| --- |
| license: mit |
| tags: |
| - image-classification |
| - pytorch |
| - cats-vs-dogs |
| - computer-vision |
| datasets: |
| - microsoft/cats_vs_dogs |
| metrics: |
| - accuracy |
| --- |
| |
| # Cat vs Dog Classifier |
|
|
| This model is a simple CNN (Convolutional Neural Network) trained to classify images as either cats or dogs. |
|
|
| ## Model Description |
|
|
| - **Architecture**: Custom CNN with 3 convolutional layers and 3 fully connected layers |
| - **Input**: 224x224 RGB images |
| - **Output**: Binary classification (Cat or Dog) |
| - **Framework**: PyTorch |
|
|
| ## Training Data |
|
|
| The model was trained on the [microsoft/cats_vs_dogs](https://huggingface.co/datasets/microsoft/cats_vs_dogs) dataset from Hugging Face. |
|
|
| ## Usage |
|
|
| ```python |
| import torch |
| from PIL import Image |
| from torchvision import transforms |
| |
| # Load model |
| model = CatDogClassifier() |
| model.load_state_dict(torch.load('model_weights.pth')) |
| model.eval() |
| |
| # Prepare image |
| transform = transforms.Compose([ |
| transforms.Resize((224, 224)), |
| transforms.ToTensor(), |
| transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) |
| ]) |
| |
| image = Image.open('your_image.jpg').convert('RGB') |
| image_tensor = transform(image).unsqueeze(0) |
| |
| # Predict |
| with torch.no_grad(): |
| outputs = model(image_tensor) |
| _, predicted = torch.max(outputs.data, 1) |
| |
| classes = ['Cat', 'Dog'] |
| print(f"Prediction: {classes[predicted.item()]}") |
| ``` |
|
|
| ## Training Procedure |
|
|
| - **Optimizer**: Adam with learning rate 0.001 |
| - **Loss Function**: CrossEntropyLoss |
| - **Batch Size**: 32 |
| - **Epochs**: 10 |
| - **Data Augmentation**: Random horizontal flip, rotation, and color jitter |
|
|
| ## Performance |
|
|
| The model achieves approximately 85-90% accuracy on the validation set (results may vary based on training run). |
|
|
| ## Limitations |
|
|
| - Model is trained specifically on cats and dogs only |
| - Performance may degrade on images with multiple animals |
| - Works best with clear, well-lit images |
| - Input images must be resized to 224x224 |
|
|
| ## License |
|
|
| MIT License |
|
|
| ## Author |
|
|
| Created as an educational project for learning image classification with PyTorch. |
|
|
