyHSvSHS2DwfTPLHbfOmFP63dAqhWLZx2jerTuG4wVl8/8HbAj0/DgENLYPGLEPenea5iTbOIeaNBKmIuInJOo4FQuR6cPmhO47tu9FU/ql/TUBbsiWH+7mie6dPAcTGKiIhIoSkpJSJXJzvtr1FP/0g+nT4EuZmXvs8v7HzC6e/JJ78ql59uF1DV3AoiN9tMXF0yifW3c+kJkHEGMLCkn8Yr/TS1gdoW8v+EjP5rW/K3c14V/zba6u8jsS4yOsu7Mri6n783Zpe5ot6RZeaxZwXo9hy0fVBFzEVE/snqYo6WmvuoWfC8/SPg5nVVj+oREYybi4XD8WkcikuhbvDVjboSERGRa6eklIhcmmFASsyFI54SDkLyiUvf5+IOlepcmHyqXBc8/Ys+bld38A8zt0swDIONRxOZsek4i3Ydxzs3mUqWZEJdU+lV3YWuVS3U8EzHcrEkVnoiYJjJrIwz5if3BeEZYCaoPAPg1DbzGVY38z9XXZ8xk1wiInJxTe+A5RPgbBRs/db82XkV/D3duK5uIMv2xzN/VwyjeikpJSIi4ixKSokI5GaZdZ0uSD4dguyUS9/nHfhX4ukfyacKNfLVZypJTqdm8ePWE8zYdJwj8Wl/nbUSHhrOkHbVGdSiKgHebpd/iN1mJqb+PmUwL3EVf5Ek1mlzOmFmkrmd0/gWs4h5pVpF9n5FRMoMFzfo/CT89jSs+QBaj8g/ArUQ+jUJM5NSu2MY1auegwMVERGRglJSSqQ8STv9t8TT35JPZyPNpMnFWFzMpEnliySfrmFZ7uJktxusPXya6RujWPRnDDk2AwBvdxcGNK/C4HbVaV4toOBLg1tdwDfI3AoWgDmi6u9JrEq1IbTpVb4jEZFyqsU9sOJtSD4JO6ZD6+FX9ZjejUJwmWPhz+hkok6nU72yt4MDFRERkYJQUkqkrLHlmkmmiyWfMs5c+j4P/7/VePpb8qlirav+JNrZYpMzmb3lBDM2RXE8MSPvfPNqAQxuV53+zavg61EMPwatVvCpbG5BKqorInLV3Dyh0yhY9AKsfg9aDAWXwv8cr+TjTvtalVh7+DQL9kTzcNc6RRCsiIiIXImSUiJlxZlI+OlhOLkF7DmXaGSBCuF/rWr3j+STb3CZWO3NZjdYvj+O6RuPs2x/HDa7OSrKz9OVW1pWZXDb6jSqUgx1rUREpGi0GQGrJsKZo7BnDjS746oe07dJKGsPn2b+7hglpURERJxESSmRsmLh83B8vbnv5m0WFf/nyKdKdcC9bE5ROHEmnR82HeeHzSeIST6/+l/bmhUZ3LY6NzYNw8u9ZNa5EhGRQnD3gQ6PwbL/msmpJrddfvXWS+jTOJSXft7DtqizxCRlEhrgWQTBioiIyOUoKSVSFpzYAvt+BYsV7l8EVVtfVQe9tMmx2VmyN5bpG4+z8mA8hjkoiorebtzWqhqD24VrqW8RkbKo3UOwdhLE74X9v0HD/oV+RIi/J61rVGRL5BkW7olheKeajo9TRERELktJKZGyYMkr5tfmQyC8rXNjKQbHEtKYsek4s7ecICE1K+9857qVGdy2Ojc0DsHDVaOiRETKLK8KZmJq1URY+Q5E3HxVU9D7Ng5lS+QZFuxWUkpERMQZlJQSKe2OLIejK8DFHbr/29nRFJnMHBsL98QwY+Nx1h05nXc+yM+DO1pX46624dSo7OPECEVEpFh1eAzWfwLR2+HwEqjbu9CP6NsklNd+38uGo6c5nZpFZV8Px8cpIiIil6SklEhpZhiwZLy53+Z+qFDdufEUgUNxKUzbcJyftp3gbLpZwN1ige71gxjcrjo9I4Jxcyn7UxVFROQffAKh9QhY/5E5WuoqklLhlbxpUtWf3SeTWfxnLIPblb3foyIiIiWZklIipdm+X83V9tx8oMszzo7GoZLSc3hr4T6mbYzKqxVVJcCTO9uGc2ebcKpU8HJugCIi4nydRsGm/0HUOji2Bmp2LvQj+jYOZffJZBbsiVFSSkREpJgpKSVSWtltsPS/5n7Hx8A3yLnxOIhhGMzZdpLXf99LQmo2AL0bhjC0Q3W61gvCxVr4miEiIlJG+YdBi6Gw5WtY9c7VJaWahPHOogOsOZRAUkYOAV5uRRCoiIiIXIzmvIiUVjtnQvw+8KpoflJcBhyKS2HI/9Yz5ocdJKRmUzfYl+kPdeCL4W3o0SBYCSkREWDChAm0bdsWPz8/goODGTRoEPv377/sPVOmTMFiseTbPD09iyniInbdaLC4wOGl5ujhQqob7Eu9YF9ybAZL98U6Pj4RERG5JCWlREqj3CxYNsHcv+4p8AxwbjzXKCPbxlsL9tHvg1WsP5KIp5uVZ/s04PcnutCxTmVnhyciUqKsWLGCkSNHsn79ehYvXkxOTg433HADaWlpl73P39+f6OjovC0yMrKYIi5iFWtCszvN/ZUTr+oRfZuEArBgd4yDghIREZGC0PQ9kdJoyxRIigK/MGj3sLOjuSZL9sYybt4eTpzJAKBXRDAvD2hMeCVvJ0cmIlIyLViwIN/xlClTCA4OZsuWLXTt2vWS91ksFkJDQ4s6POe4bgzsmAH7f4PYPRDSuFC3920SyodLD7HiQDzp2bl4u6uLLCIiUhw0UkqktMlKhZVvm/vdngO30lnw++TZDB7+djMPfLOZE2cyqBLgyWf3tuaL4W2UkBIRKYSkpCQAKlWqdNl2qamp1KhRg/DwcAYOHMiePXuKI7ziEVQfGg0w91e9W+jbG4X5E17Ji8wcO8v3xzs4OBEREbkUJaVESpsNn0BaPFSqDS3vdXY0hZZjs/PZisP0nriCRX/G4mq18EjX2iwe040+jUOxWFQ3SkSkoOx2O6NHj6Zz5840adLkku0aNGjAV199xc8//8z333+P3W6nU6dOnDhx4pL3ZGVlkZycnG8r0c6tQrvnJzh9uFC3WiwW+jUJAzSFT0REpDgpKSVSmqQnwpoPzf0eL4BL6VohaNOxRG6etJoJ8/eRkWOjbc2K/PZEF8be2BAfD02VEBEprJEjR7J7925mzJhx2XYdO3Zk2LBhtGjRgm7duvHTTz8RFBTEZ599dsl7JkyYQEBAQN4WHh7u6PAdK6wZ1OsDhh1WF3601Lm6Ukv3xZGVa3N0dCIiInIRSkqJlCZr3oesJAhpAo1vdXY0BZaYls2zs3Zwx6fr2B+bQkVvN966vRkzH+5Ig1A/Z4cnIlIqPf744/z6668sW7aMatWqFepeNzc3WrZsyaFDhy7ZZuzYsSQlJeVtx48fv9aQi17Xv0ZL7ZgBZwsXb4tqFQjx9yA1K5fVBxOKIDgRERH5JyWlREqL5GjY8Ncn2j1fBGvJ/+drtxvM2BhFz4nLmbXFnCIyuG04S5/uzp1twrFaNVVPRKSwDMPg8ccfZ86cOSxdupRatWoV+hk2m41du3YRFhZ2yTYeHh74+/vn20q88HZQqyvYc2HNB4W61Wq10LexVuETEREpTiX/f7UiYlr5FuRmQnh7qN/H2dFc0d7oZO74bB3//mkXZ9NziAj148dHO/LGbc2o6OPu7PBEREqtkSNH8v333zNt2jT8/PyIiYkhJiaGjIyMvDbDhg1j7Nixecfjx49n0aJFHDlyhK1bt3LPPfcQGRnJgw8+6Iy3ULTO1Zba+i2kxBbq1r5/1ZVavDeWHJvd0ZGJiIjIP6iIi0hpkHjE7FwD9BoHJbgYeGpWLu8vPsDXa49hsxv4uLvw1PX1ua9TTVxdlAcXEblWn3zyCQDdu3fPd/7rr7/mvvvuAyAqKgrr30bUnjlzhoceeoiYmBgqVqxI69atWbt2LY0aNSqusItPra5QrS2c2ATrJsMNrxb41rY1K1LJx53EtGw2Hk2kc93AIgxURERElJQSKQ2WvW5ORajbG2p2dnY0F2UYBgt2x/DKL38Sk5wJQL8mobzUvxFhAV5Ojk5EpOwwDOOKbZYvX57v+L333uO9994roohKGIsFuj4L0+6ETV/CdU+Bd6UC3erqYuWGRiHM2HSc+bujlZQSEREpYk4ftvDRRx9Rs2ZNPD09ad++PRs3brxs+7NnzzJy5EjCwsLw8PCgfv36/P7778UUrYgTxOyGXbPN/V4vOTeWS4g6nc6IKZt4dOpWYpIzCa/kxdcj2vLJPa2VkBIRkeJX7wYIbQo5abDh00Ldem4VvoV7YrHbr5wAFBERkavn1KTUzJkzGTNmDOPGjWPr1q00b96cPn36EBcXd9H22dnZXH/99Rw7dozZs2ezf/9+/ve//1G1atVijlykGC19FTCg8S0Q1tzZ0eSTlWvjwyUHuf69FSzfH4+bi4VRPeuy+Klu9GgQ7OzwRESkvLJYoMvT5v6GTyEzucC3dqoTiJ+nK/EpWWyNOlNEAYqIiAg4OSn17rvv8tBDDzFixAgaNWrEp59+ire3N1999dVF23/11VckJiYyd+5cOnfuTM2aNenWrRvNm5es/6iLOEzUejiwACwu0OM/zo4mn7WHEuj3wSomLj5AVq6dTnUqs2B0V56+oQGebi7ODk9ERMq7hgOgcj3ITILNXxb4NndXK70bhgAwX6vwiYiIFCmnJaWys7PZsmULvXv3Ph+M1Urv3r1Zt27dRe+ZN28eHTt2ZOTIkYSEhNCkSRNef/11bDbbJV8nKyuL5OTkfJtIqWAYsGS8ud9yKATWdW48f4lLyeTJGdu4+4sNHIlPI9DXgw8Gt2Dqg+2pE+Tr7PBERERMVpfzo6XWTobs9ALfem4K34LdMQWq4SUiIiJXx2lJqYSEBGw2GyEhIfnOh4SEEBNz8U+ljhw5wuzZs7HZbPz++++8+OKLTJw4kf/+97+XfJ0JEyYQEBCQt4WHhzv0fYgUmUNLIHINuHhAt387OxpsdoNv1x2j18QV/Lz9FBYLDOtYgyVPd2Ngi6pYSvCKgCIiUk41vR0qVIf0hPOr2BZA13pBeLm5cPJsBrtP6gNNERGRouL0QueFYbfbCQ4O5vPPP6d169bcddddvPDCC3z66aULWI4dO5akpKS87fjx48UYschVstthySvmfruHIMC5ddN2njjLLR+v4aWf95CSmUvTqgH8PLIz4wc2IcDLzamxiYiIXJKLG3Qebe6vnQS5WQW6zcvdhR4RQQDM3x1dRMGJiIiI05JSgYGBuLi4EBsbm+98bGwsoaGhF70nLCyM+vXr4+Jyvl5Nw4YNiYmJITs7+6L3eHh44O/vn28TKfH+nAsxO8HdD64b47QwkjNzeOnn3Qz8aA07TyTh5+HK+IGNmTuyM82qVXBaXCIiIgXWYij4hUHySdgxvcC39WmsKXwiIiJFzWlJKXd3d1q3bs2SJUvyztntdpYsWULHjh0vek/nzp05dOgQdrs979yBAwcICwvD3d29yGMWKRa2XFj2mrnf6XHwqVzsIRiGwc/bT9LznRV8uy4Sw4CBLaqw5JluDOtYExerpuqJiEgp4eYJnUaZ+6vfM3/PFkDPiGDcXawcSUjjYFxqEQYoIiJSfjl1+t6YMWP43//+xzfffMPevXt59NFHSUtLY8SIEQAMGzaMsWPH5rV/9NFHSUxM5Mknn+TAgQP89ttvvP7664wcOdJZb0HE8bZPhdOHwLsydCz+v9uH41O558sNPDljOwmpWdQO9GHqg+35YHBLgv08iz0eERGRa9b6PvP36pljsOenAt3i5+lGl3qBAMzfpVX4REREioKrM1/8rrvuIj4+npdeeomYmBhatGjBggUL8oqfR0VFYbWez5uFh4ezcOFCnnrqKZo1a0bVqlV58skn+de//uWstyDiWDmZsOJNc7/L0+DhV2wvnZlj4+Nlh/h0xRGybXY8XK083qMuD3erjYery5UfICIiUlK5+0CHx2Dpq7BqIjS5HaxX/my2T5NQluyLY/7uaJ7sXa8YAhURESlfLEY5mySfnJxMQEAASUlJqi8lJc+6j2Dh8+BfDUZtMaccFINl++MY9/MeohLN5bK7Nwhi/IAmVK/sXSyvLyJSkqnvYCr134fMJHivKWQlwZ3fQaMBV7zlTFo2bV77A5vdYPkz3akZ6FMMgYqIiJR+Be03lKrV90TKtKwU89NbgO7/KpaEVHRSBo9+v4URX28iKjGdUH9PPhnaiq/va6uElIiIlC2eAeaKtgCr3oECfC5b0cedjrXN2o4L9mgKn4iIiKMpKSVSUqz7CNJPQ+V60PzuIn2pXJudL1YdoffEFczfHYOL1cKD19Xij6e70a9pGBaLCpmLiEgZ1OExcPOG6B1waMmV22NO4QNzFT4RERFxLCWlREqCtNOwdrK53/MFcCm6cm9bIs9w84er+e9ve0nLttGqegV+efw6/nNzI3w9nFpmTkREpGj5VIY295v7K98u0GipPo1DsFhg+/GzRCdlFHGAIiIi5YuSUiIlwep3ITsFwppDw4FF8hJn07MZ+9NObvtkLftiUqjg7cYbtzZl9v91olGVUlgbRERE5Gp0fBxc3OH4eohcc8XmwX6etKlREdBoKREREUdTUkrE2ZJOwMb/mfu9XirQakCFYRgGszYfp+fEFUzfeByAO1pXY8mYbgxuVx2rVVP1RESkHPEPg5b3mPsr3ynQLX0aawqfiIhIUVBSSsTZVrwJtiyocR3U6eXQR++PSeHOz9bx7OydJKZlUz/Elx8e6cjbdzSnsq+HQ19LRESk1Og8GiwucGQZnNhyxeZ9/6ortelYIgmpWUUcnIiISPmhpJSIMyUcgm1Tzf3e48BBBcbTs3OZMH8vN01axaZjZ/Byc2Fsvwh+e6IL7WpVcshriIiIlFoVa0Czu8z9VVceLVWtojdNqwZgN2DRntgiDk5ERKT8UFJKxJmW/RcMG9TvB+HtHPLIRXti6D1xBZ+tOEKu3eCGRiH88XQ3HulWBzcX/ZMXEREBoMsYwAL7f4eY3Vdsfm601II9msInIiLiKPofqoiznNoOe+YAFuj14jU/LjY5kwe/2cTD323hVFImVSt48cWwNnw+rA1VK3hd8/NFRETKlMB60HiQub9q4hWb9/srKbX2UAJJ6TlFGJiIiEj5oaSUiLMsfdX82vQOCGl8bY/aF0u/D1bxx944XK0WHutehz/GdKN3oxAHBCoiIlJGdXna/Lpnjjml/jJqB/lSP8SXXLvBH3s1hU9ERMQRlJQScYZja+DQH2B1hR5jr/oxWbk2XvllD/dP2UxiWjaNwvz5/ckuPNc3Ai93FwcGLCIiUgaFNoX6fQEDVr93xeZ9m4QBmsInIiLiKEpKiRQ3w4Alr5j7rYZBpdpX9ZhDcanc8tFavl5zDIARnWsyZ2Qn6of4OShQERGRcqDLM+bXnTPgbNRlm56bwrfyQDxpWblFHZmIiEiZp6SUSHE7sBCObwBXL+j6XKFvNwyDHzYdp/+Hq/kzOplKPu58ObwN4/o3xsNVo6NEREQKJbwt1OoG9lxY88Flm0aE+lGjsjdZuXaW7Y8rpgBFRETKLiWlRIqT3X6+llT7h8E/rFC3J2fmMGr6Np77cScZOTY61anM/Ce70KuhakeJiIhcta5/jZba+h2kXHpqnsViOb8K325N4RMREblWSkqJFKfdP0LsbvAIgM6jC3Xr1qgz3PjBKn7dGY2L1cJzfRvw3QPtCfH3LJpYRUREyouaXaBaO7BlwbrJl23a76+6Usv2xZGZYyuO6ERERMosJaVEiostB5a9Zu53HgXelQp2m93go2WHuOPTdZw4k0F4JS9m/V9HHuteFxerpQgDFhERKScsFuj6rLm/6StIT7xk02ZVAwgL8CQt28aqgwnFFKCIiEjZpKSUSHHZ+i2cOQo+QdD+0QLdEpOUyT1fbODthfux2Q36N6/Cb090oVX1ikUcrIiISDlT73oIbQY5abD+k0s2s1ot9GmsKXwiIiKOoKSUSHHITocVb5n7XZ8FD98r3rJkbyz9PljJuiOn8XJz4e3bmzFpcAv8Pd2KOFgREZFyyGKBLk+b+xs/g8ykSzY9V1fqj72x5NjsxRGdiIhImaSklEhx2Pg5pMZAQHVofd9lm2bm2Hh53h4e+GYzZ9JzaFzFn1+fuI472oRjsWi6noiISJFpOAACG5gJqU1fXLJZ25qVCPR1Jykjh/VHThdjgCIiImWLklIiRS3jLKx+z9zvMRZcPS7Z9FBcKrd8vJYpa48B8MB1tfjpsU7UCbryyCoRERG5RlYrdBlj7q/72BzpfBEuVgvXNzJHS83XFD4REZGrpqSUSFFb+yFknoWgCGh210WbGIbBzE1R9P9wNXujk6ns487X97XlxZsb4eHqUrzxioiIlGdNbocKNSA9AbZ+c8lm56bwLdoTg81uFFd0IiIiZYqSUiJFKTXufLHUnv8B64UJpqSMHB6fvo1//biLjBwbnetWZv6TXegREVzMwYqIiAgurnDdU+b+mkmQm3XRZh1rV8bf05WE1Gy2RJ4pxgBFRETKDiWlRIrSynfMVXyqtoaImy+4vCUykRs/WMVvO6NxtVr4V98Ivru/PcH+nk4IVkRERABocTf4VYGUU7B92kWbuLta6d0oBID5u6OLMzoREZEyQ0kpkaJyJhI2f2Xu93rJXNXnLza7weSlB7nzs/WcPJtB9UrezH60E492r4PVqmLmIiIiTuXqAZ1Gmftr3gdb7kWb9W1sTuFbuDsGw9AUPhERkcJSUkqkqCx/A+w5UKsb1O6edzomKZN7vtjAO4sOYLMbDGhehd+euI4W4RWcFqqIiIj8Q+vh4F0ZzhyD3T9etEnX+kF4u7twKimTnSeSijc+ERGRMkBJKZGiELcPds4w93uNyzv9x5+x9PtgJeuOnMbb3YV37mjOB4Nb4Ofp5qRARURE5KLcfaDjSHN/1USw2y9o4unmklcDUqvwiYiIFJ6SUiJFYemrYNjNOlLVWpOZY2Pcz7t58NvNnEnPoUlVf34ddR23t66GxaLpeiIiIiVS2wfBIwAS9sO+Xy7a5NwUvgW7ozWFT0REpJCUlBJxtJNbYN+vYLFCzxc5FJfCoI/W8M26SAAevK4WPz7aidpBvk4OVERERC7LMwDaP2zur3wHLpJ06hERjLurlWOn09kfm1LMAYqIiJRuSkqJONqS8QAYze5ixjFvbv5wNftiUqjs487XI9ryn5sb4eHq4uQgRUREpEDaPwpuPhCzEw4uvuCyr4crXesFATB/l6bwiYiIFIaSUiKOdGQFHFmOYXXjpaQB/PunXWTm2OlSL5D5o7vQo0GwsyMUERGRwvCpDG1GmPurLj5aqm+Tc1P4lJQSEREpDCWlRBzFMGDJKwDMttzAd/sMXK0WxvaL4JsR7Qj283RygCIiInJVOo0CFw84vgGOrb7gcu+GwbhaLeyPTeFIfKoTAhQRESmdlJQScRDb3l/h5BbSDA/eTLuZGpW9mf1oJx7pVgerVcXMRURESi2/UGh1r7m/6p0LLlfwdqdjncoALNij0VIiIiIFpaSUiANEn0nl1E/PA/CVrR/XtWjIr6Ouo0V4BecGJiIiIo7R+UmwusKR5XBi8wWXz03hW6gpfCIiIgWmpJTINVq0J4aPJk0gPDeKJMOHmv3/xfuDW+Ln6ebs0ERERMRRKlSHZneZ+ysvHC11Q6NQLBbYcSKJk2czijk4ERGR0klJKZGrlJljY9zPuxn53QYesc0EwNbpSfq3b+TkyERERKRIXPcUYIED8yFmV75LQX4etK1RCVDBcxERkYIqdFKqZs2ajB8/nqioqKKIR6RUOBibwqCP1vDNukiGuCwh3BqP4RtKpR6jnB2aiIiIFJXAetD4FnN/1cQLLmsKn4iISOEUOik1evRofvrpJ2rXrs3111/PjBkzyMrKKorYREocwzCYtiGK/pNXsy8mhWo+dv7j9xsAlm7Pgru3kyMUERGRItXlafPrnrmQcDDfpXNJqU2RicSlZBZzYCIiIqXPVSWltm/fzsaNG2nYsCGjRo0iLCyMxx9/nK1btxZFjCIlQlJ6DiOnbeX5ObvIzLHTpV4gCzruwT0zASrWhJbDnB2iiIiIFLXQJlC/H2DA6vfyXapSwYvm1QIwDFi0J9Y58YmIiJQiV11TqlWrVkyaNIlTp04xbtw4vvjiC9q2bUuLFi346quvMAzDkXGKONXmY4ncOGkVv++KwdVq4fkbI/hmcD18N31sNujxAri6OzdIERERKR5dnzG/7pgBZyLzXerbJAyAhXs0hU9ERORKrjoplZOTww8//MCAAQN4+umnadOmDV988QW33XYbzz//PEOHDnVknCJOYbMbTFpykDs/W8fJsxnUqOzNj4924uGudbCu/QCykiC4MTS53dmhioiISHGp1gZqdwfDBms+yHfp3BS+dYdPczY92wnBiYiIlB6uhb1h69atfP3110yfPh2r1cqwYcN47733iIiIyGtzyy230LZtW4cGKlLcopMyGD1jOxuOJgJwa8uqjB/UBF8PV0iJgQ2fmQ17vQhWLWQpIiJSrnR5Bo4sh23fQ7fnwM9MRtUK9CEi1I99MSks/jOWO9qEOzdOERGREqzQ/5Nu27YtBw8e5JNPPuHkyZO88847+RJSALVq1WLw4MEOC1KkuC3cE0O/D1ax4WgiPu4uvHtnc969q4WZkAJY8RbkZkC1dlC/r3ODFRERkeJX8zoI7wC2LFj7Yb5LeavwaQqfiIjIZRV6pNSRI0eoUaPGZdv4+Pjw9ddfX3VQIs6SmWPjtd/28t16sz5Es2oBTBrckpqBPucbJR6Brd+Y+73HgcXihEhFRETEqSwWs7bU1Nth81dw3RjwqQyYSan3/zjIyoMJpGblnv9QS0RERPIp9EipuLg4NmzYcMH5DRs2sHnzZocEJeIMB2JTGDh5TV5C6uGutZn9f53yJ6QAlk0Aey7U6WV+SioiIiLlU93eENYcctJhwyd5pxuE+FEr0IfsXDtL98U5MUAREZGSrdBJqZEjR3L8+PELzp88eZKRI0c6JCiR4mQYBtM2RDFg8mr2x6YQ6OvBN/e34/kbG+Lu+o9/IrF7YNcsc7/XS8UfrIiIiJQcFgt0edrc3/A5ZCb9ddqSN4Vv/q5oZ0UnIiJS4hU6KfXnn3/SqlWrC863bNmSP//80yFBiRSXs+nZPPr9Vp6fs4vMHDtd6wcx/8kudKsfdPEblrwKGNBoEFRpUYyRioiISIkU0R8CG5gr8m78X97pm5qGAbBgTwyrDyY4KzoREZESrdBJKQ8PD2JjYy84Hx0djaur5stL6bHxaCI3frCKBXticHOx8MKNDZlyX1uC/DwufkPUBjgwHywu0PM/xRusiIiIlExW6/nRUus/huw0AJpUDWBw23AMA56csY3Y5EwnBikiIlIyFTopdcMNNzB27FiSkpLyzp09e5bnn3+e66+/3qHBiRSVH7ecYPDn6ziVlEnNyt78+GgnHupaG6v1EkXLDQOWjDf3W9wNgfWKL1gREREp2ZrcBhVrQvpp2PJN3umXBzQmItSP02nZjJq2jVyb3XkxioiIlECFTkq98847HD9+nBo1atCjRw969OhBrVq1iImJYeLEiUURo4hDbT6WyL9/2ondgEEtqvDrE11oVq3C5W86vAQiV4OLB3T/d7HEKSIiIqWEiytc95S5v3YS5GYB4OnmwsdDW+Hr4crGY4lMXHzAiUGKiIiUPIVOSlWtWpWdO3fy1ltv0ahRI1q3bs0HH3zArl27CA8PL4oYRRzmxJl0HvluCzk2g5uahvHunS2uvEyz3X5+lFTbByGgWtEHKiIiIqVL8yHgVwVSomH71LzTtYN8eeO2pgB8svwwy7Qan4iISJ5CJ6UAfHx8ePjhh/noo4945513GDZsGG5ubo6OTcSh0rJyeejbLZxOy6ZxFX/euaP5pafr/d3enyF6B7j7QpcxRR+oiIjIZUyYMIG2bdvi5+dHcHAwgwYNYv/+/Ve8b9asWURERODp6UnTpk35/fffiyHacsTVAzo/Ye6vfh9suXmXbm5WhWEdawDw1A/bOXk2wwkBioiIlDxXlZQCcxW+BQsWMG/evHybSElktxuM+WE7e6OTCfT14H/D2uDl7nLlG225sPQ1c7/j4+ATWLSBioiIXMGKFSsYOXIk69evZ/HixeTk5HDDDTeQlpZ2yXvWrl3LkCFDeOCBB9i2bRuDBg1i0KBB7N69uxgjLwdaDQfvQDgbCbtn57v0wk0NaVo1gLPpOTw+bSvZuaovJSIiYjEMwyjMDUeOHOGWW25h165dWCwWzt1usZgjTmw2m+OjdKDk5GQCAgJISkrC39/f2eFIMXl30X4mLT2Eu4uV6Q93oHWNigW7ceu3MG8UeFWCJ3eAp/7OiIiUNyW97xAfH09wcDArVqyga9euF21z1113kZaWxq+//pp3rkOHDrRo0YJPP/20QK9T0r8PJcaqd2HJKxBYHx7bYK7O95fjiencOGkVKZm5PHBdLV68uZETAxURESk6Be03FHqk1JNPPkmtWrWIi4vD29ubPXv2sHLlStq0acPy5cuvJWaRIvHLjlNMWnoIgAm3Ni14QionE5a/Ye53eVoJKRERuWbHjx/nxIkTeccbN25k9OjRfP7551f9zHMrIleqVOmSbdatW0fv3r3znevTpw/r1q276teVS2j7IHgGQMIB2Jt/FkF4JW8m3tEcgC9XH2XB7hhnRCgiIlJiFDoptW7dOsaPH09gYCBWqxWr1cp1113HhAkTeOKJJ4oiRpGrtvPEWZ6ZtQOAR7rW5rbWhShSvvlLSD4J/lXNDqaIiMg1uvvuu1m2bBkAMTExXH/99WzcuJEXXniB8ePHF/p5drud0aNH07lzZ5o0aXLJdjExMYSEhOQ7FxISQkzMpZMiWVlZJCcn59ukADz9od0j5v6qd+AfkxJuaBzKQ11qAfDs7B1EnU4v7ghFRERKjEInpWw2G35+fgAEBgZy6tQpAGrUqFGgIpsX89FHH1GzZk08PT1p3749GzduvGTbKVOmYLFY8m2enp5X9bpStsUlZ/Lwt1vIyrXTMyKY5/pGFPzmrBRYNdHc7/YvcNPfMRERuXa7d++mXbt2APzwww80adKEtWvXMnXqVKZMmVLo540cOZLdu3czY8YMB0dqFlQPCAjI27TKciF0eBTcfCBmFxxcdMHl5/pG0Kp6BVIyc3ls2hYyc0p2+QsREZGiUuikVJMmTdixwxx50r59e9566y3WrFnD+PHjqV27dqEDmDlzJmPGjGHcuHFs3bqV5s2b06dPH+LiLr1crr+/P9HR0XlbZGRkoV9XyrbMHBsPfbeFmORM6gX78sHgFrgUZKW9c9Z9DOmnoXJdaDG06AIVEZFyJScnBw8PDwD++OMPBgwYAEBERATR0dGFetbjjz/Or7/+yrJly6hW7fIjgUNDQ4mNjc13LjY2ltDQ0EveM3bsWJKSkvK248ePFyq+cs27ErS939xf9B84cyzfZTcXK5PvbkVFbzd2n0zmtd/2Fn+MIiIiJUChk1L/+c9/sNvN1ULGjx/P0aNH6dKlC7///juTJk0qdADvvvsuDz30ECNGjKBRo0Z8+umneHt789VXX13yHovFQmhoaN72z+HoUr4ZhsG/f9zJjuNnqeDtxhfD2+Dn6VbwB6SdhrUfmvs9XgAX16IJVEREyp3GjRvz6aefsmrVKhYvXkzfvn0BOHXqFJUrVy7QMwzD4PHHH2fOnDksXbqUWrVqXfGejh07smTJknznFi9eTMeOHS95j4eHB/7+/vk2KYSOo8AnyKwt9VlX2L8g3+UqFbx4964WAHy3PpJfdpxyQpAiIiLOVeikVJ8+fbj11lsBqFu3Lvv27SMhIYG4uDh69uxZqGdlZ2ezZcuWfIU3rVYrvXv3vmzhzdTUVGrUqEF4eDgDBw5kz549l2yregjlz6crjjB3+ylcrRY+HtqKGpV9CveA1e9CdgqENoNGg4okRhERKZ/efPNNPvvsM7p3786QIUNo3twsej1v3ry8aX1XMnLkSL7//numTZuGn58fMTExxMTEkJGRkddm2LBhjB07Nu/4ySefZMGCBUycOJF9+/bx8ssvs3nzZh5//HHHvkE5zy8EHloGVdtAZhJMvwuWjAf7+al6PRoEM7JHHQD+/eNOjsSnOitaERERpyhUUionJwdXV1d2796d73ylSpWwWAoxNeovCQkJ2Gy2QhXebNCgAV999RU///wz33//PXa7nU6dOuVbyebvVA+hfPnjz1jeWrgPgHEDGtOpTmDhHpB0Ejb+z9zv9VK+ZZxFRESuVffu3UlISCAhISHfqPCHH36YTz/9tEDP+OSTT0hKSqJ79+6EhYXlbTNnzsxrExUVlW86YKdOnZg2bRqff/45zZs3Z/bs2cydO/eyxdHFASqEw4j50O5h83jVRPhuEKTG5zV5qnd92teqRFq2jcemblV9KRERKVcshvGPJUGuoHbt2syZMyfvk71rcerUKapWrcratWvzDR9/7rnnWLFiBRs2bLjiM3JycmjYsCFDhgzh1VdfveB6VlYWWVlZecfJycmEh4eTlJSkYehlzP6YFG79eA1p2Tbu7VCDVwddRUd73hOw9Ruo3glG/A5XkWwVEZGyJTk5mYCAAIf0HTIyMjAMA29vbwAiIyOZM2cODRs2pE+fPo4It8g48vtQLu2abfYzctLALwzumALVOwAQm5zJTZNWkZCazV1twnnz9mbOjVVEROQaFbTfUOhhIC+88ALPP/88iYmJ1xQgmKv3ubi4FLrw5t+5ubnRsmVLDh06dNHrqodQPiSmZfPAN5tIy7bRsXZlXurfqPAPSTgE274393uPU0JKREQcbuDAgXz77bcAnD17lvbt2zNx4kQGDRrEJ5984uTopEg1vR0eWgqBDSAlGqbcBOs+AsMgxN+TDwa3xGKBmZuP8+OWi88AEBERKWsKnZSaPHkyK1eupEqVKjRo0IBWrVrl2wrD3d2d1q1b5yu8abfbWbJkyWULb/6dzWZj165dhIWFFeq1pezIzrXz6PdbOHEmgxqVvfl4aCvcXK5i2t2y18CwQb0+eZ9cioiIONLWrVvp0qULALNnzyYkJITIyEi+/fbbq1owRkqZ4AgzMdXkNrDnwsLnYdZwyEymc91ARveqD8B/5u7mQGyKk4MVEREpeoVeVmzQoEEODWDMmDEMHz6cNm3a0K5dO95//33S0tIYMWIEYBbqrFq1KhMmTADMFf86dOhA3bp1OXv2LG+//TaRkZE8+OCDDo1LSgfDMBg3bw8bjibi6+HKF8PaUNHHvfAPit4Be34y93u96NggRURE/pKeno6fnx8AixYt4tZbb8VqtdKhQwciIyOdHJ0UCw9fuO1LCO9gJqX+/Bli98Cd3/F4z4Zsjkxk1cEEHpu6lZ9HdsbHQ6sAi4hI2VXo33Ljxo1zaAB33XUX8fHxvPTSS8TExNCiRQsWLFiQV/w8KioK69+KTZ85c4aHHnqImJgYKlasSOvWrVm7di2NGl3FdC0p9b5dF8n0jVFYLPDhkJbUC/G7ugct+aseWZPbIbSp4wIUERH5m7p16zJ37lxuueUWFi5cyFNPPQVAXFycSgyUJxYLtH8YqrQ0R0qdPgRf9MLl5vd5765B3DRpFYfiUnlhzi7eu6vFVS0oJCIiUhoUutB5aacinWXH6oMJDP96Iza7wfM3RvBw1zpX96DItfB1P7C6wsiNUPkqnyMiImWSI/sOs2fP5u6778Zms9GzZ08WL14MmKsFr1y5kvnz5zsi5CKhPlQRSUuAHx+EI8vM4zb3s7HBswz5ejs2u8GEW5sypF1158YoIiJSSEVW6NxqteLi4nLJTaQ4HE1I47GpW7DZDW5rVY2HutS+ugcZBvzxirnf8l4lpEREpEjdfvvtREVFsXnzZhYuXJh3vlevXrz33ntOjEycxicQ7vkRuv0LsMDmr2i37G5e6WKO/h43bw97TiU5N0YREZEiUujpe3PmzMl3nJOTw7Zt2/jmm2945ZVXHBaYyKUkZeTwwDebSM7MpVX1Crx+a5OrH9Z+cBEcXw+untDtOccGKiIichGhoaGEhoZy4oS5wlq1atVo166dk6MSp7K6QI/noVo7+OlBOLWNoYn3EF/9WT6IqsXIqVv5ZdR1+Hm6OTtSERERhyp0UmrgwIEXnLv99ttp3LgxM2fO5IEHHnBIYCIXk2uzM2r6No7Ep1ElwJNP722Nh+tVjtCz28/Xkmr3MPhXcVygIiIiF2G32/nvf//LxIkTSU1NBcDPz4+nn36aF154IV8dTSmH6vWGR1bCD8OxnNrKU5kvUNHnDsafHsi/f9zF5Ltbqr6UiIiUKQ7r+XTo0IElS5Y46nEiFzVh/j5WHojHy82Fz4e1IdjP8+oftucniN0FHv5w3VOOC1JEROQSXnjhBSZPnswbb7zBtm3b2LZtG6+//joffvghL76o1V8FqFAd7l8Abc2Vpe+zzeJb9zdYt2s/367TCo0iIlK2OGSN2YyMDCZNmkTVqlUd8TiRi/ph03G+XH0UgIl3NqdJ1YCrf5gtB5b+19zv9AR4V3JAhCIiIpf3zTff8MUXXzBgwIC8c82aNaNq1ao89thjvPbaa06MTkoMVw+4aSKEd4BfnuC6nN385vE8T/z+JC3C76N5eAVnRygiIuIQhU5KVaxYMd+wYcMwSElJwdvbm++//96hwYmcs+lYIi/M3QXA6N71uLFp2NU/LCsFFoyFM0fBJwg6POqgKEVERC4vMTGRiIiIC85HRESQmJjohIikRGt2B4Q2wZh5L2GnDzLNZTwffRtFzdFvEODj7uzoRERErlmhk1LvvfdevqSU1WolKCiI9u3bU7FiRYcGJwJw4kw6//fdFnJsBjc1DeOJnvWu/mEHFsKvYyDZLC5Lr3Hg4euYQEVERK6gefPmTJ48mUmTJuU7P3nyZJo1a+akqKREC26I5eFlZM8Zifu+nxmd8yWbPj5Mm1HfYfG89BLbIiIipYHFMAzD2UEUp+TkZAICAkhKSsLfX7/IS7q0rFxu+2Qt+2JSaFzFn9n/1wkv96sobJ4aDwv+Dbtnm8cVa8LN70OdHo4MV0REyiBH9h1WrFjBTTfdRPXq1enYsSMA69at4/jx4/z+++906dLFESEXCfWhnMwwiF70HoFr/4ubxcYZ71pUvG86BDd0dmQiIiIXKGi/odCFzr/++mtmzZp1wflZs2bxzTffFPZxIpdktxuM+WE7+2JSCPT14H/D2hQ+IWUYsGMGfNTWTEhZrNBpFDy6TgkpEREpdt26dePAgQPccsstnD17lrNnz3LrrbeyZ88evvvuO2eHJyWZxUJYnzEs6fA10UYlKqYfxfZ5D9h5Yb9cRESktCj0SKn69evz2Wef0aNH/v/Qr1ixgocffpj9+/c7NEBH06d8pcfERfv5cOkh3F2szHikA62qF3J66JlI+HU0HF5qHoc0hQGToGorh8cqIiJlV3H0HXbs2EGrVq2w2WxF8nxHUB+qZDAMg+e/X8ZNB/7DdS57zJNtH4Q+r5sF0kVEREqAIhspFRUVRa1atS44X6NGDaKiogr7OJGL+mXHKT5cegiACbc2LVxCym6DdR/Dxx3MhJSLh1k76uFlSkiJiIhIqWaxWHjhzq687P8qk3IHmSc3fQFf94Ozx50am4iISGEVOikVHBzMzp07Lzi/Y8cOKleu7JCgpHzbeeIsz8zaAcAjXWtzW+tqBb85Zjd80RsWjoWcdKjRGR5dC13GgItbEUUsIiIiUnx8PVyZfG9bPmIw92U/S6arP5zcAp91hUN/ODs8ERGRAit0UmrIkCE88cQTLFu2DJvNhs1mY+nSpTz55JMMHjy4KGKUciQ2OZOHvt1MVq6dnhHBPNf3wmWzLyonE5b+Fz7vBqe2goe/Wch8+K8QWLdIYxYREREpbhGh/rw6sAnL7S25Pu1VUis1gYxE+P52WDbBHDkuIiJSwrkW9oZXX32VY8eO0atXL1xdzdvtdjvDhg3j9ddfd3iAUn5k5th4+LstxCZnUS/Ylw8Gt8DFarnyjZHr4JcnIOGAeRxxM9z4DviHFW3AIiIiBXTrrbde9vrZs2eLJxApU+5oU40NRxP5cSv0SX6BP5ovwGvHN7DiDTixEW79Anw0k0FEREquQiel3N3dmTlzJv/973/Zvn07Xl5eNG3alBo1ahRFfFJOGIbBv3/cyY7jZ6ng7cYXw9vg53mF6XaZyfDHy7D5S/PYNwRufBsaDSzyeEVERAojICDgiteHDRtWTNFIWWGxWHh1UGN2nTzLgdhUHki4m+8HdcT661NmXc3PusKd30C1Ns4OVURE5KIKvfpeaaeVY0qmj5cf4q0F+3G1Wvj2gXZ0qhN4+Rv2z4dfx0DKKfO45b1ww6vgVcgV+kRERK5AfQeTvg8l16G4FAZMXkN6to0netVjTLMcmHkvJB4Gq5u5Ml+7h8BSgBHoIiIiDlBkq+/ddtttvPnmmxecf+utt7jjjjsK+zgRFv8Zy9sL9wMwbkDjyyekUuNg1n0wfbCZkKpYC4bNg4GTlZASERGRcqlusB+v39IUgA+XHmRlUjA8vBwaDgB7Dsx/Fn58ALJSnRuoiIjIPxQ6KbVy5UpuvPHGC87369ePlStXOiQoKT/2xSQzesY2DAPu7VCDeztcYhqoYcC2qTC5LeyZAxYX6DwaHlsHtbsVa8wiIiIiJc2gllUZ0q46hgFPzdxOTJY73PmtOUrK6gq7f4T/9YT4/c4OVUREJE+hk1Kpqam4u7tfcN7NzY3k5GSHBCXlw+nULB78ZjNp2TY61q7MS/0bXbxh4lH4bhD8/BhknoXQZvDQUrj+FXDzKs6QRUREREqscf0b0SjMn9Np2YyavpVcuwEdR8J9v4FfGCTsh897wK7Zzg5VREQEuIqkVNOmTZk5c+YF52fMmEGjRpdIKoj8Q3aunUenbuXEmQxqVPbm46GtcHP5x19HWy6s/RA+7ghHloOrJ/R+BR5aBlVaOCNsERERkRLL082Fj4e2wtfDlU3HzvDOor9WJq7eAR5ZCbW6Qk6aOZXv9+cgN9u5AYuISLlX6NX3XnzxRW699VYOHz5Mz549AViyZAnTpk1j9mx96iJXZhgG4+btZuPRRHw9XPliWBsq+vxj9F3MLpg3Ck5tM49rdoH+H0DlOsUfsIiIiEgpUTPQh7dub8ZjU7fy6YrDtK1ZkV4NQ8A3GO6dC8teg1UTYeNncGor3DEFAqo5O2wRESmnCj1Sqn///sydO5dDhw7x2GOP8fTTT3Py5EmWLl1K3bp1iyJGKWO+WXuM6RuPY7HAh0NaUi/E7/zFnAz44xX4rJuZkPIIgAEfwvBflJASERERKYAbm4ZxX6eaAIz5YQcnzqSbF6wu0OslGDITPAPgxCb4rCscXuq8YEVEpFwrdFIK4KabbmLNmjWkpaVx5MgR7rzzTp555hmaN2/u6PikjFl1MJ5Xf9sLwNh+EfSICD5/8dhq+KQzrH4XDJu5YszjG6HVMC1hLCIiIlIIY2+MoHm1AJIycnh82jayc+3nLzboa07nC2sO6afhu1thxVtgt1/6gSIiIkXgqpJSYK7CN3z4cKpUqcLEiRPp2bMn69evd2RsUsYciU9l5NSt2OwGt7WqxkNdapsXMpPglydhyk2QeBh8Q+Gu7+Gu78Av1LlBi4iIiJRCHq4uTL67Ff6ermw/fpY35u/L36BiTbh/EbQaDhjmtL5pd0J6ojPCFRGRcqpQSamYmBjeeOMN6tWrxx133IG/vz9ZWVnMnTuXN954g7Zt2xZVnFLKJWXk8OC3m0nOzKVV9Qq8fmsTLBYL7P0VJreDLVPMhq3vg5EboGF/Z4YrIiIiUuqFV/Jm4p0tAPhqzVEW7I7O38DNEwZMgkGfmAvKHFpsTuc7uaX4gxURkXKpwEmp/v3706BBA3bu3Mn777/PqVOn+PDDD4syNikjcm12Rk3fxpH4NKoEePLpva3xyEiAmffCzKGQGgOV6pjLFff/ALwqODtkERERkTLh+kYhPNLVHJ3+7KydRJ5Ou7BRi7vhwSVQqTYkHYev+sKmL8AwijlaEREpbwqclJo/fz4PPPAAr7zyCjfddBMuLi5FGZeUIRPm72PlgXi83Fz4/N7WBB/8AT5qC3vngcUFrhsDj66Bmtc5O1QRERGRMueZPg1oXaMiKVm5jJy2lcwc24WNQpvAw8sh4mawZcNvT8NPD0P2RZJYIiIiDlLgpNTq1atJSUmhdevWtG/fnsmTJ5OQkFCUsUkZ8MOm43y5+igAn9xYgSZ/3APzRpl1pMJawCMroPc4cPNybqAiIiIiZZSbi5XJd7ekorcbu08m89/f/rx4Q88As67nDf81Pzjc9QP8rxckHCzegEVEpNwocFKqQ4cO/O9//yM6OppHHnmEGTNmUKVKFex2O4sXLyYlJaUo45RSaNOxRF6YuwsXbHwXsY7uSwbCsVXg6mV2dh5cAqFNnR2miIiISJkXFuDFe3e1wGKB79dH8fP2kxdvaLFAp1Ew/BfwDYH4vfB5d9gzp1jjFRGR8qHQq+/5+Phw//33s3r1anbt2sXTTz/NG2+8QXBwMAMGDCiKGKUUOnEmnf/7bgv17UdZFjCeLsc+hNxMqN0dHltndnZcXJ0dpoiIiEi50b1BMI/3qAvA8z/t4nB86qUb1+wMj6yCGtdBdirMug8WjAVbTvEEKyIi5UKhk1J/16BBA9566y1OnDjB9OnTHRWTlHJpWbk8NmUND2V9wzyP/1A96yB4VoCBH8O9c6FSLWeHKCIiIlIuje5dnw61K5GWbWPk1K1kZF+kvtQ5fiEw7GfoPNo8Xv8xTLkJki4xykpERKSQrikpdY6LiwuDBg1i3rx5jniclGJ2u8GnU75m0pmR/J/rL7hgh8a3wMiN0HKoOSRcRERERJzCxWph0uCWBPp6sC8mhXHzdl/hBle4/hUYPB08AuD4BviiN6QnFk/AIiJSpjkkKSUCQMYZ9nw6jKejn6GmNZZs71CzA3PHFPOTNhERERFxumB/TyYNaYHVAj9sPsGszcevfFPEjfDIcqhUG1JOwaIXizxOEREp+5SUEsf482cy329D0zhztNzhGnfh/sRGswMjIiIiIiVKpzqBPNW7PgAv/ryb/TEFWLSoUm0Y9Clgge3fw+FlRRukiIiUeUpKybVJjoYZQ+GHYXhmJXDYHsZ3DT+lzojPzWWFRURERKREGtmjLl3rB5GZY+exqVtIy8q98k3V20O7h8z9X56E7PSiDVJERMo0JaXk6tjtsPlr+Kgd7PuVXFyYlDuIN2t+yd13DHZ2dCIiIiJyBVarhffubE6ovyeH49N4fs4uDMO48o29XgL/anA2Epa9VvSBiohImaWklBRewiH4pj/8OhqykjngWp+bsl7jl0r3M/HudrhYVcxcREREpDSo7OvBh3e3xMVq4eftp5i+sQD1pTz84OZ3zf31H8PJrUUbpIiIlFlKSknB2XJg1UT4pBNErsZw8+an4JH0TX2JWK86fDG8DX6ebs6OUkREREQKoW3NSjzXpwEAL/+yh90nk658U/0+0OR2MOww7wmznygiIlJISkpJwZzcCp/3gCXjwZYFdXryfasZjInqjNXqwsdDW1Gjso+zoxQRERGRq/BQl9r0bhhMdq6dkdO2kpxZgCRT3zfAqyLE7oK1k4o+SBERKXOUlJIr2zYVvuhldji8KsItn7G41Se8tDIVgHEDGtOpTqCTgxQRERGRq2W1WnjnjuZUreBF5Ol0/v3jzivXl/INMhNTAMvfNEs8iIiIFIKSUnJ5dptZwNKwQ6OBMHIT+0JuZPTM7RgG3NuhBvd2qOHsKEVERETkGlXwduejoa1wc7Hw+64Yvll77Mo3NbsL6vQ0R9L/8qS5GI6IiEgBKSkll3d0BSSfBM8KcMvnnMafB7/ZTFq2jY61K/NS/0bOjlBERETk/9u77/CoyryN49+Z9E4KKYTQS0ILHUOTKoIN110bq6irrgpYsIG7Cuoqa2ftZXX3dS1gQ1EQhSgliNIMCCRAKBJKOqmkz7x/nBCItIDJnJnk/lzXXDlz5pyZ38wJ+uSep0gD6R3TgocmxAHwxOIUktPzT3+CxQIXzwUPX/g1CTb+X6PXKCIiTYdCKTm95A+Nnz2uoMLiye3vb2T/4VLahvry6qS+eLjpV0hERESkKblhcDvG94ikstrOlPc3kn+k4vQnBLeFUQ8b20sfgcJDjV+kiIg0CUoU5NTKCiDlSwDsva9l1sItrN2Th7+XO/++vj/Bfp4mFygiIiIiDc1isfDUH3vRNtSXA/ml3PfxpjPPLzXorxDdD8oLYfF9cKbjRUREUCglp7P1c6gqhbCu/N/eED5cm47FAi9d04fOEQFmVyciIiIijSTQ24NXru2Lp7uVZSlZvLVq9+lPsLrBpS+B1R1Sv4KUhY4pVEREXJpCKTm15A8A2NP6Mh5blALAzPGxjIwNN7MqEREREXGAHtFBzKqZP/SpJdtZvzfv9CdEdIeh9xjbi++H0sONXKGIiLg6hVJycrm7IP1H7BYrd6d0wWaHK/q25pZhHcyuTEREREQc5NqBbbisdyuqbXamfvAzucXlpz9h2H0Q2hmKM+Hbhx1TpIiIuCyFUnJym4wJzvcGDWJTgS/RLXz4x8QeWCwWkwsTEREREUexWCw8eXlPOrb0I6OwjHs+2oTNdpr5ojy8jWF8AD//D3avcEyhIiLikhRKyYlsNtg0D4AXcwcA8LeL4vDxdDOzKhERERExgZ+XO69O6oe3h5WVO7J56bu005/QNgH6/8XY/vIuqCxt/CJFRMQlKZSSE+1dBQXpHLH6sbiyLwkdQhnfI9LsqkRERETEJF0jA3j8sh4AvLBsB3fP+5mCI5WnPmHMbAhoBYf3wPI5jilSRERcjkIpOVHNBOcLKs6jyurFo5d117A9ERERkWbuT/1juHdsF6wW+Dz5IOPmrmTVzuyTH+wdCBc9Z2z/8DIcTHZYnSIi4joUSkld5UXYa5bw/aR6ONcntKVLRIDJRYmIiIiIM5g2ujOf3D6Y9mHGHFPXvb2WR77YwpGKqhMPjp0A3S8HezUsnAbVJzlGRESaNYVSUte2L7BUHmGXLYp9Pt24e0wXsysSERERESfSt00wi+4cyvUJbQF4d82vXPRiEhv3HT7x4PFPg3cLyNgMa152bKEiIuL0FEpJHRUb3gPg0+rhPDA+liAfD5MrEhERERFn4+vpzmOX9eDdmwYSGejNnpwS/vjaDzz37XYqqmzHDvQPh3FPGtvL50DuLnMKFhERp+QUodQrr7xCu3bt8Pb2ZtCgQaxdu7Ze582bNw+LxcLEiRMbt8DmIm83nvvXYLNbSAmfwJ/6xZhdkYiIiIg4seFdWvLN3cOZ2LsVNju89F0al7+6mh2ZRccO6n0tdBgBVWXGanx2u2n1ioiIczE9lJo/fz7Tp09n1qxZbNy4kfj4eMaNG0dWVtZpz9u7dy/33Xcfw4YNc1ClTd+hlf8BIMnWg2mXn4/VqsnNRUREROT0gnw9mHt1H165ti8tfD3YerCQi19K4q2Vu6m22cFigYvngruPscrzz/8zu2QREXESpodSzz//PLfccgs33ngj3bp14/XXX8fX15d33nnnlOdUV1czadIkHn30UTp06ODAapsuW3U11s3zAdjXZiJ92wSbXJGIiIiIuJKLekXx7d3DGdm1JRVVNp5YnMI1b/1Iet4RCGkPo/5mHPjt36Eow9xiRUTEKZgaSlVUVLBhwwbGjBlTu89qtTJmzBjWrFlzyvMee+wxwsPD+ctf/uKIMpuFFUs/J8KWSZHdhwuuuMnsckREROQ0Vq5cySWXXEKrVq2wWCx8/vnnpz1++fLlWCyWE24ZGQoGpGGFB3rzzg0DmPOHnvh6urF2Tx4Xzl3JR+vSsQ+6DaJ6Q1kBLL7f7FJFRMQJmBpK5eTkUF1dTURERJ39ERERp2wkJSUl8fbbb/PWW2/V6zXKy8spLCysc5O6CkorKf7pXQAOtLqQ8JAQkysSERGR0ykpKSE+Pp5XXnnlrM7bvn07hw4dqr2Fh4c3UoXSnFksFq4Z2IYldw1nQLtgSiqqeeDTzdzyXjJ5Y54HixukLISUL80uVURETGb68L2zUVRUxHXXXcdbb71FWFhYvc6ZM2cOQUFBtbeYGE3e/VuvfrOJUTajZ1rHC241uRoRERE5k/Hjx/OPf/yDyy+//KzOCw8PJzIysvZmtbpUU1BcTJtQX+bdmsDM8bF4ullZlpLFmA/y2NWlZrTDovugNN/UGkVExFymtkTCwsJwc3MjMzOzzv7MzEwiIyNPOH7Xrl3s3buXSy65BHd3d9zd3Xn33XdZuHAh7u7u7Np14hKzM2fOpKCgoPaWnp7eaO/HFe3MLCJv/cf4WcopDWiLR7sEs0sSERGRRtK7d2+ioqIYO3Ysq1evPu2x6m0uDcHNauGv53dk4bQhxEUFkldSwYRNg8nyjIHiDFg2y+wSRUTERKaGUp6envTr14/ExMTafTabjcTERBISTgxHYmNj+eWXX0hOTq69XXrppYwcOZLk5OST9oLy8vIiMDCwzk0Mdrud2V9u5Q+WlQD4DLjOWB1FREREmpSoqChef/11Pv30Uz799FNiYmIYMWIEGzduPOU56m0uDSk2MpDPpwzmjhEdqbR4Mq3oBuOBDf+FvUlmliYiIiZyN7uA6dOnM3nyZPr378/AgQOZO3cuJSUl3HjjjQBcf/31REdHM2fOHLy9venRo0ed81u0aAFwwn45syVbMvh1VyoJXtuwY8HS62qzSxIREZFG0LVrV7p27Vp7f/DgwezatYsXXniB//3vfyc9Z+bMmUyfPr32fmFhoYIp+V283N144MJYRsWGc+/HvnxQMIpr3b8j98Pb8LvrR7x9/c0uUUREHMz0UOqqq64iOzubRx55hIyMDHr37s2SJUtqJz/ft2+f5jtoBKUV1fxjUQpXWFcBYGk/HFqooSkiItJcDBw4kKSkU/dQ8fLywsvLy4EVSXPRv10Ii+8cxnNfepP5y0YiytP5YO5ddL/uOeJjWphdnoiIOJDpoRTA1KlTmTp16kkfW758+WnP/e9//9vwBTUDr6/YxcH8Eq7yNkIpek8ytyARERFxqOTkZKKioswuQ5opPy93HvljAr8EP0nEqju4svwzLnu9P2NGjGbqqE54uOlLaRGR5sApQilxrPS8I7y+YhcDLNuJJhM8/SHuYrPLEhERkXoqLi4mLS2t9v6ePXtITk4mJCSENm3aMHPmTA4cOMC7774LwNy5c2nfvj3du3enrKyMf//733z33Xd8++23Zr0FEQB6jp5EReZiPHd8xRy3N7g8MZrvUrN44ap4OoUHmF2eiIg0Mn0F0Qw9sSiF8iobtwevNXZ0nwiefqbWJCIiIvW3fv16+vTpQ58+fQBjjs4+ffrwyCOPAHDo0CH27dtXe3xFRQX33nsvPXv25Pzzz2fTpk0sW7aM0aNHm1K/yPE8L3kevIPoZd3DHd5L+eVAARe9mMQ7SXuw2exmlyciIo3IYrfbm9V/6QsLCwkKCqKgoKBZrsSXtDOHP7/9E/7Wcjb7TcVaWQI3fg1tB5tdmoiIiFNq7m2Ho/Q5SKPa+C4snIbd3Yf7W77OJ3s8AEjoEMqzV8YT3cLH5AJFRORs1LfdoJ5SzUhltY3ZX24F4LHOu41AKrgdtEkwtzARERERad76XAfth2OpKuUZ73f4x2Xd8fFwY83uXC58YSWfbNhPM/suXUSkWVAo1Yz83w97ScsqJsTPk0tZYeyMvxYsFnMLExEREZHmzWKBi+eCuzeWPSv4s/dqvr5rGH3btKCovIr7Pt7EX/+3gZzicrMrFRGRBqRQqpnILirnX8t2AjB7eADuv9asuhd/tYlViYiIiIjUCO0II2Ya2988RDvvEj76awL3j+uKh5uFb7dlcuHclXy7NcPcOkVEpMEolGomnl6SSlF5Fb1aB3GxfSVgh3bDILit2aWJiIiIiBgSpkJkLyjLh68fwN3NypSRnfh8yhC6RgSQU1zBrf/bwP0fb6KorNLsakVE5HdSKNUMJKfn8/GG/QDMvqQb1s0fGg/0vtbEqkREREREfsPNHS59CSxusHUBpC4GoHurIBZOG8Jfz++AxQIfb9jPhXNXsWZXrskFi4jI76FQqomz2ezM+mILAFf0bU1fyw7I2w0efhB3qcnViYiIiIj8RqveMHiqsb3oXigrAMDL3Y2Z4+OYf2sCMSE+HMgv5Zq3fuTxr7ZRVlltXr0iInLOFEo1cZ9s3M+m/QX4e7nz4PiukPy+8UD3ieDlb2ptIiIiIiInNWImBLeHooOw7NE6Dw1sH8LXdw3nmoExALydtIdLXkril/0FZlQqIiK/g0KpJqywrJKnl6QCcNfozoR72WDLAuPB+GtMrExERERE5DQ8fODSF43t9W/Drz/Uedjfy505f+jFOzf0J8zfi51ZxVz+6mpeTNxJVbXNhIJFRORcKJRqwv61bCc5xRV0aOnH5MHtIHURVBRBizbQdojZ5YmIiIiInFr74dD3emN74Z1QWXbCIaNiI/j2nuFM6BlJlc3O80t3cMXra9iVXezgYkVE5FwolGqidmYW8X8/7AVg9iXd8XS3Hhu6F38tWHXpRURERMTJjX0M/CMgdyesevakh4T4efLKtX3519W9CfR2Z1N6Phe9uIr/rt6DzWZ3cMEiInI2lEw0QXa7ndlfbqXKZmdstwiGd2kJBfth93LjgPirTa1PRERERKRefIJhwjPGdtILkLHlpIdZLBYu6x3NN/cMZ2inMMoqbcz+chvXv7OWg/mlDixYRETOhkKpJuibrRmsTsvF093Kwxd1M3Zung/YjWF7Ie1NrU9EREREpN66XQaxF4OtChZOA9upV9qLCvLh3ZsG8thl3fH2sJKUlsO4uStZ8PN+7Hb1mhIRcTYKpZqY0opqHv8qBYDbhnegTagv2O2Q/IFxQO9rTaxOREREROQcTHgWvALh4Eb46Y3THmq1Wrg+oR2L7xxG75gWFJVVcc/8Tdzx/kbySiocVLCIiNSHQqkm5o2VuziQX0qrIG9uH9HJ2Ll/HeSmgYev8U2TiIiIiIgrCYwy5pcC+O5xOLz3jKd0aOnPJ7clcN8FXXC3Wvh6SwYXvLCSxJTMxq1VRETqTaFUE5Ked4TXlu8C4G8XdcPH08144GgvqbhLwSvApOpERERERH6HvpOh7VCoPAJf3WOMBjgDdzcrU0d15vMpQ+gc7k9OcTl/+b/1zPh0M8XlVQ4oWkRETkehVBPy5OIUyqtsJHQIZULPSGNnZSls+czY1tA9EREREXFVVitc8i9w84Jd38GmefU+tUd0EF9OG8otw9pjscC8demM/9dK1u7Ja8SCRUTkTBRKNRGr03L4eksGblYLsy7thsViMR5IXQTlBRAUA+2GmVukiIiIiMjvEdYJRswwtr+ZCcXZ9T7V28ONv13UjQ9vOY/oFj6k55Vy1ZtrmLM4hbLKU0+eLiIijUehVBNQWW1j9sKtAFx3XltiIwOPPXh06F781ca3SyIiIiIirmzwNIjoCaWHYcmMsz79vA6hLLl7GFf2b43dDm+s3M3FLyWxZMshbDat0Cci4khKKZqAd9f8ys6sYkL8PLlnTJdjDxQehN3fG9vx15hTnIiIiIhIQ3LzgEtfBIsVtnwCO74566cI8Pbg6T/G89b1/Qnz9yQtq5jb3tvIhBdX8fUvCqdERBxFoZSLyy4qZ+7SHQDcP64rQb4exx7cPB/sNmiTAKEdTapQRERERKSBRfeFhCnG9lf3QHnROT3N2G4RJE4fwZ2jOuHv5U5qRhG3v69wSkTEURRKubhnvkmlqLyKntFBXNk/5tgDdvuxoXua4FxEREREmpoRD0FwOyg8AImPnfPTBPl6MP2CriQ9OJI7R3Ui4Lhwavy/VrFY4ZSISKNRKOXCktPz+Wj9fgBmX9odN6vl2IMHNkLODnD3gW4TzSlQRERERKSxePrCxXON7bVvwb6fftfTtfD1rAmnRnHn6M4EeLmzPbOIO2rCqUWbFU6JiDQ0hVIuymazM6tmcvM/9I2mX9vgugckv2/8jLsEvAMREREREWlyOo6E3n8G7LBwGlSV/+6nDPL1YPrYLiQ9OIq7RncmwNsIp6Z8sJEL/7WSrzYfVDglItJAFEq5qE827mdTej7+Xu7MuDC27oOVZcakj6CheyIiIiLStF3wOPiFQ852WPVcgz1tkK8H99SEU3ePMcKpHZnFTP3gZ8bNXcmXmw5SrXBKROR3USjlggrLKnl6SSoAd47uRHigd90DdnwNZQUQGA3th5tQoYiIiIiIg/iGwISnje1Vz0PmtgZ9+iAfD+4eY4RT94zpQqC3Ozuzipn24c9cOHclCxVOiYicM4VSLuhfy3aSU1xBh5Z+3DC4/YkHHJ3gPP5qsLo5tjgREREREUfrNhG6TgBbpTGMz1bd4C8R5OPBXWM6kzRjFNPHHgun7vzQ6Dn1RfIBhVMiImdJoZSL2ZlZxP/9sBeAWZd0x9P9N5ewKAPSlhnb8Rq6JyIiIiLNgMUCE54FzwA4sN6Y+LyRBHp7cOdoI5y6d2wXgnw8SMsq5q55yVzwwgqFUyIiZ0GhlAux2+08+uU2qmx2xnaL4PwuLU88aPNHYLdB64EQ1snxRYqIiIiImCEoGsY+amwnPgb5+xr15QK9PZg2ujNJD47kvguMcGpXdgl3zUtm7Asr+PxnhVMiImeiUMqFfLM1g6S0HDzdrTx8UbcTD7Dbjw3d0wTnIiIiItLc9LsR2iRAZQl8Nd1oHzeyAG8Ppo46Fk618PVgd3YJd89XOCUiciYKpVxEWWU1j3+VAsBfh3egTajviQcd/BmyU8DdG7pf7uAKRURERERMZrXCJS+CmyekLYVfPnbYSx8Np1Y9MJL7x3WtG049v4IFP++nqtrmsHpERFyBQikX8fqKXRzIL6VVkDe3j+h48oOO9pKKvRh8WjisNhERERERp9GyC5z/gLH99YNQkuPQlw/w9mDKyE4kPTiK+8d1JdjXg905JdwzfxNjX1jJZxsVTomIHKVQygXsP3yE15bvAuChi+Lw9XQ/8aCqctjyibHd+xoHViciIiIi4mQG3wXh3aE0D5bMNKUEfy93pozsxKoHR/HAhUY4tSenhOkfbWLM8yv4dIPCKRERhVIu4IlFKZRX2TivQwgX9Yw6+UE7lkDpYQiIgg4jHVugiIiIiIgzcfeES18CixV++Qh2LjWtFH8vd+4YYYRTD14YS7CvB3tzj3Dvx0Y49YnCKRFpxhRKObnVaTl8vSUDN6uF2Zd2x2KxnPzAo0P34q8Gq5vjChQRERERcUat+8Gg243tr+6B8mJTy/H3cuf2ER1JenAUM8bHEuLnyd7cI9z38SZGP7+Cj9enK5wSkWZHoZQTq6y2MXvhVgCuO68tsZGBJz+wOOvYtz/xWnVPRERERASAUX+DFm2gIB2+e9zsagDw83LntvM7suqBkcysCad+zT3C/Z9sZvTzK/hofTqVCqdEpJlQKOXE/rfmV3ZmFRPi58k9Y7qc+sDNH4G9GqL7GxM7ioiIiIgIePrBxXON7Z/egPR1ppZzPD8vd/56fkeSHhzJQxNiCa0Jpx74ZDOjn1vBR+sUTolI06dQyknlFJfzwrIdANw/ritBvh4nP9BuPzZ0r7d6SYmIiIiI1NFpNMRfA9hh4TSoqjC7ojp8Pd25dXhHVtWEU2H+nuzLO8IDn25m1HPLmb9un8IpEWmyFEo5qaeXpFJUVkWP6ECu7B9z6gMzNkPWVnDzgh5/cFyBIiIiIiKuYtyT4BsG2SmQ9ILZ1ZzU0XBq5QMj+duEOML8PUnPK+XBT39h5LMKp0SkaVIo5YSS0/P5aP1+AB69tDtu1lNMbg7HeknFTgCfYAdUJyIiIiLiYnxDYPxTxvbKZyAr1dx6TsPX051bhndg1QOj+PtFcYT5e7H/8LFwat7afVRUKZwSkaZBoZSTsdnszKqZ3PwPfaLp1zbk1AdXVRjzSQH0nuSA6kREREREXFSPK6DzOLBVwpd3gs25gx0fTzduHtaBVQ+MrBNOzfjMCKc+VDglIk2AQikn8+nG/WxKz8ffy50Z42NPf/DOb6A0D/wjocNIxxQoIiIiIuKKLBa4+Hnw9If0n2D922ZXVC/Hh1MPX9yNlgFeHMgvZWZNOPXBTwqnxMlUV5ldgbgQhVJOpLCskqeWGF2J7xzdifBA79OfkPyh8bPXleDm3sjViYiIiIi4uKDWMGa2sb1sNhTsN7Oas+Lj6cZfhrZn1QMjeeS4cOqhBUY49f5PvyqcEnOVFULi4/DPNvDWKNi/weyKxAUolHIiLy7bSU5xBR1a+nHD4PanP7g42+gpBVp1T0RERESkvvr/BWIGQUUxfDXdWM3ahXh7uHFTTTg165JuhNeEU39bsIWRzy7nvR9/pbyq2uwypTmpqoCf3oAXe8OqZ6GyBA5sgH+PNla8LMkxu0JxYgqlnERaVhH//WEvAI9c3A1P9zNcml8+BlsVtOoL4XGNX6CIiIiISFNgtcIlL4Kbp/El75ZPza7onHh7uHHjkPasfGAks48Lp/7++RZGPrOcd9fsJa+kwuwyz11lKexfD+vehi/vgjdHwHOxsGQmFGWaXZ2AEehu+QxeGQhfPwBHciG0M/zh3xB/LWCHje/CS31h7VtgU1gqJ7LY7S721cDvVFhYSFBQEAUFBQQGBppdDgB2u53r3l5LUloOY+Ii+Pfk/mc+6fWhkPELTHgWBt7S+EWKiIg0U87YdjCDPgdpcpY/BcufBN8wmLrOWKHPhZVVVjN/XTqvLk8js7AcAKsF+rcL4YJuEVzQLZI2ob4mV3kKpfnG3zYZm+HQZji0CXJ2gP0UIYa7Dwz4Cwy5C/zDHVqq1NizCpY+Agc3Gvf9wmHkTOhz/bGpZfb9CIvvM64tQGRP4+/XNueZU7M4VH3bDQqlnMCSLRnc9t4GPN2tLLvn/DP/z+LQZnhjmPHtzr3bXf5/oCIiIs7MGdsOZtDnIE1OVQW8MRyyU6DbRBgxA4Lbg8cZ5nV1ckfDqXnr0kk5VFjnsa4RAVzQPYKx3SLoGR2ExWJxfIHFWUbodPSWsRkO7z35sX4tIbIXRMVDVC9w84Kk52H/OuNxD18YcLMRTvmFOewtNGuZ24z52I5OJePhZ3z+CVPAy//E423VsP4d+O5xKCsw9sVfA2MehYAIh5UtjqdQ6hScrUFVVlnNmOdXsP9wKVNHduK+cV3PfNKSmfDjq9DtMrjy3cYvUkREpBlztraDWfQ5SJOUvg7eHgsc/ZPIAoHRENoBQjpCaMdjP4PbgbuXicWevfS8IyxLyeTbrZms3ZtHte3Yn35RQd6MiTMCqvM6hJ55+pCzZbdD/r5jwdOhTcaX68UZJz8+qI0RPEXFHwuiAiKNVRN/+7xpiUYvtwM1E2l7+BmjRwbfCX6hDfs+xFBwwPjMkz8Auw2s7tDvBjj/wfr1VivJgcTHjOF82MEzwOhZNfBWcPNo7OrFBAqlTsHZGlT/WraTF5btICrIm8R7z8fX8wyr6FVXGmOpj+TANfOh64WOKVRERKSZcra2g1n0OUiT9fP7sO4tyN0N5QWnOdACQTEnBlYhHWoCK09HVXxO8o9U8P32LL7dmsmKHdkcqTg2NC7Ay50RseFc0C2CEV1bEuB9liGBrRpydh4XPm0yhmyV5Z/kYAuEda7bAyqy19mP/rDbYee38P2TcCjZ2OfpD4P+CglTNZqkoZQVQNJco1NEVZmxL+5SGD0Lwjqd/fPt32AM6Ts67K9lHEx4BtoPa7CSxTkolDoFZ2pQ7T98hNHPraC8ysZL1/ThkvhWZz4pdTHMu8YYszt9m1JlERGRRuZMbQcz6XOQJs9uNyZqzt0Febshb1fN9i4jsKooOvW5FmtNYHVcz6qQmvAquK3TtdnLKqv5YVcOS7dlsnRbFjnF5bWPebhZOK9DKBd0j2RsXASRQb8ZzlhVDlnbjF5PR0OozK1QeeTEF7J6GIsyRcUf6wEV0f3kw7zOld0OO5YY4VTGZmOfZwCcdzsk3AE+wQ33Ws1JVbkx7G7F01CaZ+xrkwBjH4OYgb/vuW02+Pl/xjDAo8/d4woY+zgERf++5xanoVDqFJypQXXH+xtY/EsG53UI4cNbzqvfmO55kyD1KyP9H/dE4xcpIiLSzDlT28FM+hykWbPboST71IFVZcmpz7W4QYs2dXtWHQ2tWrQ9Nim0SWw2Oz+n59cEVBnsyj72Xvwo5eKIXC4KyyLePZ3A/K1YslONVcB/y8MPInscN/yul9ELxlE9yOx2SF0Ey/8JmTUTa3sFGeHUebeDTwvH1OHqbDbY+pkx1C7/V2NfWBdjDqiu408cTvl7HMmD758wwi+7zfgdOv8BOO8Op+95KGemUOoUnKVBtToth0n//gmrBRbfNYzYyHrUUpILz3UFWyXc/oPxLYOIiIg0KmdpO5hNn4PIKdjtUJx5ksBqt3E7WQ+io6zuRjB1fM+qo8MDg2IcG1iV5ELGJnLT1pO3az2+uVuIqjqI1XLin4uVni1wj47HcnwPqNCOYHVzXL2nYrMZX+Ivn2P06ALwDjK+1B90G3jrv1+ntHuFsaLe0eGQ/hEw8iHo/efG/V08tAkW3w/pPxn3QzvDhKeh46jGe01pdPVtN5gbyzdTldU2Hv1yKwDXnde2foEUwJZPjEAqKl6BlIiIiIiIM7BYjAm5AyKh3ZC6j9ntUHTo1IFVVZmxL2/Xic9r9TDmqjq+Z9XR3lZBrc89ALLbofDgcROQ1wzBK9wPQGjNzXhvcMQ7nB2WDiSVRLO5qg1bbO05WBZKSLUXo3zDGRsdwfCglvg4QyAFYLVCt0sh9mJI+cLoOZWdavTIWfMKDK4Jp7wCzK7UeWRuhaWzIG2pcd8zoGZFvTvA06/xXz8qHm76BjbNM0Kx3J3wv8sh7hIY96TR01CaLKfoKfXKK6/wzDPPkJGRQXx8PC+99BIDB558nOpnn33Gk08+SVpaGpWVlXTu3Jl7772X6667rl6v5Qzf8r2TtIfHvtpGsK8Hy+8bSZBvPceYvzHc+B/G+KeNCfxERESk0TlD28EZ6HMQaWA2GxQd/E1gVfMzbw9Ul5/6XDfPmsCq44mBVWC0EcwcfY3De4yeL8fPAXUk9+TPG9LhNxOQx4N/SwBKyqtYuSObpdsySUzNoqC0svY0bw8rQzu15ILuEYyODSfU34lWKbRVw9YFsOIpyNlh7PMJgcHTjJXfGnJ+K1dTsN+Yiyv5A8Bu9NzrfxMMf6D2ujtcWYERJP70Btirwd0Hht1rXC8P7zOfL07DZYbvzZ8/n+uvv57XX3+dQYMGMXfuXD7++GO2b99OePiJS0suX76cw4cPExsbi6enJ1999RX33nsvixYtYty4cWd8PbMbVDnF5Yx8djlFZVU8eXlPrh1Uz9Q3cyu8Ntj4xuTe7VrqVERExEHMbjs4C30OIg5kq4bCA3V7VR2dw+rwXqiuOPW5bl4Q0h68AiEr5eQTtFvcoGWsETwdHX4X2bPeQ9uqqm2s3ZvH0m2ZfLs1kwP5pbWPWS3Qr20wF3SLZGy3CNqFOaCnTX3YqmHLZ7Din5CbZuzzDYXBd8LAWxzTI8hZlOZD0gvw0+vHVtTrNhFGP2KEm84gcyssfgB+TTLuB7eH8U9BlzP/zS/OwWVCqUGDBjFgwABefvllAGw2GzExMUybNo0ZM2bU6zn69u3LRRddxOOPP37GY81uUD34yWbmr0+nR3QgX0wZipu1nhPFffM3WPOy0Q316vcbt0gRERGpZXbbwVnocxBxErZqKEg/Lqj6TWD120nI3b2NqT+O7wEV3r3Bep3Y7XZSDhUZAdW2DLYeLKzzeJcIf8Z2i2Bst0h6RQdhre/fP42lusqYFmXFU8ZnB+DX0hiu1v8v4Olrbn2Nqaoc1r4Fq56F0sPGvrZDjBX1Wvc3t7aTsdthy6fw7d+NYbAAXS6EC+cYvfrEqblEKFVRUYGvry+ffPIJEydOrN0/efJk8vPz+eKLL057vt1u57vvvuPSSy/l888/Z+zYsSccU15eTnn5sa6vhYWFxMTEmNKg2pSez8RXV2O3w6e3J9CvbUj9TqyuhOe7QUkWXP0hxE5o3EJFRESklsIYgz4HERdQXQUF+4ywpTQfwrsZK6c5cML0A/mlLKsJqH7anUeV7difmxGBXoyJi+CC7pGc1yEEL3cT56GqroJfPjLCqcN7jX1+4TD0Huh/I3j4mFdbQ7PZjCDuu8chf5+xr2WssaJel3ENu6JeYygvgpXPGHOC2aqM3oBD7jKuVVMOEV2cS4RSBw8eJDo6mh9++IGEhITa/Q888AArVqzgp59+Oul5BQUFREdHU15ejpubG6+++io33XTTSY+dPXs2jz766Emfw5ENKpvNzh9e+4Hk9Hz+0Cea56/qXf+Tty+BD68C3zC4NxXc6jkHlYiIiPxuCmMM+hxE5GwVHKnk++1ZLN2WyfLtWZRUVNc+5u/lzvldW3JBtwhGdA0nyMekv3GqK40Jtlc+fSyw8Y+EYdOh72TXn8do1/fG5OEZm437AVHGinrx1zp2dceGkL0Dvr4fdi837ge1gQufNEYTOXuw1gw16dX3AgICSE5Opri4mMTERKZPn06HDh0YMWLECcfOnDmT6dOn194/2lPK0T7duJ/k9Hz8PN2YMT727E7e9IHxs9eVCqRERERERMQlBPl6MLFPNBP7RFNeVc0Pu3JZui2TpdsyyS4qZ9HmQyzafAh3q4WEjqGM7RbBmLgIWrVwYC8lNw/oex30usr4u2vls8bwyK8fgKS5NeHU9eDuRJO310fGL8aKersSjfueATD0bjjvDtftXdSyC1z3OaR8Cd88ZPQKnP9n6DgKxj8DYZ3MrlDOgUsP3zvq5ptvJj09nW+++eaMx5rxLV9hWSWjnl1BTnE5M8fH8tfzz2LyuCN58FxXYzLD25KMCQhFRETEYdRDyKDPQUQais1mZ9P+/Jp5qDJJyyqu83jP6CDGdovggu4RdI0IwOLIXjBVFZD8nhFOFR4w9gVGGyvA9bkO3D0dV8u5yE+H758wen9hNxbKGvAXGH4/+IWZXV3DqTgCSc/D6n8ZfytbPWDwVBh2X/NeUdGJ1LfdYHVgTSfw9PSkX79+JCYm1u6z2WwkJibWGc53Jjabrc68Uc7mxWU7ySkup0OYHzcOaX92J2/51PhHFtlTgZSIiIiIiLg8q9VCnzbBPHBhLMumn8/3943goQmx9G8bjMUCvxwo4PmlO7hw7iqGP/M9j325jR9351JVbWv84tw9of9NcOfPMOFZCGhlhFOLpsNLfWH9f4zgytmUHjYmBH+pH2z6ELBD9z/A1LXGqnVNKZACo7fXqL/DHT9C5wvAVmmsKPjKQGOVRXPXc5OzYPrqe/Pnz2fy5Mm88cYbDBw4kLlz5/LRRx+RmppKREQE119/PdHR0cyZMweAOXPm0L9/fzp27Eh5eTmLFy9mxowZvPbaa9x8881nfD1Hf8uXllXEhXNXUWWz898bBzCia/jZPcGbI+Dgz3DhP+G82xulRhERETk19RAy6HMQEUfILirnu1RjiN+qnTmUVx0Lolr4ejCyazij48IZ3qUlgd4OmNqksgw2vgurnoPijJpC2hg9j+KvMX96lcoyWPumUV9ZvrGv3TAY+yhE9zO1NIfa/jV8/SDk/2rcbzcMJjwD4XHm1tWMucycUldddRXZ2dk88sgjZGRk0Lt3b5YsWUJERAQA+/btw2o91qGrpKSEO+64g/379+Pj40NsbCzvvfceV111lVlv4ZTsdjuPfrmNKpudMXERZx9IZaUYgZTVHXr+qXGKFBEREZezcuVKnnnmGTZs2MChQ4dYsGBBnakQTmb58uVMnz6drVu3EhMTw9///nduuOEGh9QrIlJfLQO8uGpAG64a0IYjFVWs3JHD0m2ZJKZmkn+kkgU/H2DBzwfwcLMwsH0IY+KMeahiQhppniQPbxh0qzHv1Ib/Gr1x8vfBwmlGEDT8AWM+KkdPGm6zGasHfvcPYw4sMFZbHPModB7b/Cb+7joeOoyEH140rsveVfD6UBh0G5z/IHjryxRnZXpPKUdz5Ld832zN4K//24Cnu5Wl9wynbajf2T3Btw8b/6i6XgTXfNA4RYqIiMhpOWMPoa+//prVq1fTr18//vCHP5wxlNqzZw89evTgtttu4+abbyYxMZG7776bRYsWMW7cuHq9pjN+DiLSfFRV29jw62ESU7NYlpLJ7uySOo93ifBndFwEY+LC6R0TjJu1kUKZiiOw/h1YPRdKso19IR2McKrnnxwTTqUlwrJZxmTmYAwxHPU3o+eW1a3xX9/ZHf7VmAg99Svjvn8EjH3cWDisuYV1p1JeDFnbjI4wfa9vlM+lvu0GhVKNpKyymjHPr2D/4VKmjuzEfeO6nt0TVFfBC92gOBOueh/iLm6cQkVEROS0nD2MsVgsZwylHnzwQRYtWsSWLVtq91199dXk5+ezZMmSer2Os38OItK87MkpITElk2Upmazbe5hq27E/a0P8PBnZNZwxceEM69ISf69GCIoqSmDdv42Jto/kGvtCOxm9cnpc0Tjh0KFNsPQR2L3cuO8VCEPvMaZ58XDgioWuIm0ZLH4A8nYZ99skGEP6mtNczXY7FB0yAsyMzZCxxdjO2w3U/Ju5dzsERDb4S7vM8L2m6o0Vu9l/uJSoIG/uGHkWq+0dtft7I5DyCTEmbhMRERE5R2vWrGHMmDF19o0bN4677777lOeUl5fXWUimsLCwscoTETlr7cP8uHlYB24e1oGCI5Us35FFYkoWy7dnkVdSwacb9/Ppxv14ulkZ1MEY5jc6LpzWwQ00zM/TD4bcBf3/AuvegtUvQm4afHYLrHzGCKe6X94w4VT+PmOY3ub5xn2rBwy8FYbfB74hv//5m6pOY+CONbDmFeOa7FsDbwyHATfDyIfAJ9jsChtWdSXk7KgJnjYb4VPmlmOh6W8FREFED6PXVIBjSz2eQqlGsP/wEV5dngbAQxPi8PU8h485+X3jZ68rnX/ZUREREXFqGRkZtfN1HhUREUFhYSGlpaX4+Jz4DfucOXN49NFHHVWiiMg5C/L14LLe0VzWO5rKahvr9x4mMSWTxNQs9uSUsGpnDqt25jBr4VZiIwMYHRfO6LgIerdugfX3DvPz8jd6Kw24GX56A354yQgGPv3LsXCq20SwnsPC90fyjPmR1r5prMgOxhDBUX+H4Ha/r+7mwt0Lhk03/q7+9u+wdYHxeW75DMbMht6Tzu3amK2s4Fivp4xfIPMXYyhe9UlWhrS4QVgXo4dYZE+I7AERPcG/pePrPgmFUo3gycUplFfZGNQ+hIt7RZ39E5QehtRFxnbvaxu2OBEREZF6mDlzJtOnT6+9X1hYSExMjIkViYicmYeblYSOoSR0DOXvF3djV3ZxzTC/LNbvzSM1o4jUjCJe+X4XYf6eNav5RTCscxh+v2eYn1eA0XNp4K1GOLXmJchOhU9uhPCacCru0voFIJVlsPaNmhX1Cox97YfD2MegVZ9zr7E5C2oNf/ov9LsRvn7AuDYLpxqT1094BqL7ml3hydntRk+5o72ejg7Dy9938uM9A+qGT5E9oWWcMWG/k1Io1cB+SMth8S8ZWC0w+9LuWM5lwrAtnxkJZ3h3iOzV8EWKiIhIsxIZGUlmZmadfZmZmQQGBp60lxSAl5cXXl5ejihPRKTRdGzpT8eW/tw6vCP5RypYvj2bZSmZrNieTU5xBR9v2M/HG4xhfgkdQxkTF86ouAiiW5zjHE3egXD+/caKfT++BmteNSaU/niyMVRqxAyIvfjkE0vbqmFzzYp6hfuNfRE9jBX1Oo3WJN0NocP5cFuSERwu/yccWA9vjYJ+k2H0LHOHQ1aVG2HZ0d5PR3tClRec/PigNseCp8iexu9Ki7Yu1/NLoVQDqqy2MfvLrQD8+by2xEWd4ySgyTUr7fW+Vv/hERERkd8tISGBxYsX19m3dOlSEhISTKpIRMTxWvh6MrFPNBP7GMP81u3JY1lKFompmfyae4QVO7JZsSObh7/YSlxUIGNqhvn1ig46+2F+3kFGADXor0Yw9eNrRk+X+X82AoQRD0HX8cbfe3b7sRX1MmsWpAhsbayo1+sqrajX0Nw8YPBU6PlHY+L4zfONHlPbvoBRD0O/Gxr/My/JNYbcHT8EL2c72KpOPNbqAeGxRoeVo+FTZI8mMyeWVt9rQCXlVTyxOIVvt2awbPr5tPA9h7mgsrfDKwONcZ/3poJ/eIPWKCIiImfHGVedKy4uJi3NmL+yT58+PP/884wcOZKQkBDatGnDzJkzOXDgAO+++y4Ae/bsoUePHkyZMoWbbrqJ7777jjvvvJNFixYxbty4er2mM34OIiINwW63k5ZVbARUKZls3HeY4xbzo2WAF6O6hjM6LpyhncPObc7gI3nGhNs/vQ4Vxca+qN4w8Bajd9SeFcY+ryAYfq8xDFAr6jnGrz/A4vuPBYJR8TDhWYgZ+Puf22aDw3uO6/1UMwyv8MDJj/cJrgmeeh7rARXWxSXnma5vu0GhVCMoKqskwNvj3E5eOgtWz4Uu4+HaeQ1al4iIiJw9Zwxjli9fzsiRI0/YP3nyZP773/9yww03sHfvXpYvX17nnHvuuYdt27bRunVrHn74YW644YZ6v6Yzfg4iIo0hr6SC71ONHlQrd+RQXH6s94qnu5UhHUMZXbOaX1TQWQZHJbnGfFM/vQmVJcf2u3kaQdSwe7Winhmqq2D9O8bQyaPD5Xr/2ZgMvb4TglccMSYbP37lu8ytx0LI3wpuXxM89To2B1RgdJMZLaVQ6hScukFlq4YXukPRIbjyXeh2mdkViYiINHtO3XZwIH0OItIcVVTZ+GlPLokpWSxLyWT/4dI6j3dvFcjouAjGxIXTo9VZDPMryYEfXjSmbuk4Ckb+DYLbNsI7kLNSnA2Js+Hn94z7XkEw8iFjdUW343rIFWUeW/XuaA+o3DSw2058TndvCO923ATkPSGiuzE5fhOmUOoUnLpBlbYM3rvC6LJ373Zj+UoRERExlVO3HRxIn4OINHd2u50dmcUsS8kkMSWTn9PzOf6v6fAAL0bHhTM6NoIhncLw8dRcUC5r/3pYdC8cSjbuh3c3JpvP3GoEUCVZJz/Pr2VNz6cex3pAhXSsG2g1E/VtNzS/T8aZHZ3gvOefFEiJiIiIiIg4EYvFQtfIALpGBjBlZCdyisuNYX4pWazamU1WUTkfrk3nw7XpeLlbGdoprHaYX0Sgt9nly9lo3R9u+Q42vguJj0LWVuN2lMUKoZ1/s/pdTwiIMK9mF6WeUs6iNB+e6wpVZXDL9xDd1+yKREREBCduOziYPgcRkVMrr6rmx915JKZkkpiSxYH8usP8ekYHMTounDFxEXRvFYilicwb1CwcnaS+LL9m5bteEB4Hnr5mV+bUNHzvFJy2QbX+P/DV3dAyDu5Y02QmNxMREXF1Ttt2cDB9DiIi9WO320nNKCIxJZNlKVls2l93mF9koDej4sIZExfO4I5heHtomJ80PRq+52qODt3rfa0CKRERERERERdlsViIiwokLiqQqaM6k11kDPNblpLJqp05ZBSW8cFP+/jgp334eLgxqEMIQzuFMbRzGF0jAtSLSpoVhVLOICcN9q81xqX2utLsakRERERERKSBtAzw4soBMVw5IIayymrW7M6tHeZ3qKCM5duzWb49u/bYoZ3CakMqzUUlTZ1CKWewqaaXVKcxEBBpbi0iIiIiIiLSKLw93BjZNZyRXcN5/DJjmF/SzhxWpeWwdk8u2UXlLPj5AAt+PgBA53B/hnY2QqpBHULx99Kf8NK06DfabLZq2DTP2O59rbm1iIiIiIiIiEMcP8zvluEdKK+qZsOvh0namcPqtBw2HyhgZ1YxO7OK+c/qvbhbLfRtE8yQml5U8a2DcHezmv02RH4XhVJm27MCCg+AdwvoMt7sakRERERERMQEXu5uDO4YxuCOYQDkH6ngh125rNqZQ1JaNul5pazdm8favXm8sGwHAd7uJHQIre1J1T7MT/NRictRKGW25A+Nnz2uAA+NFxYRERERERFo4evJhJ5RTOgZBcC+3COsSssmaWcOP+zKpaC0km+3ZfLttkwAolv4MLRTGEM6hzGkYyih/l5mli9SLwqlzFRWAClfGtu9J5lbi4iIiIiIiDitNqG+TApty6RBbam22dlyoICktBySduaw4dfDHMgvZf76dOavTwege6vA2gnTB7QLwdvDzeR3IHIihVJm2vo5VJVCWFeI7mt2NSIiIiIiIuIC3KwW4mNaEB/TgikjO3Gkooq1e/JI2plDUloOqRlFbD1YyNaDhbyxcjee7lYGtAtmaKeWDOscRreoQKxWDfUT8ymUMtOmmqF7va8Bjf0VERERERGRc+Dr6c6IruGM6BoOQFZRGT+kHZuPKrOwnNVpuaxOy+WpJRDs68HgTmEMq+lJ1TrY1+R3IM2VQimz5O6CfWvAYoVeV5ldjYiIiIiIiDQR4QHeTOwTzcQ+0djtdnZlF7OqZlW/NbtyOXykkkWbD7Fo8yEA2oX61kyY3pKEjqEE+XiY/A6kuVAoZZajvaQ6joLAVubWIiIiIiIiIk2SxWKhU3gAncIDuHFIeyqrbSSn59cO9UtOz2dv7hH25u7jvR/3YbVAr9YtGNY5jCGdwujbJhhPd6vZb0OaKIVSZrDZYNM8Y7v3tebWIiIiIiIiIs2Gh5uVAe1CGNAuhHvGdqGwrJIfd+WyOi2HVWk57M4uITk9n+T0fF76Lg1fTzcGtQ9haOeWDO0URpcIfyyafkYaiEIpM+xdBQXp4BUEXS8yuxoRERERERFppgK9PbigeyQXdI8E4EB+KatrelGtTssht6SC77dn8/32bADCA7wY2snoRTW0cxgRgd5mli8uTqGUGZI/MH72+AN46B+wiIiIiIiIOIfoFj5cOSCGKwfEYLPZSckoNHpR7cxh7Z48sorK+eznA3z28wEAukT4M6RTGMM6hzGofSh+XooZpP702+Jo5UWQstDY7j3J3FpERERERERETsFqtdC9VRDdWwVx6/COlFVWs/HXw6xKyyFpZw5bDhawI7OYHZnF/Gf1XtytFvq2CWZwp1DO6xBKnzYt8HJ3M/ttiBNTKOVo276AyiMQ2gla9ze7GhEREREREZF68fZwY3CnMAZ3CuPBC+FwSQU/7MolKS2bVTtz2H+4lLV781i7Nw/YiZe7lb5tgjmvQyjndQiht0Iq+Q2FUo52dOhe72tBk8OJiIiIiIiIiwr28+SiXlFc1CsKu93OvrwjrNqZw4+7c/lxdx45xeWs2Z3Lmt25AHi5W+nX9mhIFUp8TJBCqmZOoZQj5e2BX1cDFuh1tdnViIiIiIiIiDQIi8VC21A/2ob68efz2mK329mVXcKa3bn8uDuXn3bnklNs9Kz6YVfdkCqhQyjndQylV2uFVM2NQilH2jTP+NlhBARFm1qKiIiIiIiISGOxWCx0CvenU7g/19WGVMWs2Z3Hj7uMoCq35LiQail4e9T0pGpvhFTxrVvg6W41+61II1Io5Sg2G2w6OnRPE5yLiIiIiIhI82GEVAF0Cg+oDanSsoprh/odDalWp+WyOu1YSNW/bQjndQjhvA6h9FJI1eQolHKUX1dD/j7wCoTYi8yuRkRERERERMQ0FouFzhEBdI4I4LqEdtjtdnbWhlRGUJVXUkFSWg5JaTkA+Hi40b/dsYnTe0YrpHJ1CqUcZdOHxs/ul4Onr7m1iIiIiIiIiDgRi8VCl4gAukQEcH1CO2w2O2nZxaypGer30x4jpFq1M4dVO08WUhlzUnm4KaRyJQqlHKG8GLZ+bmz3vtbUUkREREREREScndV6LKSaPNgIqer2pMrl8JHKOiGVr6dbndX9FFI5P4VSjpCyECpLIKQDxAwyuxoRERERERERl2K1WugaGUDXyGMh1Y6soppJ0/P4ac/JQ6r+7Y7NSdUzWiGVs1Eo5QjJRyc4vxYsFnNrEREREREREXFxVquF2MhAYiMDuWFI+9qQ6vjhfvlHKlm5I5uVO7IB8KsNqY7OSRWEu0IqUymUamyHf4W9qwAL9Lra7GpEREREREREmpzjQ6oba0Kq7ZlF/Lg7lzW7jJCqoLSSFTuyWfGbkCqhozHcr0erQIVUDqZQqrFtmmf8bD8cWsSYW4uIiIiIiIhIM2C1WoiLCiQu6lhIlZpRVDsf1alCqgHtQ2rnpFJI1fgUSjUmux02HR26N8ncWkRERERERESaKavVQrdWgXRrFchNQ42QKiWjkB935xkh1e5cCsuqWL49m+XbjZDK38udATWr+/VtG0xsZAAB3h4mv5OmRaFUY9q3Bg7vBU9/iLvY7GpEREREREREBCOk6t4qiO6tgvjL0PZU2+ykZhTWzEmVx9o9Rkj1/fZsvq8JqQBaB/sYPbAiA4iNCiQ2MoC2oX64WTV/9LlQKNWYkt83fnafCJ5+ppYiIiIiIiIiIifndlxIdfOwDlTb7KQcKqwZ7pfH1oMFHCooY//hUvYfLmXptszac3083OgSGUBcZABxNUFVbGQgQb7qVXUmCqUaS0UJbP3c2NbQPRERERERERGX4Wa10CM6iB7RRkgFcLikgtSMIlIzCkk5VEhqRhHbM4ooraxmU3o+m9Lz6zxHdAsfI6CKOhpWBdI+TL2qjqdQqrGkfAUVxRDcDtokmF2NiIiIiIiIiPwOwX6eJHQMJaFjaO2+apudPTklpGYUknroaGBVxIH80tpbYmpW7fFe7la6RgbU9qaKjQogLjKQYD9PM96S6RRKNZajQ/firwWLUlARERERERGRpsbNaqFTuD+dwv25uNex/QWllWzPKKrpUWUEVUd7VW3eX8Dm/QV1nicy0Pu4HlXGzw5hfk1+9T+FUo0hPx32rDS24682txYRERERERERcaggHw8Gtg9hYPuQ2n02m51f846QeqiQlOMCq/S8UjIKy8goLKtd+Q/A081K5wh/YiMDiTsusAr19zLjLTUKhVKNYfM8wA7thkFwW7OrERERERERERGTWa0W2of50T7Mj/E9o2r3F5XV9KrKKCK1Zq6q1EOFlFRUs/VgIVsPFtZ5npYBXsetAGgMA+zY0h9Pd9frVaVQqqHZ7ZD8gbHd+1pzaxERERERERERpxbg7UH/diH0b1e3V9X+w6WkHJ1UvWa+qr25R8guKie7KJuVO471qvJws9Cxpb8RVkUdm68qPMDbjLdUbwqlGlr6T5C3Gzz8IO5Ss6sRERERERERERdjtVpoE+pLm1BfxnWPrN1fUl7F9syi4yZVNwKrovKqmpUBi1jw87HnCfP3NAKqmnmqYqMC6BTuj5e7mwnv6kQKpRra0QnOu08EL39TSxERERERERGRpsPPy52+bYLp2ya4dp/dbudAfikph44N/0s5VMie3BJyiitISsshKS2n9ng3q4WOLf2Iiwrk3rFdaRPqa8ZbARRKNazKUtj6ubEdf42ppYiIiIiIiIhI02exWGgd7EvrYF/Gdouo3V9aUc2OzKLa1f9SDhk9qwrLqtiRWcyOzGJmjI81sXKFUg3L3Rtu+ApSvoS2Q8yuRkRERERERESaKR9PN+JjWhAf06J2n91u51BBGakZhezMLCYy0Nw5pxRKNSSLBaLijZuIiIiIiIiIiBOxWCy0auFDqxY+jIqNOPMJjcwp1gt85ZVXaNeuHd7e3gwaNIi1a9ee8ti33nqLYcOGERwcTHBwMGPGjDnt8SIiIiIiIiIi4nxMD6Xmz5/P9OnTmTVrFhs3biQ+Pp5x48aRlZV10uOXL1/ONddcw/fff8+aNWuIiYnhggsu4MCBAw6uXEREREREREREzpXFbrfbzSxg0KBBDBgwgJdffhkAm81GTEwM06ZNY8aMGWc8v7q6muDgYF5++WWuv/76Mx5fWFhIUFAQBQUFBAYG/u76RUREpGlT28Ggz0FERETqq77tBlN7SlVUVLBhwwbGjBlTu89qtTJmzBjWrFlTr+c4cuQIlZWVhISENFaZIiIiIiIiIiLSwEyd6DwnJ4fq6moiIupOrhUREUFqamq9nuPBBx+kVatWdYKt45WXl1NeXl57v7Cw8NwLFhERERERERGRBmH6nFK/xz//+U/mzZvHggUL8PY++TKGc+bMISgoqPYWExPj4CpFREREREREROS3TA2lwsLCcHNzIzMzs87+zMxMIiMjT3vus88+yz//+U++/fZbevXqdcrjZs6cSUFBQe0tPT29QWoXEREREREREZFzZ2oo5enpSb9+/UhMTKzdZ7PZSExMJCEh4ZTnPf300zz++OMsWbKE/v37n/Y1vLy8CAwMrHMTERERERERERFzmTqnFMD06dOZPHky/fv3Z+DAgcydO5eSkhJuvPFGAK6//nqio6OZM2cOAE899RSPPPIIH3zwAe3atSMjIwMAf39//P39TXsfIiIiIiIiIiJSf6aHUldddRXZ2dk88sgjZGRk0Lt3b5YsWVI7+fm+ffuwWo916HrttdeoqKjgj3/8Y53nmTVrFrNnz3Zk6SIiIiIiIiIico4sdrvdbnYRjlRYWEhQUBAFBQUayiciIiJnpLaDQZ+DiIiI1Fd92w0uvfqeiIiIiIiIiIi4JoVSIiIiIiIiIiLicAqlRERERERERETE4RRKiYiIiIiIiIiIw5m++p6jHZ3XvbCw0ORKRERExBUcbTM0s7VhTqA2lIiIiNRXfdtPzS6UKioqAiAmJsbkSkRERMSVFBUVERQUZHYZplEbSkRERM7WmdpPFnsz+9rPZrNx8OBBAgICsFgsDf78hYWFxMTEkJ6eruWSnZyulWvQdXINuk6uQdfp3NjtdoqKimjVqhVWa/Od+UBtKAFdJ1eh6+Q6dK1cg67T2atv+6nZ9ZSyWq20bt260V8nMDBQv6wuQtfKNeg6uQZdJ9eg63T2mnMPqaPUhpLj6Tq5Bl0n16Fr5Rp0nc5OfdpPzffrPhERERERERERMY1CKRERERERERERcTiFUg3My8uLWbNm4eXlZXYpcga6Vq5B18k16Dq5Bl0ncWb6/XQNuk6uQdfJdehauQZdp8bT7CY6FxERERERERER86mnlIiIiIiIiIiIOJxCKRERERERERERcTiFUiIiIiIiIiIi4nAKpRrYK6+8Qrt27fD29mbQoEGsXbvW7JLkOHPmzGHAgAEEBAQQHh7OxIkT2b59u9llyRn885//xGKxcPfdd5tdipzEgQMH+POf/0xoaCg+Pj707NmT9evXm12WHKe6upqHH36Y9u3b4+PjQ8eOHXn88cfRtJLiLNR+cn5qQ7kmtaGcl9pPzk/tJ8dQKNWA5s+fz/Tp05k1axYbN24kPj6ecePGkZWVZXZpUmPFihVMmTKFH3/8kaVLl1JZWckFF1xASUmJ2aXJKaxbt4433niDXr16mV2KnMThw4cZMmQIHh4efP3112zbto3nnnuO4OBgs0uT4zz11FO89tprvPzyy6SkpPDUU0/x9NNP89JLL5ldmojaTy5CbSjXozaU81L7yTWo/eQYWn2vAQ0aNIgBAwbw8ssvA2Cz2YiJiWHatGnMmDHD5OrkZLKzswkPD2fFihUMHz7c7HLkN4qLi+nbty+vvvoq//jHP+jduzdz5841uyw5zowZM1i9ejWrVq0yuxQ5jYsvvpiIiAjefvvt2n1XXHEFPj4+vPfeeyZWJqL2k6tSG8q5qQ3l3NR+cg1qPzmGeko1kIqKCjZs2MCYMWNq91mtVsaMGcOaNWtMrExOp6CgAICQkBCTK5GTmTJlChdddFGdf1fiXBYuXEj//v3505/+RHh4OH369OGtt94yuyz5jcGDB5OYmMiOHTsA2LRpE0lJSYwfP97kyqS5U/vJdakN5dzUhnJuaj+5BrWfHMPd7AKaipycHKqrq4mIiKizPyIigtTUVJOqktOx2WzcfffdDBkyhB49ephdjvzGvHnz2LhxI+vWrTO7FDmN3bt389prrzF9+nQeeugh1q1bx5133omnpyeTJ082uzypMWPGDAoLC4mNjcXNzY3q6mqeeOIJJk2aZHZp0syp/eSa1IZybmpDOT+1n1yD2k+OoVBKmq0pU6awZcsWkpKSzC5FfiM9PZ277rqLpUuX4u3tbXY5cho2m43+/fvz5JNPAtCnTx+2bNnC66+/rkaVE/noo494//33+eCDD+jevTvJycncfffdtGrVStdJRM6a2lDOS20o16D2k2tQ+8kxFEo1kLCwMNzc3MjMzKyzPzMzk8jISJOqklOZOnUqX331FStXrqR169ZmlyO/sWHDBrKysujbt2/tvurqalauXMnLL79MeXk5bm5uJlYoR0VFRdGtW7c6++Li4vj0009NqkhO5v7772fGjBlcffXVAPTs2ZNff/2VOXPmqFElplL7yfWoDeXc1IZyDWo/uQa1nxxDc0o1EE9PT/r160diYmLtPpvNRmJiIgkJCSZWJsez2+1MnTqVBQsW8N1339G+fXuzS5KTGD16NL/88gvJycm1t/79+zNp0iSSk5PVmHIiQ4YMOWFJ8B07dtC2bVuTKpKTOXLkCFZr3f/lu7m5YbPZTKpIxKD2k+tQG8o1qA3lGtR+cg1qPzmGeko1oOnTpzN58mT69+/PwIEDmTt3LiUlJdx4441mlyY1pkyZwgcffMAXX3xBQEAAGRkZAAQFBeHj42NydXJUQEDACXNU+Pn5ERoaqrkrnMw999zD4MGDefLJJ7nyyitZu3Ytb775Jm+++abZpclxLrnkEp544gnatGlD9+7d+fnnn3n++ee56aabzC5NRO0nF6E2lGtQG8o1qP3kGtR+cgyL3W63m11EU/Lyyy/zzDPPkJGRQe/evXnxxRcZNGiQ2WVJDYvFctL9//nPf7jhhhscW4yclREjRmg5Yyf11VdfMXPmTHbu3En79u2ZPn06t9xyi9llyXGKiop4+OGHWbBgAVlZWbRq1YprrrmGRx55BE9PT7PLE1H7yQWoDeW61IZyTmo/OT+1nxxDoZSIiIiIiIiIiDic5pQSERERERERERGHUyglIiIiIiIiIiIOp1BKREREREREREQcTqGUiIiIiIiIiIg4nEIpERERERERERFxOIVSIiIiIiIiIiLicAqlRERERERERETE4RRKiYiIiIiIiIiIwymUEhH5nSwWC59//rnZZYiIiIi4DLWfRAQUSomIi7vhhhuwWCwn3C688EKzSxMRERFxSmo/iYizcDe7ABGR3+vCCy/kP//5T519Xl5eJlUjIiIi4vzUfhIRZ6CeUiLi8ry8vIiMjKxzCw4OBoyu4a+99hrjx4/Hx8eHDh068Mknn9Q5/5dffmHUqFH4+PgQGhrKrbfeSnFxcZ1j3nnnHbp3746XlxdRUVFMnTq1zuM5OTlcfvnl+Pr60rlzZxYuXNi4b1pERETkd1D7SUScgUIpEWnyHn74Ya644go2bdrEpEmTuPrqq0lJSQGgpKSEcePGERwczLp16/j4449ZtmxZnUbTa6+9xpQpU7j11lv55ZdfWLhwIZ06darzGo8++ihXXnklmzdvZsKECUyaNIm8vDyHvk8RERGRhqL2k4g4hF1ExIVNnjzZ7ubmZvfz86tze+KJJ+x2u90O2G+77bY65wwaNMh+++232+12u/3NN9+0BwcH24uLi2sfX7Rokd1qtdozMjLsdrvd3qpVK/vf/va3U9YA2P/+97/X3i8uLrYD9q+//rrB3qeIiIhIQ1H7SUScheaUEhGXN3LkSF577bU6+0JCQmq3ExIS6jyWkJBAcnIyACkpKcTHx+Pn51f7+JAhQ7DZbGzfvh2LxcLBgwcZPXr0aWvo1atX7bafnx+BgYFkZWWd61sSERERaVRqP4mIM1AoJSIuz8/P74Tu4A3Fx8enXsd5eHjUuW+xWLDZbI1RkoiIiMjvpvaTiDgDzSklIk3ejz/+eML9uLg4AOLi4ti0aRMlJSW1j69evRqr1UrXrl0JCAigXbt2JCYmOrRmERERETOp/SQijqCeUiLi8srLy8nIyKizz93dnbCwMAA+/vhj+vfvz9ChQ3n//fdZu3Ytb7/9NgCTJk1i1qxZTJ48mdmzZ5Odnc20adO47rrriIiIAGD27NncdttthIeHM378eIqKili9ejXTpk1z7BsVERERaSBqP4mIM1AoJSIub8mSJURFRdXZ17VrV1JTUwFjZZd58+Zxxx13EBUVxYcffki3bt0A8PX15ZtvvuGuu+5iwIAB+Pr6csUVV/D888/XPtfkyZMpKyvjhRde4L777iMsLIw//vGPjnuDIiIiIg1M7ScRcQYWu91uN7sIEZHGYrFYWLBgARMnTjS7FBERERGXoPaTiDiK5pQSERERERERERGHUyglIiIiIiIiIiIOp+F7IiIiIiIiIiLicOopJSIiIiIiIiIiDqdQSkREREREREREHE6hlIiIiIiIiIiIOJxCKRERERERERERcTiFUiIiIiIiIiIi4nAKpURERERERERExOEUSomIiIiIiIiIiMMplBIREREREREREYdTKCUiIiIiIiIiIg73/9FhyUa4mU3aAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_graph(cnn_history)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "edlmW0YVUI5b" + }, + "source": [ + "# Resnet(Basic)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "Ggk2xWRSsy-3" + }, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "from tensorflow.keras.applications import ResNet50\n", + "from tensorflow.keras.models import Model\n", + "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D , Dropout\n", + "from tensorflow.keras.optimizers import Adam\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "bYBGWOAPsL1k", + "outputId": "5ff7de24-5683-44dc-a959-e4b69a9b11eb" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n", + "94765736/94765736 [==============================] - 3s 0us/step\n" + ] + } + ], + "source": [ + "base_model = ResNet50(weights='imagenet', include_top=False)\n", + "\n", + "for layer in base_model.layers[-10:]:\n", + " layer.trainable = True\n", + "\n", + "# Add new top layers\n", + "x = base_model.output\n", + "x = GlobalAveragePooling2D()(x)\n", + "x = Dense(1024, activation='relu')(x)\n", + "predictions = Dense(22, activation='softmax')(x)\n", + "\n", + "res_model = Model(inputs=base_model.input, outputs=predictions)\n", + "\n", + "\n", + "# Re-compile the model\n", + "res_model.compile(optimizer=Adam(learning_rate=1e-5),\n", + " loss='categorical_crossentropy',\n", + " metrics=['accuracy'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6ANqeRP8tWV4", + "outputId": "5091ac68-6b19-4668-8584-3b046dd64b7e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "551/551 [==============================] - 150s 201ms/step - loss: 1.3461 - accuracy: 0.6010 - val_loss: 3.0756 - val_accuracy: 0.0867\n", + "Epoch 2/10\n", + "551/551 [==============================] - 104s 188ms/step - loss: 0.5116 - accuracy: 0.8258 - val_loss: 0.5763 - val_accuracy: 0.8042\n", + "Epoch 3/10\n", + "551/551 [==============================] - 110s 200ms/step - loss: 0.3162 - accuracy: 0.8965 - val_loss: 0.4449 - val_accuracy: 0.8479\n", + "Epoch 4/10\n", + "551/551 [==============================] - 104s 189ms/step - loss: 0.2023 - accuracy: 0.9386 - val_loss: 0.4379 - val_accuracy: 0.8522\n", + "Epoch 5/10\n", + "551/551 [==============================] - 104s 187ms/step - loss: 0.1295 - accuracy: 0.9632 - val_loss: 0.4506 - val_accuracy: 0.8542\n", + "Epoch 6/10\n", + "551/551 [==============================] - 104s 187ms/step - loss: 0.0870 - accuracy: 0.9776 - val_loss: 0.4571 - val_accuracy: 0.8601\n", + "Epoch 7/10\n", + "551/551 [==============================] - 104s 188ms/step - loss: 0.0579 - accuracy: 0.9848 - val_loss: 0.4730 - val_accuracy: 0.8562\n", + "Epoch 8/10\n", + "551/551 [==============================] - 104s 189ms/step - loss: 0.0477 - accuracy: 0.9855 - val_loss: 0.5126 - val_accuracy: 0.8617\n", + "Epoch 9/10\n", + "551/551 [==============================] - 104s 188ms/step - loss: 0.0387 - accuracy: 0.9878 - val_loss: 0.5597 - val_accuracy: 0.8491\n", + "Epoch 10/10\n", + "551/551 [==============================] - 104s 188ms/step - loss: 0.0378 - accuracy: 0.9864 - val_loss: 0.5381 - val_accuracy: 0.8495\n" + ] + } + ], + "source": [ + "resnet_history= res_model.fit(train_ds,validation_data=val_ds,epochs=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 468 + }, + "id": "7QLWlY1dvfUU", + "outputId": "5d0f74ca-f30f-47c7-db63-ec68d0c1de13" + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_graph(resnet_history)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WGcdJoLHvjxd" + }, + "source": [ + "# ResNet(Perfect) with Data Augumentaion and Regularizations" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6kfaFjzVemsj", + "outputId": "6cff68f7-0fe9-460a-955f-97608240456a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 17605 images belonging to 22 classes.\n", + "Found 2538 images belonging to 22 classes.\n" + ] + } + ], + "source": [ + "# Data augmentaion\n", + "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", + "\n", + "datagen = ImageDataGenerator(\n", + " rotation_range=20,\n", + " width_shift_range=0.2,\n", + " height_shift_range=0.2,\n", + " shear_range=0.2,\n", + " zoom_range=0.2,\n", + " horizontal_flip=True,\n", + " fill_mode='nearest'\n", + ")\n", + "\n", + "train_generator = datagen.flow_from_directory(\n", + " 'filtered_train',\n", + " target_size=(150,150),\n", + " batch_size=32,\n", + " class_mode='categorical'\n", + ")\n", + "\n", + "val_generator = datagen.flow_from_directory(\n", + " 'filtered_val',\n", + " target_size=(150, 150),\n", + " batch_size=32,\n", + " class_mode='categorical'\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "id": "AsmRa-MUfsCs" + }, + "outputs": [], + "source": [ + "from tensorflow.keras.applications import ResNet50\n", + "from tensorflow.keras.layers import GlobalAveragePooling2D, Dense, Dropout\n", + "from tensorflow.keras.models import Model\n", + "from tensorflow.keras.optimizers import Adam\n", + "from tensorflow.keras.regularizers import l2\n", + "from tensorflow.keras.callbacks import EarlyStopping\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "h7_q9fbffbgd", + "outputId": "065ed344-07fa-4bfe-de27-62018da4e819" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/5\n", + "551/551 [==============================] - 218s 342ms/step - loss: 16.7730 - accuracy: 0.2183 - val_loss: 15.0907 - val_accuracy: 0.5839\n", + "Epoch 2/5\n", + "551/551 [==============================] - 187s 339ms/step - loss: 15.0545 - accuracy: 0.5087 - val_loss: 14.0818 - val_accuracy: 0.7206\n", + "Epoch 3/5\n", + "551/551 [==============================] - 187s 339ms/step - loss: 14.0944 - accuracy: 0.6330 - val_loss: 13.3983 - val_accuracy: 0.7545\n", + "Epoch 4/5\n", + "551/551 [==============================] - 189s 343ms/step - loss: 13.3619 - accuracy: 0.6914 - val_loss: 12.7492 - val_accuracy: 0.7888\n", + "Epoch 5/5\n", + "551/551 [==============================] - 192s 349ms/step - loss: 12.6688 - accuracy: 0.7296 - val_loss: 12.1332 - val_accuracy: 0.8081\n" + ] + } + ], + "source": [ + "# Load the base model\n", + "base_model = ResNet50(weights='imagenet', include_top=False)\n", + "\n", + "# Make the last 10 layers trainable\n", + "for layer in base_model.layers[-10:]:\n", + " layer.trainable = True\n", + "\n", + "# Add new top layers\n", + "x = base_model.output\n", + "x = GlobalAveragePooling2D()(x)\n", + "x = Dropout(0.5)(x) # Adding dropout for regularization\n", + "x = Dense(1024, activation='relu', kernel_regularizer=l2(0.01))(x)\n", + "x = Dropout(0.5)(x) # Adding another dropout layer for regularization\n", + "predictions = Dense(22, activation='softmax', kernel_regularizer=l2(0.01))(x)\n", + "\n", + "res_model_perfect = Model(inputs=base_model.input, outputs=predictions)\n", + "\n", + "# Re-compile the model\n", + "res_model_perfect.compile(optimizer=Adam(learning_rate=1e-5),\n", + " loss='categorical_crossentropy',\n", + " metrics=['accuracy'])\n", + "\n", + "# Early stopping callback\n", + "early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n", + "\n", + "# Train the model\n", + "res_model_perfect_history = res_model_perfect.fit(train_generator, epochs=5, validation_data=val_generator, callbacks=[early_stopping])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "rottw-JQjOq4", + "outputId": "275497e0-d72c-400c-d5e7-b777665d7d68" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 6/10\n", + "551/551 [==============================] - 189s 344ms/step - loss: 11.9784 - accuracy: 0.7541 - val_loss: 11.4686 - val_accuracy: 0.8282\n", + "Epoch 7/10\n", + "551/551 [==============================] - 189s 342ms/step - loss: 11.3271 - accuracy: 0.7718 - val_loss: 10.8296 - val_accuracy: 0.8294\n", + "Epoch 8/10\n", + "551/551 [==============================] - 191s 347ms/step - loss: 10.6584 - accuracy: 0.7876 - val_loss: 10.2008 - val_accuracy: 0.8373\n", + "Epoch 9/10\n", + "551/551 [==============================] - 192s 347ms/step - loss: 10.0059 - accuracy: 0.8040 - val_loss: 9.6002 - val_accuracy: 0.8408\n", + "Epoch 10/10\n", + "551/551 [==============================] - 194s 352ms/step - loss: 9.3686 - accuracy: 0.8173 - val_loss: 8.9783 - val_accuracy: 0.8455\n" + ] + } + ], + "source": [ + "res_model_perfect_history1 = res_model_perfect.fit(train_generator, initial_epoch=5,epochs=10, validation_data=val_generator, callbacks=[early_stopping])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lhkIRX0oEpTj" + }, + "source": [ + "# VGG16 Model" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "v93hTELS5Jzl" + }, + "outputs": [], + "source": [ + "from tensorflow.keras.applications import VGG16\n", + "from tensorflow.keras.layers import GlobalAveragePooling2D, Dense, Dropout\n", + "from tensorflow.keras.models import Model\n", + "from tensorflow.keras.optimizers import SGD\n", + "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "TbOkFGjzEw5p", + "outputId": "d30519f5-efa9-465c-b5bb-f43e9f81e484" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n", + "58889256/58889256 [==============================] - 2s 0us/step\n", + "Model: \"model_2\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " input_3 (InputLayer) [(None, None, None, 3)] 0 \n", + " \n", + " block1_conv1 (Conv2D) (None, None, None, 64) 1792 \n", + " \n", + " block1_conv2 (Conv2D) (None, None, None, 64) 36928 \n", + " \n", + " block1_pool (MaxPooling2D) (None, None, None, 64) 0 \n", + " \n", + " block2_conv1 (Conv2D) (None, None, None, 128) 73856 \n", + " \n", + " block2_conv2 (Conv2D) (None, None, None, 128) 147584 \n", + " \n", + " block2_pool (MaxPooling2D) (None, None, None, 128) 0 \n", + " \n", + " block3_conv1 (Conv2D) (None, None, None, 256) 295168 \n", + " \n", + " block3_conv2 (Conv2D) (None, None, None, 256) 590080 \n", + " \n", + " block3_conv3 (Conv2D) (None, None, None, 256) 590080 \n", + " \n", + " block3_pool (MaxPooling2D) (None, None, None, 256) 0 \n", + " \n", + " block4_conv1 (Conv2D) (None, None, None, 512) 1180160 \n", + " \n", + " block4_conv2 (Conv2D) (None, None, None, 512) 2359808 \n", + " \n", + " block4_conv3 (Conv2D) (None, None, None, 512) 2359808 \n", + " \n", + " block4_pool (MaxPooling2D) (None, None, None, 512) 0 \n", + " \n", + " block5_conv1 (Conv2D) (None, None, None, 512) 2359808 \n", + " \n", + " block5_conv2 (Conv2D) (None, None, None, 512) 2359808 \n", + " \n", + " block5_conv3 (Conv2D) (None, None, None, 512) 2359808 \n", + " \n", + " block5_pool (MaxPooling2D) (None, None, None, 512) 0 \n", + " \n", + " global_average_pooling2d_2 (None, 512) 0 \n", + " (GlobalAveragePooling2D) \n", + " \n", + " dense_4 (Dense) (None, 1024) 525312 \n", + " \n", + " dense_5 (Dense) (None, 22) 22550 \n", + " \n", + "=================================================================\n", + "Total params: 15262550 (58.22 MB)\n", + "Trainable params: 15262550 (58.22 MB)\n", + "Non-trainable params: 0 (0.00 Byte)\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "\n", + "# Load the base model\n", + "base_model = VGG16(weights='imagenet', include_top=False)\n", + "\n", + "# Make more layers trainable\n", + "for layer in base_model.layers[-10:]:\n", + " layer.trainable = True\n", + "\n", + "# Add new top layers\n", + "x = base_model.output\n", + "x = GlobalAveragePooling2D()(x)\n", + "x = Dense(1024, activation='relu')(x)\n", + "predictions = Dense(22, activation='softmax')(x)\n", + "\n", + "# Create the final model\n", + "vgg_model = Model(inputs=base_model.input, outputs=predictions)\n", + "\n", + "vgg_model.compile(optimizer=Adam(learning_rate=1e-5),\n", + " loss='categorical_crossentropy',\n", + " metrics=['accuracy'])\n", + "\n", + "vgg_model.summary()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Nc8DXxH8MvnG", + "outputId": "babb6905-0dab-4fc2-d615-6e08898524ca" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "551/551 [==============================] - 148s 239ms/step - loss: 1.2364 - accuracy: 0.5985 - val_loss: 0.7437 - val_accuracy: 0.7360\n", + "Epoch 2/10\n", + "551/551 [==============================] - 123s 223ms/step - loss: 0.5993 - accuracy: 0.7848 - val_loss: 0.6208 - val_accuracy: 0.7683\n", + "Epoch 3/10\n", + "551/551 [==============================] - 128s 232ms/step - loss: 0.4672 - accuracy: 0.8295 - val_loss: 0.5241 - val_accuracy: 0.8081\n", + "Epoch 4/10\n", + "551/551 [==============================] - 128s 232ms/step - loss: 0.3806 - accuracy: 0.8620 - val_loss: 0.4916 - val_accuracy: 0.8140\n", + "Epoch 5/10\n", + "551/551 [==============================] - 124s 224ms/step - loss: 0.3119 - accuracy: 0.8891 - val_loss: 0.5329 - val_accuracy: 0.8069\n", + "Epoch 6/10\n", + "551/551 [==============================] - 124s 224ms/step - loss: 0.2599 - accuracy: 0.9063 - val_loss: 0.5281 - val_accuracy: 0.8128\n", + "Epoch 7/10\n", + "551/551 [==============================] - 123s 223ms/step - loss: 0.2171 - accuracy: 0.9225 - val_loss: 0.5258 - val_accuracy: 0.8262\n", + "Epoch 8/10\n", + "551/551 [==============================] - 128s 232ms/step - loss: 0.1756 - accuracy: 0.9380 - val_loss: 0.5114 - val_accuracy: 0.8353\n", + "Epoch 9/10\n", + "551/551 [==============================] - 123s 223ms/step - loss: 0.1501 - accuracy: 0.9474 - val_loss: 0.5555 - val_accuracy: 0.8286\n", + "Epoch 10/10\n", + "551/551 [==============================] - 128s 232ms/step - loss: 0.1284 - accuracy: 0.9558 - val_loss: 0.5891 - val_accuracy: 0.8270\n" + ] + } + ], + "source": [ + "vgg_history= vgg_model.fit(train_ds,epochs=10,validation_data=val_ds)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 468 + }, + "id": "5E-06ul8He9M", + "outputId": "210c701f-518b-4253-86ec-f5754d5798eb" + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_graph(vgg_history)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RcmcV-PLnCw_" + }, + "source": [ + "# Model Evaluation and Best model Testing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "P6m-twEGm_AH", + "outputId": "9ecddb0d-3dc8-4cc5-ac29-4f7e77b5aeff" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "157/157 [==============================] - 7s 38ms/step - loss: 1.8495 - accuracy: 0.3898\n", + "ANN Test accuracy: 38.98%\n" + ] + } + ], + "source": [ + "loss_ann, accuracy_ann = ann_model.evaluate(test_ds)\n", + "print(f\"ANN Test accuracy: {accuracy_ann * 100:.2f}%\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "sAs7JCS1MQgL", + "outputId": "19d046a2-6e95-4542-8f60-3aa69acd6ef7" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "157/157 [==============================] - 9s 53ms/step - loss: 0.9678 - accuracy: 0.6992\n", + "CNN Test accuracy: 69.92%\n" + ] + } + ], + "source": [ + "loss_cnn, accuracy_cnn = cnn_model.evaluate(test_ds)\n", + "print(f\"CNN Test accuracy: {accuracy_cnn * 100:.2f}%\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "K2zPnXNaMQP6", + "outputId": "ba65fa34-0993-443c-e39f-71bd03be6a0e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "158/158 [==============================] - 9s 57ms/step - loss: 0.5548 - accuracy: 0.8562\n", + "ResNet Test accuracy: 85.62%\n" + ] + } + ], + "source": [ + "loss_res, accuracy_res = res_model.evaluate(test_ds)\n", + "print(f\"ResNet Test accuracy: {accuracy_res * 100:.2f}%\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vkawaMLVMP6d", + "outputId": "46a0d654-b34a-47b9-bf61-819e60e6f08e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "158/158 [==============================] - 11s 70ms/step - loss: 0.5903 - accuracy: 0.8303\n", + "VGG16 Test accuracy: 83.03%\n" + ] + } + ], + "source": [ + "\n", + "loss_vgg16, accuracy_vgg16 = vgg_model.evaluate(test_ds)\n", + "print(f\"VGG16 Test accuracy: {accuracy_vgg16 * 100:.2f}%\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "PLRg-hzH3xCv", + "outputId": "bd18867b-1445-40f0-d001-b63d62339591" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 5027 images belonging to 22 classes.\n" + ] + } + ], + "source": [ + "# Preparing test data for Renset Perfect model\n", + "test_generator = datagen.flow_from_directory(\n", + " 'filtered_test',\n", + " target_size=(150, 150),\n", + " batch_size=32,\n", + " class_mode='categorical'\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9z53Loe7uKVH", + "outputId": "63799035-a5cb-4279-89f5-69188aefb10e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "158/158 [==============================] - 47s 299ms/step - loss: 8.9857 - accuracy: 0.8472\n", + "ResNet(Perfect) Test accuracy: 84.72%\n" + ] + } + ], + "source": [ + "loss_res_model_perfect, accuracy_res_model_perfect = res_model_perfect.evaluate(test_generator)\n", + "print(f\"ResNet(Perfect) Test accuracy: {accuracy_res_model_perfect * 100:.2f}%\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "swE1qjVO30EQ" + }, + "source": [ + "# Tabulating the Model and it's Accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wG_5qUPadj7N" + }, + "source": [ + " | Model Name | Accuracy (%) |\n", + " |-----------------|--------------|\n", + " | ANN | 38.98 |\n", + " | CNN | 69.92 |\n", + " | VGG16 | 83.03 |\n", + " | ResNet | 85.62 |\n", + " | ResNet(Perfect) | 84.58 |" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2F0_YBHIc1zt" + }, + "source": [ + "- The ResNet(Perfect) model is chosen as the optimal model due to its balance of high accuracy and reduced overfitting, making it the best candidate for deployment. While the standard ResNet model achieved the highest accuracy, its overfitting issues could compromise its performance on new, unseen data. Therefore, for practical applications where generalization is crucial, the ResNet(Perfect) model is the preferred choice\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "_un1ZG_XDvzr" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kaggle": { + "accelerator": "none", + "dataSources": [ + { + "datasetId": 4280832, + "sourceId": 7368427, + "sourceType": "datasetVersion" + } + ], + "dockerImageVersionId": 30699, + "isGpuEnabled": false, + "isInternetEnabled": true, + "language": "python", + "sourceType": "notebook" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} From 8ec1d64a6e4b91f6c0674817449e499f27ed86e7 Mon Sep 17 00:00:00 2001 From: Karthik110505 Date: Wed, 22 May 2024 22:51:59 +0530 Subject: [PATCH 5/6] Solving the ipynb problem --- ..._Pest_and_Disease_Detection_using_DL.ipynb | 6598 ----------------- 1 file changed, 6598 deletions(-) diff --git a/Crop Pest and Disease Detection using DL/Model/Crop_Pest_and_Disease_Detection_using_DL.ipynb b/Crop Pest and Disease Detection using DL/Model/Crop_Pest_and_Disease_Detection_using_DL.ipynb index 04cafd22c..b2539c963 100644 --- a/Crop Pest and Disease Detection using DL/Model/Crop_Pest_and_Disease_Detection_using_DL.ipynb +++ b/Crop Pest and Disease Detection using DL/Model/Crop_Pest_and_Disease_Detection_using_DL.ipynb @@ -1,5 +1,4 @@ { -<<<<<<< HEAD:Crop Pest and Disease Detection using DL/Model/Crop_Pest_and_Disease_Detection_using_DL_.ipynb "cells": [ { "cell_type": "markdown", @@ -7,6603 +6,6 @@ "source": [ "# Note: This notebook is prepared in Google collab" ] -======= - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Note: This notebook is prepared in Google collab" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "IRYcu-UwzQAU" - }, - "source": [ - "# Loading Basic Libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "_ryJq3BVUI5F" - }, - "outputs": [], - "source": [ - "import os\n", - "import cv2\n", - "import tensorflow as tf\n", - "from tensorflow.keras import layers, models\n", - "import matplotlib.pyplot as plt\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "Mukt0YWFjy8U" - }, - "outputs": [], - "source": [ - "# Creating dataset folder to download the dataset\n", - "mkdir dataset\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "OOTLURUXlxxL", - "outputId": "a34cf209-2f49-4270-978f-d51147037fe5" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/content/dataset\n" - ] - } - ], - "source": [ - "# changing current directory path\n", - "cd dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "QieK7HFzidPC", - "outputId": "3c9a5312-d30e-4950-eb6d-1913c3f56a79" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.6.14 / client 1.6.12)\n", - "Dataset URL: https://www.kaggle.com/datasets/nirmalsankalana/crop-pest-and-disease-detection\n", - "License(s): CC0-1.0\n", - "Downloading crop-pest-and-disease-detection.zip to /content/dataset\n", - "100% 1.24G/1.25G [00:46<00:00, 31.4MB/s]\n", - "100% 1.25G/1.25G [00:46<00:00, 29.0MB/s]\n" - ] - } - ], - "source": [ - "# Downloading the dataset\n", - "!kaggle datasets download -d nirmalsankalana/crop-pest-and-disease-detection" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "collapsed": true, - "id": "FhJ3tB5mjVe0", - "outputId": "8b6fff35-753b-441f-ecc9-4095b21eed32" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n", - " inflating: Tomato leaf blight/leaf blight1302_.jpg \n", - " inflating: Tomato leaf blight/leaf blight1303_.jpg \n", - " inflating: Tomato leaf blight/leaf blight1304_.jpg \n", - " inflating: Tomato leaf blight/leaf blight1305_.jpg \n", - " inflating: Tomato leaf blight/leaf blight1306_.jpg \n", - " inflating: Tomato leaf blight/leaf blight1307_.jpg \n", - " inflating: Tomato leaf blight/leaf blight1308_.jpg \n", - " inflating: Tomato leaf blight/leaf blight1309_.jpg \n", - " inflating: Tomato leaf blight/leaf blight130_.jpg \n", - " inflating: Tomato leaf blight/leaf blight1310_.jpg \n", - " inflating: Tomato leaf blight/leaf blight1311_.jpg \n", - 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"source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "def plot_graph(history):\n", - " fig, ax = plt.subplots(1, 2, figsize=(12, 5)) # Create a figure with 1 row and 2 columns\n", - "\n", - " # Plot training & validation accuracy values\n", - " ax[0].plot(history.history['accuracy'])\n", - " ax[0].plot(history.history['val_accuracy'])\n", - " ax[0].set_title('Model accuracy')\n", - " ax[0].set_ylabel('Accuracy')\n", - " ax[0].set_xlabel('Epoch')\n", - " ax[0].legend(['Train', 'Validation'], loc='upper left')\n", - "\n", - " # Plot training & validation loss values\n", - " ax[1].plot(history.history['loss'])\n", - " ax[1].plot(history.history['val_loss'])\n", - " ax[1].set_title('Model loss')\n", - " ax[1].set_ylabel('Loss')\n", - " ax[1].set_xlabel('Epoch')\n", - " ax[1].legend(['Train', 'Validation'], loc='upper left')\n", - "\n", - " plt.tight_layout()\n", - " plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "LOwqeXYpUI5N" - }, - "source": [ - "# Data Cleaning and filter valid image data" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Lc9naWsczqaA" - }, - "source": [ - "The given dataset contains some invalid images. The below code filters the dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "collapsed": true, - "id": "yj3Dw9mWUI5O", - "outputId": "ad848410-2b0e-4988-a53c-458d917fdfae" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 392 image files in dataset/Cashew gumosis\n", - "Copied 274 images to train/Cashew gumosis\n", - "Copied 78 images to test/Cashew gumosis\n", - "Copied 40 images to val/Cashew gumosis\n", - "Found 673 image files in dataset/Maize grasshoper\n", - "Copied 471 images to train/Maize grasshoper\n", - "Copied 134 images to test/Maize grasshoper\n", - "Copied 68 images to val/Maize grasshoper\n", - "Found 1368 image files in dataset/Cashew healthy\n", - "Copied 957 images to train/Cashew healthy\n", - "Copied 273 images to test/Cashew healthy\n", - "Copied 138 images to val/Cashew healthy\n", - "Found 1015 image files in dataset/Cassava green mite\n", - "Copied 710 images to train/Cassava green mite\n", - "Copied 203 images to test/Cassava green mite\n", - "Copied 102 images to val/Cassava green mite\n", - "Found 948 image files in dataset/Maize leaf beetle\n", - "Copied 663 images to train/Maize leaf beetle\n", - "Copied 189 images to test/Maize leaf beetle\n", - "Copied 96 images to val/Maize leaf beetle\n", - "Found 1205 image files in dataset/Cassava mosaic\n", - "Copied 843 images to train/Cassava mosaic\n", - "Copied 241 images to test/Cassava mosaic\n", - "Copied 121 images to val/Cassava mosaic\n", - "Found 1006 image files in dataset/Maize leaf blight\n", - "Copied 704 images to train/Maize leaf blight\n", - "Copied 201 images to test/Maize leaf blight\n", - "Copied 101 images to val/Maize leaf blight\n", - "Found 979 image files in dataset/Maize streak virus\n", - "Copied 685 images to train/Maize streak virus\n", - "Copied 195 images to test/Maize streak virus\n", - "Copied 99 images to val/Maize streak virus\n", - "Found 1259 image files in dataset/Maize leaf spot\n", - "Copied 881 images to train/Maize leaf spot\n", - "Copied 251 images to test/Maize leaf spot\n", - "Copied 127 images to val/Maize leaf spot\n", - "Found 285 image files in dataset/Maize fall armyworm\n", - "Copied 199 images to train/Maize fall armyworm\n", - "Copied 57 images to test/Maize fall armyworm\n", - "Copied 29 images to val/Maize fall armyworm\n", - "Found 470 image files in dataset/Tomato healthy\n", - "Copied 329 images to train/Tomato healthy\n", - "Copied 94 images to test/Tomato healthy\n", - "Copied 47 images to val/Tomato healthy\n", - "Found 1193 image files in dataset/Cassava healthy\n", - "Copied 835 images to train/Cassava healthy\n", - "Copied 238 images to test/Cassava healthy\n", - "Copied 120 images to val/Cassava healthy\n", - "Found 518 image files in dataset/Tomato leaf curl\n", - "Copied 362 images to train/Tomato leaf curl\n", - "Copied 103 images to test/Tomato leaf curl\n", - "Copied 53 images to val/Tomato leaf curl\n", - "Found 1301 image files in dataset/Tomato leaf blight\n", - "Copied 910 images to train/Tomato leaf blight\n", - "Copied 260 images to test/Tomato leaf blight\n", - "Copied 131 images to val/Tomato leaf blight\n", - "Found 1682 image files in dataset/Cashew red rust\n", - "Copied 1177 images to train/Cashew red rust\n", - "Copied 336 images to test/Cashew red rust\n", - "Copied 169 images to val/Cashew red rust\n", - "Found 1481 image files in dataset/Cassava brown spot\n", - "Copied 1036 images to train/Cassava brown spot\n", - "Copied 296 images to test/Cassava brown spot\n", - "Copied 149 images to val/Cassava brown spot\n", - "Found 773 image files in dataset/Tomato verticulium wilt\n", - "Copied 541 images to train/Tomato verticulium wilt\n", - "Copied 154 images to test/Tomato verticulium wilt\n", - "Copied 78 images to val/Tomato verticulium wilt\n", - "Found 1729 image files in dataset/Cashew anthracnose\n", - "Copied 1210 images to train/Cashew anthracnose\n", - "Copied 345 images to test/Cashew anthracnose\n", - "Copied 174 images to val/Cashew anthracnose\n", - "Found 2743 image files in dataset/Tomato septoria leaf spot\n", - "Copied 1920 images to train/Tomato septoria leaf spot\n", - "Copied 548 images to test/Tomato septoria leaf spot\n", - "Copied 275 images to val/Tomato septoria leaf spot\n", - "Found 1378 image files in dataset/Cashew leaf miner\n", - "Copied 964 images to train/Cashew leaf miner\n", - "Copied 275 images to test/Cashew leaf miner\n", - "Copied 139 images to val/Cashew leaf miner\n", - "Found 208 image files in dataset/Maize healthy\n", - "Copied 145 images to train/Maize healthy\n", - "Copied 41 images to test/Maize healthy\n", - "Copied 22 images to val/Maize healthy\n", - "Found 2614 image files in dataset/Cassava bacterial blight\n", - "Copied 1829 images to train/Cassava bacterial blight\n", - "Copied 522 images to test/Cassava bacterial blight\n", - "Copied 263 images to val/Cassava bacterial blight\n" - ] - } - ], - "source": [ - "import random\n", - "import shutil\n", - "\n", - "# Set the source directory\n", - "src_dir = 'dataset'\n", - "\n", - "# Set the destination directories\n", - "train_dir = 'train'\n", - "test_dir = 'test'\n", - "val_dir = 'val'\n", - "\n", - "# Set the split ratios\n", - "train_ratio = 0.7\n", - "test_ratio = 0.2\n", - "val_ratio = 0.1\n", - "\n", - "# Create the destination directories if they don't exist\n", - "os.makedirs(train_dir, exist_ok=True)\n", - "os.makedirs(test_dir, exist_ok=True)\n", - "os.makedirs(val_dir, exist_ok=True)\n", - "\n", - "# Get a list of all subdirectories in the source directory\n", - "subdirs = [d for d in os.listdir(src_dir) if os.path.isdir(os.path.join(src_dir, d))]\n", - "\n", - "for subdir in subdirs:\n", - " subdir_path = os.path.join(src_dir, subdir)\n", - " image_files = [f for f in os.listdir(subdir_path) if f.endswith('.jpg') or f.endswith('.png')]\n", - "\n", - " print(f\"Found {len(image_files)} image files in {subdir_path}\")\n", - "\n", - " # If no image files found in the subdirectory, skip it\n", - " if not image_files:\n", - " print(f\"No image files found in {subdir_path}. Skipping...\")\n", - " continue\n", - "\n", - " # Shuffle the list of image files\n", - " random.shuffle(image_files)\n", - "\n", - " # Calculate the number of images for each set\n", - " num_images = len(image_files)\n", - " num_train = int(num_images * train_ratio)\n", - " num_test = int(num_images * test_ratio)\n", - " num_val = num_images - num_train - num_test\n", - "\n", - " # Create subdirectories for train, test, and val sets\n", - " train_subdir = os.path.join(train_dir, subdir)\n", - " test_subdir = os.path.join(test_dir, subdir)\n", - " val_subdir = os.path.join(val_dir, subdir)\n", - "\n", - " os.makedirs(train_subdir, exist_ok=True)\n", - " os.makedirs(test_subdir, exist_ok=True)\n", - " os.makedirs(val_subdir, exist_ok=True)\n", - "\n", - " # Copy the images to their respective sets\n", - " train_files = image_files[:num_train]\n", - " test_files = image_files[num_train:num_train+num_test]\n", - " val_files = image_files[num_train+num_test:]\n", - "\n", - " for file in train_files:\n", - " src_path = os.path.join(subdir_path, file)\n", - " dst_path = os.path.join(train_subdir, file)\n", - " shutil.copy(src_path, dst_path)\n", - "\n", - " for file in test_files:\n", - " src_path = os.path.join(subdir_path, file)\n", - " dst_path = os.path.join(test_subdir, file)\n", - " shutil.copy(src_path, dst_path)\n", - "\n", - " for file in val_files:\n", - " src_path = os.path.join(subdir_path, file)\n", - " dst_path = os.path.join(val_subdir, file)\n", - " shutil.copy(src_path, dst_path)\n", - "\n", - " print(f\"Copied {num_train} images to {train_subdir}\")\n", - " print(f\"Copied {num_test} images to {test_subdir}\")\n", - " print(f\"Copied {num_val} images to {val_subdir}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true, - "id": "KwzUAq8xUI5Q" - }, - "outputs": [], - "source": [ - "# Function to check if an image is valid using OpenCV\n", - "def is_image_valid(image_path):\n", - " try:\n", - " img = cv2.imread(image_path)\n", - " if img is None:\n", - " return False\n", - " return True\n", - " except:\n", - " return False\n", - "\n", - "# Function to filter out corrupted images\n", - "def filter_and_save_valid_images(source_dir, target_dir):\n", - " if not os.path.exists(target_dir):\n", - " os.makedirs(target_dir)\n", - "\n", - " for root, _, files in os.walk(source_dir):\n", - " for file in files:\n", - " file_path = os.path.join(root, file)\n", - " if is_image_valid(file_path):\n", - " relative_path = os.path.relpath(file_path, source_dir)\n", - " target_path = os.path.join(target_dir, relative_path)\n", - " target_subdir = os.path.dirname(target_path)\n", - " if not os.path.exists(target_subdir):\n", - " os.makedirs(target_subdir)\n", - " cv2.imwrite(target_path, cv2.imread(file_path))\n", - "\n", - "# Specify the source and target directories\n", - "train_source_dir = 'train'\n", - "train_target_dir = 'filtered_train'\n", - "val_source_dir = 'val'\n", - "val_target_dir = 'filtered_val'\n", - "test_source_dir = 'test'\n", - "test_target_dir = 'filtered_test'\n", - "\n", - "# Filter and save valid images\n", - "filter_and_save_valid_images(train_source_dir, train_target_dir)\n", - "filter_and_save_valid_images(val_source_dir, val_target_dir)\n", - "filter_and_save_valid_images(test_source_dir, test_target_dir)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "aKljc92wz7Es" - }, - "source": [ - "# Loading the dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "kIbRzkJ5UI5R", - "outputId": "a0820fa1-f15a-4f26-a72e-6c8350757de6" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 17605 files belonging to 22 classes.\n", - "Found 2538 files belonging to 22 classes.\n" - ] - } - ], - "source": [ - "\n", - "# Function to load and preprocess the dataset\n", - "def load_and_preprocess_dataset(directory, image_size=(150, 150), batch_size=32):\n", - " dataset = tf.keras.utils.image_dataset_from_directory(\n", - " directory=directory,\n", - " labels='inferred',\n", - " label_mode='categorical',\n", - " batch_size=batch_size,\n", - " image_size=image_size)\n", - "\n", - " class_names = dataset.class_names\n", - "\n", - " # Define a preprocessing function to scale the images\n", - " def preprocess(image, label):\n", - " # Rescale the image pixel values from [0, 255] to [0, 1]\n", - " image = tf.cast(image, tf.float32) / 255.0\n", - " return image, label\n", - "\n", - " # Apply the preprocessing function to the dataset\n", - " dataset = dataset.map(preprocess, num_parallel_calls=tf.data.AUTOTUNE)\n", - " return dataset, class_names\n", - "\n", - "# Load the datasets\n", - "train_ds, train_class_names = load_and_preprocess_dataset(train_target_dir)\n", - "val_ds, val_class_names = load_and_preprocess_dataset(val_target_dir)\n", - "\n", - "# Ensure the class names are consistent\n", - "assert train_class_names == val_class_names, \"Class names in training and validation datasets do not match.\"\n", - "\n", - "# Prefetching to improve performance\n", - "train_ds = train_ds.prefetch(buffer_size=tf.data.AUTOTUNE)\n", - "val_ds = val_ds.prefetch(buffer_size=tf.data.AUTOTUNE)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "3SStCzWbUI5S", - "outputId": "32ff7f88-d132-427a-d79a-40ae71c41ea9" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 5027 files belonging to 22 classes.\n" - ] - } - ], - "source": [ - "test_ds, test_class_names = load_and_preprocess_dataset(test_target_dir)\n", - "test_ds = test_ds.prefetch(buffer_size=tf.data.AUTOTUNE)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "GOC_HjrYUI5U" - }, - "source": [ - "# ANN model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "MvkyNR5CUI5V", - "outputId": "d44f1c8f-194e-40b6-ccd6-df8c1f68712c" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"sequential\"\n", - "_________________________________________________________________\n", - " Layer (type) Output Shape Param # \n", - "=================================================================\n", - " flatten (Flatten) (None, 67500) 0 \n", - " \n", - " dense (Dense) (None, 512) 34560512 \n", - " \n", - " dense_1 (Dense) (None, 256) 131328 \n", - " \n", - " dense_2 (Dense) (None, 22) 5654 \n", - " \n", - "=================================================================\n", - "Total params: 34697494 (132.36 MB)\n", - "Trainable params: 34697494 (132.36 MB)\n", - "Non-trainable params: 0 (0.00 Byte)\n", - "_________________________________________________________________\n" - ] - } - ], - "source": [ - "from tensorflow.keras import layers, models\n", - "ann_model = models.Sequential([\n", - " layers.Flatten(input_shape=(150,150,3)), # Input layer flattens the image\n", - " layers.Dense(512, activation='relu'), # Hidden layer with 512 units and ReLU activation\n", - " layers.Dense(256, activation='relu'), # Hidden layer with 256 units and ReLU activation\n", - " layers.Dense(22, activation='softmax') # Output layer with softmax activation for classification\n", - "])\n", - "\n", - "# Compile the model\n", - "ann_model.compile(optimizer='adam',\n", - " loss='categorical_crossentropy',\n", - " metrics=['accuracy'])\n", - "\n", - "ann_model.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "cWbWa40UUI5V", - "outputId": "f5c99b23-20d6-4298-e480-a68e572dad6c" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/10\n", - "551/551 [==============================] - 32s 56ms/step - loss: 2.5090 - accuracy: 0.2331 - val_loss: 2.4573 - val_accuracy: 0.2368\n", - "Epoch 2/10\n", - "551/551 [==============================] - 35s 62ms/step - loss: 2.2748 - accuracy: 0.2769 - val_loss: 2.1671 - val_accuracy: 0.3073\n", - "Epoch 3/10\n", - "551/551 [==============================] - 31s 55ms/step - loss: 2.1548 - accuracy: 0.3088 - val_loss: 2.1161 - val_accuracy: 0.3180\n", - "Epoch 4/10\n", - "551/551 [==============================] - 29s 51ms/step - loss: 2.0701 - accuracy: 0.3264 - val_loss: 2.0176 - val_accuracy: 0.3416\n", - "Epoch 5/10\n", - "551/551 [==============================] - 29s 52ms/step - loss: 2.0042 - accuracy: 0.3417 - val_loss: 2.0083 - val_accuracy: 0.3424\n", - "Epoch 6/10\n", - "551/551 [==============================] - 28s 51ms/step - loss: 1.9771 - accuracy: 0.3518 - val_loss: 1.9500 - val_accuracy: 0.3660\n", - "Epoch 7/10\n", - "551/551 [==============================] - 29s 51ms/step - loss: 1.9136 - accuracy: 0.3694 - val_loss: 1.8914 - val_accuracy: 0.3881\n", - "Epoch 8/10\n", - "551/551 [==============================] - 31s 56ms/step - loss: 1.8748 - accuracy: 0.3789 - val_loss: 2.0321 - val_accuracy: 0.3416\n", - "Epoch 9/10\n", - "551/551 [==============================] - 31s 56ms/step - loss: 1.8413 - accuracy: 0.3881 - val_loss: 1.9444 - val_accuracy: 0.3751\n", - "Epoch 10/10\n", - "551/551 [==============================] - 29s 51ms/step - loss: 1.8477 - accuracy: 0.3902 - val_loss: 1.8945 - val_accuracy: 0.3822\n" - ] - } - ], - "source": [ - "ann_history= ann_model.fit(train_ds,validation_data=val_ds,epochs=10)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 507 - }, - "id": "N1G8-LeCBU8d", - "outputId": "6d6717f7-e10d-4757-e619-71b63f3e2338" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_graph(ann_history)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "dlI2JELTUI5W" - }, - "source": [ - "# Basic CNN model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "WmkAduHdUI5W" - }, - "outputs": [], - "source": [ - "from keras.optimizers import Adam" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "O9R68g_lUI5W", - "outputId": "21d1cdb3-56ea-480d-e662-375c82003429" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"sequential_1\"\n", - "_________________________________________________________________\n", - " Layer (type) Output Shape Param # \n", - "=================================================================\n", - " conv2d (Conv2D) (None, 148, 148, 32) 896 \n", - " \n", - " batch_normalization (Batch (None, 148, 148, 32) 128 \n", - " Normalization) \n", - " \n", - " max_pooling2d (MaxPooling2 (None, 74, 74, 32) 0 \n", - " D) \n", - " \n", - " conv2d_1 (Conv2D) (None, 72, 72, 64) 18496 \n", - " \n", - " batch_normalization_1 (Bat (None, 72, 72, 64) 256 \n", - " chNormalization) \n", - " \n", - " conv2d_2 (Conv2D) (None, 70, 70, 64) 36928 \n", - " \n", - " batch_normalization_2 (Bat (None, 70, 70, 64) 256 \n", - " chNormalization) \n", - " \n", - " max_pooling2d_1 (MaxPoolin (None, 35, 35, 64) 0 \n", - " g2D) \n", - " \n", - " conv2d_3 (Conv2D) (None, 33, 33, 128) 73856 \n", - " \n", - " batch_normalization_3 (Bat (None, 33, 33, 128) 512 \n", - " chNormalization) \n", - " \n", - " max_pooling2d_2 (MaxPoolin (None, 16, 16, 128) 0 \n", - " g2D) \n", - " \n", - " conv2d_4 (Conv2D) (None, 14, 14, 128) 147584 \n", - " \n", - " batch_normalization_4 (Bat (None, 14, 14, 128) 512 \n", - " chNormalization) \n", - " \n", - " conv2d_5 (Conv2D) (None, 12, 12, 128) 147584 \n", - " \n", - " batch_normalization_5 (Bat (None, 12, 12, 128) 512 \n", - " chNormalization) \n", - " \n", - " max_pooling2d_3 (MaxPoolin (None, 6, 6, 128) 0 \n", - " g2D) \n", - " \n", - " flatten_1 (Flatten) (None, 4608) 0 \n", - " \n", - " dense_3 (Dense) (None, 1024) 4719616 \n", - " \n", - " batch_normalization_6 (Bat (None, 1024) 4096 \n", - " chNormalization) \n", - " \n", - " dropout (Dropout) (None, 1024) 0 \n", - " \n", - " dense_4 (Dense) (None, 22) 22550 \n", - " \n", - "=================================================================\n", - "Total params: 5173782 (19.74 MB)\n", - "Trainable params: 5170646 (19.72 MB)\n", - "Non-trainable params: 3136 (12.25 KB)\n", - "_________________________________________________________________\n" - ] - } - ], - "source": [ - "from tensorflow.keras import layers, models\n", - "\n", - "# Define the CNN model with batch normalization\n", - "cnn_model = models.Sequential([\n", - " layers.Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)),\n", - " layers.BatchNormalization(),\n", - " layers.MaxPooling2D((2, 2)),\n", - " layers.Conv2D(64, (3, 3), activation='relu'),\n", - " layers.BatchNormalization(),\n", - " layers.Conv2D(64, (3, 3), activation='relu'),\n", - " layers.BatchNormalization(),\n", - " layers.MaxPooling2D((2, 2)),\n", - " layers.Conv2D(128, (3, 3), activation='relu'),\n", - " layers.BatchNormalization(),\n", - " layers.MaxPooling2D((2, 2)),\n", - " layers.Conv2D(128, (3, 3), activation='relu'),\n", - " layers.BatchNormalization(),\n", - " layers.Conv2D(128, (3, 3), activation='relu'),\n", - " layers.BatchNormalization(),\n", - " layers.MaxPooling2D((2, 2)),\n", - " layers.Flatten(),\n", - " layers.Dense(1024, activation='relu'),\n", - " layers.BatchNormalization(),\n", - " layers.Dropout(0.75),\n", - " layers.Dense(22, activation='softmax')\n", - "])\n", - "\n", - "\n", - "# Compile the model\n", - "cnn_model.compile(optimizer='adam',\n", - " loss='categorical_crossentropy',\n", - " metrics=['accuracy'])\n", - "\n", - "# Display the model summary\n", - "cnn_model.summary()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "QjOA_sUCUI5W", - "outputId": "0b72dc7c-2df8-485f-8746-c999f31cb301" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/10\n", - "551/551 [==============================] - 46s 64ms/step - loss: 3.2072 - accuracy: 0.2810 - val_loss: 2.7506 - val_accuracy: 0.2116\n", - "Epoch 2/10\n", - "551/551 [==============================] - 35s 63ms/step - loss: 2.0666 - accuracy: 0.4187 - val_loss: 1.9861 - val_accuracy: 0.4283\n", - "Epoch 3/10\n", - "551/551 [==============================] - 36s 65ms/step - loss: 1.5801 - accuracy: 0.5168 - val_loss: 1.7026 - val_accuracy: 0.4803\n", - "Epoch 4/10\n", - "551/551 [==============================] - 37s 67ms/step - loss: 1.3267 - accuracy: 0.5757 - val_loss: 1.1292 - val_accuracy: 0.6340\n", - "Epoch 5/10\n", - "551/551 [==============================] - 38s 69ms/step - loss: 1.1433 - accuracy: 0.6254 - val_loss: 1.0762 - val_accuracy: 0.6466\n", - "Epoch 6/10\n", - "551/551 [==============================] - 35s 64ms/step - loss: 1.0291 - accuracy: 0.6573 - val_loss: 1.1473 - val_accuracy: 0.6399\n", - "Epoch 7/10\n", - "551/551 [==============================] - 35s 62ms/step - loss: 0.9183 - accuracy: 0.6904 - val_loss: 0.9613 - val_accuracy: 0.6848\n", - "Epoch 8/10\n", - "551/551 [==============================] - 36s 65ms/step - loss: 0.8195 - accuracy: 0.7247 - val_loss: 1.1618 - val_accuracy: 0.6584\n", - "Epoch 9/10\n", - "551/551 [==============================] - 36s 65ms/step - loss: 0.7272 - accuracy: 0.7481 - val_loss: 0.9426 - val_accuracy: 0.7104\n", - "Epoch 10/10\n", - "551/551 [==============================] - 36s 65ms/step - loss: 0.6606 - accuracy: 0.7719 - val_loss: 1.0112 - val_accuracy: 0.6899\n" - ] - } - ], - "source": [ - "cnn_history = cnn_model.fit(\n", - " train_ds,\n", - " validation_data=val_ds,\n", - "\n", - " epochs=10 \n", - ")\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 507 - }, - "id": "09A0ihQVUI5X", - "outputId": "29a4f519-ff28-41f9-b2e6-6de784815841" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_graph(cnn_history)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "edlmW0YVUI5b" - }, - "source": [ - "# Resnet(Basic)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "id": "Ggk2xWRSsy-3" - }, - "outputs": [], - "source": [ - "from tensorflow.keras.applications import ResNet50\n", - "from tensorflow.keras.models import Model\n", - "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D , Dropout\n", - "from tensorflow.keras.optimizers import Adam\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "bYBGWOAPsL1k", - "outputId": "5ff7de24-5683-44dc-a959-e4b69a9b11eb" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n", - "94765736/94765736 [==============================] - 3s 0us/step\n" - ] - } - ], - "source": [ - "base_model = ResNet50(weights='imagenet', include_top=False)\n", - "\n", - "for layer in base_model.layers[-10:]:\n", - " layer.trainable = True\n", - "\n", - "# Add new top layers\n", - "x = base_model.output\n", - "x = GlobalAveragePooling2D()(x)\n", - "x = Dense(1024, activation='relu')(x)\n", - "predictions = Dense(22, activation='softmax')(x)\n", - "\n", - "res_model = Model(inputs=base_model.input, outputs=predictions)\n", - "\n", - "\n", - "# Compile the model\n", - "res_model.compile(optimizer=Adam(learning_rate=1e-5),\n", - " loss='categorical_crossentropy',\n", - " metrics=['accuracy'])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "6ANqeRP8tWV4", - "outputId": "5091ac68-6b19-4668-8584-3b046dd64b7e" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/10\n", - "551/551 [==============================] - 150s 201ms/step - loss: 1.3461 - accuracy: 0.6010 - val_loss: 3.0756 - val_accuracy: 0.0867\n", - "Epoch 2/10\n", - "551/551 [==============================] - 104s 188ms/step - loss: 0.5116 - accuracy: 0.8258 - val_loss: 0.5763 - val_accuracy: 0.8042\n", - "Epoch 3/10\n", - "551/551 [==============================] - 110s 200ms/step - loss: 0.3162 - accuracy: 0.8965 - val_loss: 0.4449 - val_accuracy: 0.8479\n", - "Epoch 4/10\n", - "551/551 [==============================] - 104s 189ms/step - loss: 0.2023 - accuracy: 0.9386 - val_loss: 0.4379 - val_accuracy: 0.8522\n", - "Epoch 5/10\n", - "551/551 [==============================] - 104s 187ms/step - loss: 0.1295 - accuracy: 0.9632 - val_loss: 0.4506 - val_accuracy: 0.8542\n", - "Epoch 6/10\n", - "551/551 [==============================] - 104s 187ms/step - loss: 0.0870 - accuracy: 0.9776 - val_loss: 0.4571 - val_accuracy: 0.8601\n", - "Epoch 7/10\n", - "551/551 [==============================] - 104s 188ms/step - loss: 0.0579 - accuracy: 0.9848 - val_loss: 0.4730 - val_accuracy: 0.8562\n", - "Epoch 8/10\n", - "551/551 [==============================] - 104s 189ms/step - loss: 0.0477 - accuracy: 0.9855 - val_loss: 0.5126 - val_accuracy: 0.8617\n", - "Epoch 9/10\n", - "551/551 [==============================] - 104s 188ms/step - loss: 0.0387 - accuracy: 0.9878 - val_loss: 0.5597 - val_accuracy: 0.8491\n", - "Epoch 10/10\n", - "551/551 [==============================] - 104s 188ms/step - loss: 0.0378 - accuracy: 0.9864 - val_loss: 0.5381 - val_accuracy: 0.8495\n" - ] - } - ], - "source": [ - "resnet_history= res_model.fit(train_ds,validation_data=val_ds,epochs=10)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 468 - }, - "id": "7QLWlY1dvfUU", - "outputId": "5d0f74ca-f30f-47c7-db63-ec68d0c1de13" - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_graph(resnet_history)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WGcdJoLHvjxd" - }, - "source": [ - "# ResNet(Perfect) with Data Augumentaion and Regularizations" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "6kfaFjzVemsj", - "outputId": "6cff68f7-0fe9-460a-955f-97608240456a" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 17605 images belonging to 22 classes.\n", - "Found 2538 images belonging to 22 classes.\n" - ] - } - ], - "source": [ - "# Data augmentaion\n", - "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", - "\n", - "datagen = ImageDataGenerator(\n", - " rotation_range=20,\n", - " width_shift_range=0.2,\n", - " height_shift_range=0.2,\n", - " shear_range=0.2,\n", - " zoom_range=0.2,\n", - " horizontal_flip=True,\n", - " fill_mode='nearest'\n", - ")\n", - "\n", - "train_generator = datagen.flow_from_directory(\n", - " 'filtered_train',\n", - " target_size=(150,150),\n", - " batch_size=32,\n", - " class_mode='categorical'\n", - ")\n", - "\n", - "val_generator = datagen.flow_from_directory(\n", - " 'filtered_val',\n", - " target_size=(150, 150),\n", - " batch_size=32,\n", - " class_mode='categorical'\n", - ")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "id": "AsmRa-MUfsCs" - }, - "outputs": [], - "source": [ - "from tensorflow.keras.applications import ResNet50\n", - "from tensorflow.keras.layers import GlobalAveragePooling2D, Dense, Dropout\n", - "from tensorflow.keras.models import Model\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.regularizers import l2\n", - "from tensorflow.keras.callbacks import EarlyStopping\n" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "h7_q9fbffbgd", - "outputId": "065ed344-07fa-4bfe-de27-62018da4e819" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/5\n", - "551/551 [==============================] - 218s 342ms/step - loss: 16.7730 - accuracy: 0.2183 - val_loss: 15.0907 - val_accuracy: 0.5839\n", - "Epoch 2/5\n", - "551/551 [==============================] - 187s 339ms/step - loss: 15.0545 - accuracy: 0.5087 - val_loss: 14.0818 - val_accuracy: 0.7206\n", - "Epoch 3/5\n", - "551/551 [==============================] - 187s 339ms/step - loss: 14.0944 - accuracy: 0.6330 - val_loss: 13.3983 - val_accuracy: 0.7545\n", - "Epoch 4/5\n", - "551/551 [==============================] - 189s 343ms/step - loss: 13.3619 - accuracy: 0.6914 - val_loss: 12.7492 - val_accuracy: 0.7888\n", - "Epoch 5/5\n", - "551/551 [==============================] - 192s 349ms/step - loss: 12.6688 - accuracy: 0.7296 - val_loss: 12.1332 - val_accuracy: 0.8081\n" - ] - } - ], - "source": [ - "# Load the base model\n", - "base_model = ResNet50(weights='imagenet', include_top=False)\n", - "\n", - "# Make the last 10 layers trainable\n", - "for layer in base_model.layers[-10:]:\n", - " layer.trainable = True\n", - "\n", - "# Add new top layers\n", - "x = base_model.output\n", - "x = GlobalAveragePooling2D()(x)\n", - "x = Dropout(0.5)(x) # Adding dropout for regularization\n", - "x = Dense(1024, activation='relu', kernel_regularizer=l2(0.01))(x)\n", - "x = Dropout(0.5)(x) # Adding another dropout layer for regularization\n", - "predictions = Dense(22, activation='softmax', kernel_regularizer=l2(0.01))(x)\n", - "\n", - "res_model_perfect = Model(inputs=base_model.input, outputs=predictions)\n", - "\n", - "# Re-compile the model\n", - "res_model_perfect.compile(optimizer=Adam(learning_rate=1e-5),\n", - " loss='categorical_crossentropy',\n", - " metrics=['accuracy'])\n", - "\n", - "# Early stopping callback\n", - "early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n", - "\n", - "# Train the model\n", - "res_model_perfect_history = res_model_perfect.fit(train_generator, epochs=5, validation_data=val_generator, callbacks=[early_stopping])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "rottw-JQjOq4", - "outputId": "275497e0-d72c-400c-d5e7-b777665d7d68" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 6/10\n", - "551/551 [==============================] - 189s 344ms/step - loss: 11.9784 - accuracy: 0.7541 - val_loss: 11.4686 - val_accuracy: 0.8282\n", - "Epoch 7/10\n", - "551/551 [==============================] - 189s 342ms/step - loss: 11.3271 - accuracy: 0.7718 - val_loss: 10.8296 - val_accuracy: 0.8294\n", - "Epoch 8/10\n", - "551/551 [==============================] - 191s 347ms/step - loss: 10.6584 - accuracy: 0.7876 - val_loss: 10.2008 - val_accuracy: 0.8373\n", - "Epoch 9/10\n", - "551/551 [==============================] - 192s 347ms/step - loss: 10.0059 - accuracy: 0.8040 - val_loss: 9.6002 - val_accuracy: 0.8408\n", - "Epoch 10/10\n", - "551/551 [==============================] - 194s 352ms/step - loss: 9.3686 - accuracy: 0.8173 - val_loss: 8.9783 - val_accuracy: 0.8455\n" - ] - } - ], - "source": [ - "res_model_perfect_history1 = res_model_perfect.fit(train_generator, initial_epoch=5,epochs=10, validation_data=val_generator, callbacks=[early_stopping])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "lhkIRX0oEpTj" - }, - "source": [ - "# VGG16 Model" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "id": "v93hTELS5Jzl" - }, - "outputs": [], - "source": [ - "from tensorflow.keras.applications import VGG16\n", - "from tensorflow.keras.layers import GlobalAveragePooling2D, Dense, Dropout\n", - "from tensorflow.keras.models import Model\n", - "from tensorflow.keras.optimizers import SGD\n", - "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "TbOkFGjzEw5p", - "outputId": "d30519f5-efa9-465c-b5bb-f43e9f81e484" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n", - "58889256/58889256 [==============================] - 2s 0us/step\n", - "Model: \"model_2\"\n", - "_________________________________________________________________\n", - " Layer (type) Output Shape Param # \n", - "=================================================================\n", - " input_3 (InputLayer) [(None, None, None, 3)] 0 \n", - " \n", - " block1_conv1 (Conv2D) (None, None, None, 64) 1792 \n", - " \n", - " block1_conv2 (Conv2D) (None, None, None, 64) 36928 \n", - " \n", - " block1_pool (MaxPooling2D) (None, None, None, 64) 0 \n", - " \n", - " block2_conv1 (Conv2D) (None, None, None, 128) 73856 \n", - " \n", - " block2_conv2 (Conv2D) (None, None, None, 128) 147584 \n", - " \n", - " block2_pool (MaxPooling2D) (None, None, None, 128) 0 \n", - " \n", - " block3_conv1 (Conv2D) (None, None, None, 256) 295168 \n", - " \n", - " block3_conv2 (Conv2D) (None, None, None, 256) 590080 \n", - " \n", - " block3_conv3 (Conv2D) (None, None, None, 256) 590080 \n", - " \n", - " block3_pool (MaxPooling2D) (None, None, None, 256) 0 \n", - " \n", - " block4_conv1 (Conv2D) (None, None, None, 512) 1180160 \n", - " \n", - " block4_conv2 (Conv2D) (None, None, None, 512) 2359808 \n", - " \n", - " block4_conv3 (Conv2D) (None, None, None, 512) 2359808 \n", - " \n", - " block4_pool (MaxPooling2D) (None, None, None, 512) 0 \n", - " \n", - " block5_conv1 (Conv2D) (None, None, None, 512) 2359808 \n", - " \n", - " block5_conv2 (Conv2D) (None, None, None, 512) 2359808 \n", - " \n", - " block5_conv3 (Conv2D) (None, None, None, 512) 2359808 \n", - " \n", - " block5_pool (MaxPooling2D) (None, None, None, 512) 0 \n", - " \n", - " global_average_pooling2d_2 (None, 512) 0 \n", - " (GlobalAveragePooling2D) \n", - " \n", - " dense_4 (Dense) (None, 1024) 525312 \n", - " \n", - " dense_5 (Dense) (None, 22) 22550 \n", - " \n", - "=================================================================\n", - "Total params: 15262550 (58.22 MB)\n", - "Trainable params: 15262550 (58.22 MB)\n", - "Non-trainable params: 0 (0.00 Byte)\n", - "_________________________________________________________________\n" - ] - } - ], - "source": [ - "\n", - "# Load the base model\n", - "base_model = VGG16(weights='imagenet', include_top=False)\n", - "\n", - "# Make more layers trainable\n", - "for layer in base_model.layers[-10:]:\n", - " layer.trainable = True\n", - "\n", - "# Add new top layers\n", - "x = base_model.output\n", - "x = GlobalAveragePooling2D()(x)\n", - "x = Dense(1024, activation='relu')(x)\n", - "predictions = Dense(22, activation='softmax')(x)\n", - "\n", - "# Create the final model\n", - "vgg_model = Model(inputs=base_model.input, outputs=predictions)\n", - "\n", - "vgg_model.compile(optimizer=Adam(learning_rate=1e-5),\n", - " loss='categorical_crossentropy',\n", - " metrics=['accuracy'])\n", - "\n", - "vgg_model.summary()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Nc8DXxH8MvnG", - "outputId": "babb6905-0dab-4fc2-d615-6e08898524ca" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/10\n", - "551/551 [==============================] - 148s 239ms/step - loss: 1.2364 - accuracy: 0.5985 - val_loss: 0.7437 - val_accuracy: 0.7360\n", - "Epoch 2/10\n", - "551/551 [==============================] - 123s 223ms/step - loss: 0.5993 - accuracy: 0.7848 - val_loss: 0.6208 - val_accuracy: 0.7683\n", - "Epoch 3/10\n", - "551/551 [==============================] - 128s 232ms/step - loss: 0.4672 - accuracy: 0.8295 - val_loss: 0.5241 - val_accuracy: 0.8081\n", - "Epoch 4/10\n", - "551/551 [==============================] - 128s 232ms/step - loss: 0.3806 - accuracy: 0.8620 - val_loss: 0.4916 - val_accuracy: 0.8140\n", - "Epoch 5/10\n", - "551/551 [==============================] - 124s 224ms/step - loss: 0.3119 - accuracy: 0.8891 - val_loss: 0.5329 - val_accuracy: 0.8069\n", - "Epoch 6/10\n", - "551/551 [==============================] - 124s 224ms/step - loss: 0.2599 - accuracy: 0.9063 - val_loss: 0.5281 - val_accuracy: 0.8128\n", - "Epoch 7/10\n", - "551/551 [==============================] - 123s 223ms/step - loss: 0.2171 - accuracy: 0.9225 - val_loss: 0.5258 - val_accuracy: 0.8262\n", - "Epoch 8/10\n", - "551/551 [==============================] - 128s 232ms/step - loss: 0.1756 - accuracy: 0.9380 - val_loss: 0.5114 - val_accuracy: 0.8353\n", - "Epoch 9/10\n", - "551/551 [==============================] - 123s 223ms/step - loss: 0.1501 - accuracy: 0.9474 - val_loss: 0.5555 - val_accuracy: 0.8286\n", - "Epoch 10/10\n", - "551/551 [==============================] - 128s 232ms/step - loss: 0.1284 - accuracy: 0.9558 - val_loss: 0.5891 - val_accuracy: 0.8270\n" - ] - } - ], - "source": [ - "vgg_history= vgg_model.fit(train_ds,epochs=10,validation_data=val_ds)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 468 - }, - "id": "5E-06ul8He9M", - "outputId": "210c701f-518b-4253-86ec-f5754d5798eb" - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_graph(vgg_history)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "RcmcV-PLnCw_" - }, - "source": [ - "# Model Evaluation and Best model Testing" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "P6m-twEGm_AH", - "outputId": "9ecddb0d-3dc8-4cc5-ac29-4f7e77b5aeff" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "157/157 [==============================] - 7s 38ms/step - loss: 1.8495 - accuracy: 0.3898\n", - "ANN Test accuracy: 38.98%\n" - ] - } - ], - "source": [ - "loss_ann, accuracy_ann = ann_model.evaluate(test_ds)\n", - "print(f\"ANN Test accuracy: {accuracy_ann * 100:.2f}%\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "sAs7JCS1MQgL", - "outputId": "19d046a2-6e95-4542-8f60-3aa69acd6ef7" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "157/157 [==============================] - 9s 53ms/step - loss: 0.9678 - accuracy: 0.6992\n", - "CNN Test accuracy: 69.92%\n" - ] - } - ], - "source": [ - "loss_cnn, accuracy_cnn = cnn_model.evaluate(test_ds)\n", - "print(f\"CNN Test accuracy: {accuracy_cnn * 100:.2f}%\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "K2zPnXNaMQP6", - "outputId": "ba65fa34-0993-443c-e39f-71bd03be6a0e" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "158/158 [==============================] - 9s 57ms/step - loss: 0.5548 - accuracy: 0.8562\n", - "ResNet Test accuracy: 85.62%\n" - ] - } - ], - "source": [ - "loss_res, accuracy_res = res_model.evaluate(test_ds)\n", - "print(f\"ResNet Test accuracy: {accuracy_res * 100:.2f}%\")\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "vkawaMLVMP6d", - "outputId": "46a0d654-b34a-47b9-bf61-819e60e6f08e" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "158/158 [==============================] - 11s 70ms/step - loss: 0.5903 - accuracy: 0.8303\n", - "VGG16 Test accuracy: 83.03%\n" - ] - } - ], - "source": [ - "\n", - "loss_vgg16, accuracy_vgg16 = vgg_model.evaluate(test_ds)\n", - "print(f\"VGG16 Test accuracy: {accuracy_vgg16 * 100:.2f}%\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "PLRg-hzH3xCv", - "outputId": "bd18867b-1445-40f0-d001-b63d62339591" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 5027 images belonging to 22 classes.\n" - ] - } - ], - "source": [ - "# Preparing test data for Renset Perfect model\n", - "test_generator = datagen.flow_from_directory(\n", - " 'filtered_test',\n", - " target_size=(150, 150),\n", - " batch_size=32,\n", - " class_mode='categorical'\n", - ")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "9z53Loe7uKVH", - "outputId": "63799035-a5cb-4279-89f5-69188aefb10e" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "158/158 [==============================] - 47s 299ms/step - loss: 8.9857 - accuracy: 0.8472\n", - "ResNet(Perfect) Test accuracy: 84.72%\n" - ] - } - ], - "source": [ - "loss_res_model_perfect, accuracy_res_model_perfect = res_model_perfect.evaluate(test_generator)\n", - "print(f\"ResNet(Perfect) Test accuracy: {accuracy_res_model_perfect * 100:.2f}%\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "swE1qjVO30EQ" - }, - "source": [ - "# Tabulating the Model and it's Accuracy" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "wG_5qUPadj7N" - }, - "source": [ - " | Model Name | Accuracy (%) |\n", - " |-----------------|--------------|\n", - " | ANN | 38.98 |\n", - " | CNN | 69.92 |\n", - " | VGG16 | 83.03 |\n", - " | ResNet | 85.62 |\n", - " | ResNet(Perfect) | 84.58 |" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "2F0_YBHIc1zt" - }, - "source": [ - "- The ResNet(Perfect) model is chosen as the optimal model due to its balance of high accuracy and reduced overfitting, making it the best candidate for deployment. While the standard ResNet model achieved the highest accuracy, its overfitting issues could compromise its performance on new, unseen data. Therefore, for practical applications where generalization is crucial, the ResNet(Perfect) model is the preferred choice\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "_un1ZG_XDvzr" - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "gpuType": "T4", - "provenance": [] - }, - "kaggle": { - "accelerator": "none", - "dataSources": [ - { - "datasetId": 4280832, - "sourceId": 7368427, - "sourceType": "datasetVersion" - } - ], - "dockerImageVersionId": 30699, - "isGpuEnabled": false, - "isInternetEnabled": true, - "language": "python", - "sourceType": "notebook" - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } ->>>>>>> refs/remotes/origin/main:Crop Pest and Disease Detection using DL/Model/Crop_Pest_and_Disease_Detection_using_DL.ipynb }, { "cell_type": "markdown", From 810f6a7b0bc13d959598149def944d2529309087 Mon Sep 17 00:00:00 2001 From: Barrenkala Veera Venkata Karthik <140957865+Karthik110505@users.noreply.github.com> Date: Thu, 23 May 2024 09:12:26 +0530 Subject: [PATCH 6/6] Update README.md