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README.md

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+---
+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.