Updated Readme file

American Sign Language Detection/README.md

@@ -1,3 +1,4 @@
+# New enanchements has been made in the model , please check it below
 # American Sign Language Detection/https://github.com/World-of-ML/DL-Simplified/issues/312
 to predict correct sign language labels corresponding to their corresponding sign images
 
@@ -34,6 +35,13 @@ To implement InceptionV3, we start by loading the pre-trained model, which comes
 
 I will utilize the **VGG16** (Visual Geometry Group) architecture, which have deeper and complex structures. These models are renowned for their exceptional performance on various image recognition tasks. By leveraging the pre-trained weights of VGG, I can benefit from the learned features and fine-tune the network for image segmentation on the Lemon Quality Dataset.
 
+`new models implemented with new approach for enanchement`
+- ResNet101V2
+- ResNet50V2
+- MobileNetV3Large
+- MobileNetV3Small
+- InceptionV3
+- NASNetMobile
 
 **Accuracy Comparison**
 
@@ -46,6 +54,18 @@ I will utilize the **VGG16** (Visual Geometry Group) architecture, which have de
 
 Since the models' decent levels of accuracy(88% and above) means that most of their pictures will be almost havinG similar predicted labels with a small room for mistake, the anticipated labels for the sign image labels are as are visualised as follows.
 
+`new models accuracy`
+| Rank | Model Name       | Test Accuracy | Trained Model Size | Training Accuracy | Training Loss |
+|------|------------------|---------------|--------------------|-------------------|---------------|
+| 1    | MobileNetV3Small | 100.0%        | 19.1MB             | 96.97%            | 0.1574        |
+| 2    | NASNetMobile     | 100.0%        | 67.1MB             | 97.96%            | 0.1058        |
+| 3    | MobileNetV3Large | 100.0%        | 48.6MB             | 97.98%            | 0.1026        |
+| 4    | InceptionV3      | 100.0%        | 287.8MB            | 98.65%            | 0.0712        |
+| 5    | ResNet50V2       | 100.0%        | 308.6MB            | 98.67%            | 0.0625        |
+| 6    | ResNet101V2      | 100.0%        | 537.5MB            | 98.74%            | 0.0605        |
+
+- ranking based on Trained Model size
+
 **Throughout the project,** 
 
 I will preprocess the dataset by resizing the images and splitting it into training,validation and testing sets. For training, I will employ a loss function suitable for image segmentation, such as cross-entropy loss, and optimize the models using technique like  Adam optimization
@@ -60,84 +80,17 @@ I will evaluate their performance using appropriate metrics. Additionally, I wil
 
 ## after evaluation, `MobileNet` or `VGG16` model looks to be the best fit model in this case of American Sign Language Classification .
 
-
-**Future Scope**
-
-This project will contribute to advancing the understanding and application of deep learning in the field of computer vision and could potentially find applications in sorting of sign languages in different classes.
-
-# Improvements in the project from @Adhivp
-## **American Sign Language**
-
-### 🎯 **Goal**
-
-- The goal of this project is to develop a deep learning model capable of accurately recognizing American Sign Language (ASL) signs from images.
-- This model aims to facilitate communication for individuals who use ASL by enabling real-time sign language interpretation.
-- Ultimately, the project seeks to bridge the communication gap between ASL users and non-ASL speakers.
-
-### 🧵 **Dataset**
-
-https://www.kaggle.com/datasets/kapillondhe/american-sign-language/data
-
-### 🧾 **Description**
-
-- This project involves training a deep learning model using a comprehensive dataset of ASL signs.
-- The dataset comprises images of 28 different ASL labels, with each label containing 6000 images.
-- To ensure efficient and effective training, the model is trained for only one epoch.
-    - This decision is based on the repetitive nature of the dataset and the inclusion of augmented images, which provide sufficient exposure to various visual patterns within a single epoch.
-    - By leveraging these characteristics, the model quickly learns to recognize ASL signs, making it a practical tool for real-time sign language interpretation.
-
-### 🧮 **What I had done!**
-
-- I have imported various pretrained models from TensorFlow and added a softmax classification layer with 28 classifications.
-
-### 🚀 **Models Implemented**
-
-- ResNet101V2
-- ResNet50V2
-- MobileNetV3Large
-- MobileNetV3Small
-- InceptionV3
-- NASNetMobile
-
-### 📚 **Libraries Needed**
-
-- pandas
-- Pillow
-- numpy
-- tensorflow
-- matplotlib
-
-### 📊 **Exploratory Data Analysis Results**
-
-#### Folder: train
-- Total images: 165670
-- Images per label: 5996 each
-
-#### Folder: test
-- Total images: 112
-- Images per label: 4 each
-
-### 📈 **Performance of the Models based on the Accuracy Scores**
-
-| Rank | Model Name       | Test Accuracy | Trained Model Size | Training Accuracy | Training Loss |
-|------|------------------|---------------|--------------------|-------------------|---------------|
-| 1    | MobileNetV3Small | 100.0%        | 19.1MB             | 96.97%            | 0.1574        |
-| 2    | NASNetMobile     | 100.0%        | 67.1MB             | 97.96%            | 0.1058        |
-| 3    | MobileNetV3Large | 100.0%        | 48.6MB             | 97.98%            | 0.1026        |
-| 4    | InceptionV3      | 100.0%        | 287.8MB            | 98.65%            | 0.0712        |
-| 5    | ResNet50V2       | 100.0%        | 308.6MB            | 98.67%            | 0.0625        |
-| 6    | ResNet101V2      | 100.0%        | 537.5MB            | 98.74%            | 0.0605        |
-
-- ranking based on Trained Model size
-
-### 📢 **Conclusion**
-
+## New models conclusian after enanchement
 - All models achieve a remarkable test accuracy of 100.0%, demonstrating their effectiveness in classification tasks.
   - MobileNetV3Small stands out with a compact size of 19.1MB, offering high accuracy while minimizing resource usage, making it suitable for memory-constrained environments.
   - NASNetMobile and MobileNetV3Large also deliver impressive accuracy with moderate model sizes, providing versatility in deployment scenarios.
   - InceptionV3, ResNet50V2, and ResNet101V2, although larger in size, exhibit robust performance, with ResNet101V2 achieving the highest training accuracy of 98.74%.
 
-### ✒️ **Adhithyan VP**
+**Future Scope**
+
+This project will contribute to advancing the understanding and application of deep learning in the field of computer vision and could potentially find applications in sorting of sign languages in different classes.
+
+### ✒️ Improvements in this project is made by **Adhithyan VP**
 
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