Merge pull request #760 from Arihant-Bhandari/cat-classify

[Project Addition] Cats Breed Classification using DL
abhisheks008 · Jun 10, 2024 · 82cc2ca · 82cc2ca
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diff --git a/Cats Breed Classification using DL/Dataset/README.md b/Cats Breed Classification using DL/Dataset/README.md
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+The link for the dataset used in this project:  https://www.kaggle.com/datasets/yapwh1208/cats-breed-dataset
+
+The dataset consists of 5 subdirectories: siamese, ragdoll, bengal, domestic_shorthair and maine_coon under cat_v1 directory, totalling to 951 images.
diff --git a/Cats Breed Classification using DL/Images/CNN Accuracy.png b/Cats Breed Classification using DL/Images/CNN Accuracy.png
diff --git a/Cats Breed Classification using DL/Images/CNN Loss.png b/Cats Breed Classification using DL/Images/CNN Loss.png
diff --git a/Cats Breed Classification using DL/Images/DenseNet121 Accuracy.png b/Cats Breed Classification using DL/Images/DenseNet121 Accuracy.png
diff --git a/Cats Breed Classification using DL/Images/DenseNet121 Loss.png b/Cats Breed Classification using DL/Images/DenseNet121 Loss.png
diff --git a/Cats Breed Classification using DL/Images/EDA.png b/Cats Breed Classification using DL/Images/EDA.png
diff --git a/Cats Breed Classification using DL/Images/InceptionV3 Accuracy.png b/Cats Breed Classification using DL/Images/InceptionV3 Accuracy.png
diff --git a/Cats Breed Classification using DL/Images/InceptionV3 Loss.png b/Cats Breed Classification using DL/Images/InceptionV3 Loss.png
diff --git a/Cats Breed Classification using DL/Images/MobileNet Accuracy.png b/Cats Breed Classification using DL/Images/MobileNet Accuracy.png
diff --git a/Cats Breed Classification using DL/Images/MobileNet Loss.png b/Cats Breed Classification using DL/Images/MobileNet Loss.png
diff --git a/Cats Breed Classification using DL/Images/NASNetMobile Accuracy.png b/Cats Breed Classification using DL/Images/NASNetMobile Accuracy.png
diff --git a/Cats Breed Classification using DL/Images/NASNetMobile Loss.png b/Cats Breed Classification using DL/Images/NASNetMobile Loss.png
diff --git a/Cats Breed Classification using DL/Images/RESNET50 Accuracy.png b/Cats Breed Classification using DL/Images/RESNET50 Accuracy.png
diff --git a/Cats Breed Classification using DL/Images/RESNET50 Loss.png b/Cats Breed Classification using DL/Images/RESNET50 Loss.png
diff --git a/Cats Breed Classification using DL/Images/VGG16 Accuracy.png b/Cats Breed Classification using DL/Images/VGG16 Accuracy.png
diff --git a/Cats Breed Classification using DL/Images/VGG16 Loss.png b/Cats Breed Classification using DL/Images/VGG16 Loss.png
diff --git a/Cats Breed Classification using DL/Images/Xception Accuracy.png b/Cats Breed Classification using DL/Images/Xception Accuracy.png
diff --git a/Cats Breed Classification using DL/Images/Xception Loss.png b/Cats Breed Classification using DL/Images/Xception Loss.png
diff --git a/Cats Breed Classification using DL/Images/bengal.png b/Cats Breed Classification using DL/Images/bengal.png
diff --git a/Cats Breed Classification using DL/Images/domestic shorthair.png b/Cats Breed Classification using DL/Images/domestic shorthair.png
diff --git a/Cats Breed Classification using DL/Images/maine coon.png b/Cats Breed Classification using DL/Images/maine coon.png
diff --git a/Cats Breed Classification using DL/Images/ragdoll.png b/Cats Breed Classification using DL/Images/ragdoll.png
diff --git a/Cats Breed Classification using DL/Images/siamese.png b/Cats Breed Classification using DL/Images/siamese.png
diff --git a/Cats Breed Classification using DL/Model/cat-classify.ipynb b/Cats Breed Classification using DL/Model/cat-classify.ipynb
diff --git a/Cats Breed Classification using DL/README.md b/Cats Breed Classification using DL/README.md
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+## **Cats Breed Classification using DL**
+
+### 🎯 **Goal**
+
+The objective of this project is to classify images of cats into 5 breeds, namely: siamese, ragdoll, bengal, domestic shorthair and maine coon.
+
+### 🧵 **Dataset**
+
+The dataset consists of 5 subdirectories: siamese, ragdoll, bengal, domestic_shorthair and maine_coon under cat_v1 directory, totalling to 951 images.
+
+### 🧾 **Description**
+
+The project deals with multi-class classification, classifying images into 5 classes.
+
+### 🧮 **What I had done!**
+
+To achieve our goals, the following steps were implemented:
+
+- Images were loaded using keras.utils and normalized to the range 0 to 1.
+
+- Implemented class balancing by loading 170 images per class.
+
+- Labels were loaded and one-hot encoded into probability distributions.
+
+- Images were resized to a fixed size of 224x224 pixels.
+
+- Custom and pre-trained models were used for this task.
+
+### 🚀 **Models Implemented**
+
+models used:
+
+- ResNet-50
+- Xception
+- VGG16
+- CNN
+- InceptionV3
+- DenseNet-121
+- MobileNet
+- NASNetMobile
+
+### 📚 **Libraries Needed**
+
+- Keras
+
+- Tensorflow
+
+- Numpy
+
+- Matplotlib
+
+### 📊 **Exploratory Data Analysis Results**
+
+
+- #### **Exploratory Data Analysis**
+
+<p align="center">
+  <img src="https://github.com/Arihant-Bhandari/DL-Simplified/blob/cat-classify/Cats%20Breed%20Classification%20using%20DL/Images/bengal.png" height="400px" width="400px" />
+  <img src="https://github.com/Arihant-Bhandari/DL-Simplified/blob/cat-classify/Cats%20Breed%20Classification%20using%20DL/Images/domestic%20shorthair.png" height="400px" width="400px" />
+  <img src="https://github.com/Arihant-Bhandari/DL-Simplified/blob/cat-classify/Cats%20Breed%20Classification%20using%20DL/Images/maine%20coon.png" height="400px" width="400px" />
+  <img src="https://github.com/Arihant-Bhandari/DL-Simplified/blob/cat-classify/Cats%20Breed%20Classification%20using%20DL/Images/ragdoll.png" height="400px" width="400px" />
+  <img src="https://github.com/Arihant-Bhandari/DL-Simplified/blob/cat-classify/Cats%20Breed%20Classification%20using%20DL/Images/siamese.png" height="400px" width="400px" />
+</p>
+
+<img src="https://github.com/Arihant-Bhandari/DL-Simplified/blob/cat-classify/Cats%20Breed%20Classification%20using%20DL/Images/EDA.png">
+
+- #### **CNN**
+
+<p align="center">
+  <img src="https://github.com/Arihant-Bhandari/DL-Simplified/blob/cat-classify/Cats%20Breed%20Classification%20using%20DL/Images/CNN%20Accuracy.png" height="400px" width="400px" />
+  <img src="https://github.com/Arihant-Bhandari/DL-Simplified/blob/cat-classify/Cats%20Breed%20Classification%20using%20DL/Images/CNN%20Loss.png" height="400px" width="400px" />
+</p>
+
+- #### **InceptionV3**
+
+<p align="center">
+  <img src="https://github.com/Arihant-Bhandari/DL-Simplified/blob/cat-classify/Cats%20Breed%20Classification%20using%20DL/Images/InceptionV3%20Accuracy.png" height="400px" width="400px" />
+  <img src="https://github.com/Arihant-Bhandari/DL-Simplified/blob/cat-classify/Cats%20Breed%20Classification%20using%20DL/Images/InceptionV3%20Loss.png" height="400px" width="400px" />
+</p>
+
+- #### **VGG16**
+
+<p align="center">
+  <img src="https://github.com/Arihant-Bhandari/DL-Simplified/blob/cat-classify/Cats%20Breed%20Classification%20using%20DL/Images/VGG16%20Accuracy.png" height="400px" width="400px" />
+  <img src="https://github.com/Arihant-Bhandari/DL-Simplified/blob/cat-classify/Cats%20Breed%20Classification%20using%20DL/Images/VGG16%20Loss.png" height="400px" width="400px" />
+</p>
+
+- #### **MobileNet**
+
+<p align="center">
+  <img src="https://github.com/Arihant-Bhandari/DL-Simplified/blob/cat-classify/Cats%20Breed%20Classification%20using%20DL/Images/MobileNet%20Accuracy.png" height="400px" width="400px" />
+  <img src="https://github.com/Arihant-Bhandari/DL-Simplified/blob/cat-classify/Cats%20Breed%20Classification%20using%20DL/Images/MobileNet%20Loss.png" height="400px" width="400px" />
+</p>
+
+- #### **RESNET50**
+
+<p align="center">
+  <img src="https://github.com/Arihant-Bhandari/DL-Simplified/blob/cat-classify/Cats%20Breed%20Classification%20using%20DL/Images/RESNET50%20Accuracy.png" height="400px" width="400px" />
+  <img src="https://github.com/Arihant-Bhandari/DL-Simplified/blob/cat-classify/Cats%20Breed%20Classification%20using%20DL/Images/RESNET50%20Loss.png" height="400px" width="400px" />
+</p>
+
+- #### **DenseNet-121**
+
+<p align="center">
+  <img src="https://github.com/Arihant-Bhandari/DL-Simplified/blob/cat-classify/Cats%20Breed%20Classification%20using%20DL/Images/DenseNet121%20Accuracy.png" height="400px" width="400px" />
+  <img src="https://github.com/Arihant-Bhandari/DL-Simplified/blob/cat-classify/Cats%20Breed%20Classification%20using%20DL/Images/DenseNet121%20Loss.png" height="400px" width="400px" />
+</p>
+
+- #### **Xception**
+
+<p align="center">
+  <img src="https://github.com/Arihant-Bhandari/DL-Simplified/blob/cat-classify/Cats%20Breed%20Classification%20using%20DL/Images/Xception%20Accuracy.png" height="400px" width="400px" />
+  <img src="https://github.com/Arihant-Bhandari/DL-Simplified/blob/cat-classify/Cats%20Breed%20Classification%20using%20DL/Images/Xception%20Loss.png" height="400px" width="400px" />
+</p>
+
+- #### **NASNetMobile**
+
+<p align="center">
+  <img src="https://github.com/Arihant-Bhandari/DL-Simplified/blob/cat-classify/Cats%20Breed%20Classification%20using%20DL/Images/NASNetMobile%20Accuracy.png" height="400px" width="400px" />
+  <img src="https://github.com/Arihant-Bhandari/DL-Simplified/blob/cat-classify/Cats%20Breed%20Classification%20using%20DL/Images/NASNetMobile%20Loss.png" height="400px" width="400px" />
+</p>
+
+### 📈 **Performance of the Models based on the Accuracy Scores**
+
+#### Metrics: 
+
+We used Validation **Loss** and **Accuracy** as metrics.
+
+| Models | Accuracy | Loss |
+|--------|---------------------|--------------------------|
+| ResNet-50 | 22.66% | 12.4663 |
+| InceptionV3 | 83.59%  | 0.4896 | 
+| CNN | 35.94% | 2.2302 |
+| VGG16 | 78.12% | 0.6243 |
+| MobileNet | 90.62% | 0.3220 |
+| DenseNet-121 | 85.16% | 0.5082 |
+| Xception | 82.81% | 0.4810 |
+| NASNetMobile | 87.50% | 0.4133 |
+
+### 📢 **Conclusion**
+
+We conclude the following:
+
+**NASNetMobile**, **Xception**, **MobileNet** and **DenseNet-121** work exceptionally on given task.
+
+### ✒️ **Your Signature**
+
+Original Contributor: Arihant Bhandari [https://github.com/Arihant-Bhandari]
diff --git a/Cats Breed Classification using DL/requirements.txt b/Cats Breed Classification using DL/requirements.txt
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+pandas
+keras
+tensorflow
+numpy
+matplotlib
+torch
+seaborn
+Dense
+Dropout
+BatchNormalization
+Adam
+EarlyStopping
+ModelCheckpoint
+Conv2D
+MaxPooling2D
+Sequential
+Nadam
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		The link for the dataset used in this project: https://www.kaggle.com/datasets/yapwh1208/cats-breed-dataset

		The dataset consists of 5 subdirectories: siamese, ragdoll, bengal, domestic_shorthair and maine_coon under cat_v1 directory, totalling to 951 images.