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--- |
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license: apache-2.0 |
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tags: |
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- image-classification |
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- cnn |
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- pytorch |
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- cat-vs-dog |
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- deep-learning |
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library_name: pytorch |
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datasets: |
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- custom |
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metrics: |
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- name: accuracy |
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type: accuracy |
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value: 90.56 |
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model_name: Cat vs Dog Classifier |
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pipeline_tag: image-classification |
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--- |
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--- |
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# 🐾 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**: |
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- Input channels: 3 (RGB) |
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- Output channels: 64 |
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- Kernel size: 3 × 3 |
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- BatchNorm + ReLU + MaxPool (2 × 2) |
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- **Conv Block 2**: |
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- Input channels: 64 |
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- Output channels: 128 |
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- Kernel size: 3 × 3 |
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- BatchNorm + ReLU + MaxPool (2 × 2) |
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- **Conv Block 3**: |
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- Input channels: 128 |
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- Output channels: 256 |
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- Kernel size: 3 × 3 |
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- BatchNorm + ReLU + MaxPool (2 × 2) |
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- **Conv Block 4**: |
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- Input channels: 256 |
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- Output channels: 512 |
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- Kernel size: 3 × 3 |
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- 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**: |
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- Input: Flattened feature vector |
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- Output: 512 units |
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- BatchNorm + ReLU + Dropout (p=0.5) |
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- **FC Layer 2**: |
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- Input: 512 units |
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- Output: 256 units |
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- BatchNorm + ReLU + Dropout (p=0.5) |
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- **FC Layer 3**: |
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- Input: 256 units |
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- Output: 2 units (cat or dog) |
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## 🧪 Training Details |
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- **Framework**: PyTorch |
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- **Input size**: 224 × 224 RGB images |
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- **Optimizer**: Adam with learning rate 0.0001 and weight decay 1e-4 |
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- **Loss Function**: CrossEntropyLoss |
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- **Scheduler**: ReduceLROnPlateau (monitors validation accuracy) |
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- **Epochs**: 40 |
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- **Batch Size**: 32 |
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- **Validation Split**: 20% of training data |
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- **Best Validation Accuracy**: 90.26% |
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- **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 |
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- Random rotations (±10 degrees) |
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- Color jitter (brightness and contrast) |
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- 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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