---
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.