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blatoet/
cat-dog-classifier

Name: cat-dog-classifier
Rating: 2.5 (8 reviews)

microsoft/cats_vs_dogs

mit

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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 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.

blatoet/cat-dog-classifier