🐾 Cat vs Dog Classifier
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.
🧠 Model Overview
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.
Convolutional Feature Extractor
-
Conv Block 1:
- Input channels: 3 (RGB)
- Output channels: 64
- Kernel size: 3 × 3
- BatchNorm + ReLU + MaxPool (2 × 2)
-
Conv Block 2:
- Input channels: 64
- Output channels: 128
- Kernel size: 3 × 3
- BatchNorm + ReLU + MaxPool (2 × 2)
-
Conv Block 3:
- Input channels: 128
- Output channels: 256
- Kernel size: 3 × 3
- BatchNorm + ReLU + MaxPool (2 × 2)
-
Conv Block 4:
- Input channels: 256
- Output channels: 512
- Kernel size: 3 × 3
- BatchNorm + ReLU + MaxPool (2 × 2)
After these blocks, the feature map is flattened to a 1D vector for classification.
Fully Connected Classifier
-
FC Layer 1:
- Input: Flattened feature vector
- Output: 512 units
- BatchNorm + ReLU + Dropout (p=0.5)
-
FC Layer 2:
- Input: 512 units
- Output: 256 units
- BatchNorm + ReLU + Dropout (p=0.5)
-
FC Layer 3:
- Input: 256 units
- Output: 2 units (cat or dog)
🧪 Training Details
- 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%
🎨 Data Augmentation
To improve generalization, the training data was augmented with:
- Random horizontal flips
- Random rotations (±10 degrees)
- Color jitter (brightness and contrast)
- Normalization using ImageNet mean and standard deviation
📄 License
This model is licensed under the Apache 2.0 License. You are free to use, modify, and distribute it with proper attribution.
🙋 Author
Created by Sathvik as part of a deep learning exploration project focused on image classification and CNN architecture optimization.
