| --- |
| license: apache-2.0 |
| tags: |
| - image-classification |
| - cnn |
| - pytorch |
| - cat-vs-dog |
| - deep-learning |
| library_name: pytorch |
| datasets: |
| - custom |
| metrics: |
| - name: accuracy |
| type: accuracy |
| value: 90.56 |
| model_name: Cat vs Dog Classifier |
| pipeline_tag: image-classification |
| --- |
| --- |
| # 🐾 Cat vs Dog Classifier |
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| This model is a deep convolutional neural network (CNN) built using PyTorch to classify images as either cats or dogs. It was trained on a labeled dataset of cat and dog images resized to 224×224 pixels, with extensive data augmentation and regularization techniques to improve generalization. The model achieves over 90% accuracy on the test set. |
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| ## 🧠 Model Overview |
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| The classifier consists of four convolutional blocks followed by three fully connected layers. Each convolutional block includes a convolutional layer, batch normalization, ReLU activation, and max pooling. The fully connected layers include batch normalization and dropout for regularization. |
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| ### Convolutional Feature Extractor |
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| - **Conv Block 1**: |
| - Input channels: 3 (RGB) |
| - Output channels: 64 |
| - Kernel size: 3 × 3 |
| - BatchNorm + ReLU + MaxPool (2 × 2) |
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| - **Conv Block 2**: |
| - Input channels: 64 |
| - Output channels: 128 |
| - Kernel size: 3 × 3 |
| - BatchNorm + ReLU + MaxPool (2 × 2) |
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| - **Conv Block 3**: |
| - Input channels: 128 |
| - Output channels: 256 |
| - Kernel size: 3 × 3 |
| - BatchNorm + ReLU + MaxPool (2 × 2) |
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| - **Conv Block 4**: |
| - Input channels: 256 |
| - Output channels: 512 |
| - Kernel size: 3 × 3 |
| - BatchNorm + ReLU + MaxPool (2 × 2) |
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| After these blocks, the feature map is flattened to a 1D vector for classification. |
|
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| ### Fully Connected Classifier |
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| - **FC Layer 1**: |
| - Input: Flattened feature vector |
| - Output: 512 units |
| - BatchNorm + ReLU + Dropout (p=0.5) |
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| - **FC Layer 2**: |
| - Input: 512 units |
| - Output: 256 units |
| - BatchNorm + ReLU + Dropout (p=0.5) |
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| - **FC Layer 3**: |
| - Input: 256 units |
| - Output: 2 units (cat or dog) |
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| ## 🧪 Training Details |
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| - **Framework**: PyTorch |
| - **Input size**: 224 × 224 RGB images |
| - **Optimizer**: Adam with learning rate 0.0001 and weight decay 1e-4 |
| - **Loss Function**: CrossEntropyLoss |
| - **Scheduler**: ReduceLROnPlateau (monitors validation accuracy) |
| - **Epochs**: 40 |
| - **Batch Size**: 32 |
| - **Validation Split**: 20% of training data |
| - **Best Validation Accuracy**: 90.26% |
| - **Final Test Accuracy**: 90.56% |
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| ## 🎨 Data Augmentation |
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| To improve generalization, the training data was augmented with: |
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| - Random horizontal flips |
| - Random rotations (±10 degrees) |
| - Color jitter (brightness and contrast) |
| - Normalization using ImageNet mean and standard deviation |
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| ## 📄 License |
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| This model is licensed under the Apache 2.0 License. You are free to use, modify, and distribute it with proper attribution. |
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| ## 🙋 Author |
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| Created by Sathvik as part of a deep learning exploration project focused on image classification and CNN architecture optimization. |
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