README.md

metadata

license: apache-2.0
tags:
  - cattle-breed-classification
  - buffalo-breed-classification
  - livestock-recognition
  - agriculture
  - computer-vision
  - onnx
  - resnet50
pipeline_tag: image-classification
base_model: microsoft/resnet-50

Cattle & Buffalo Breed Classification Model

This model classifies cattle and buffalo breeds using computer vision. It's based on ResNet-50 architecture and trained to recognize 10 different breeds.

Model Description

• Model Type: Feature extraction + similarity matching
• Architecture: ResNet-50 backbone with L2 normalization
• Format: ONNX (89.6MB)
• Input: RGB images (224x224)
• Output: 2048-dimensional feature vectors
• Classification: Cosine similarity with breed prototypes

Supported Breeds

Buffalo Breeds (5)

• Bhadawari
• Jaffarbadi
• Mehsana
• Murrah
• Surti

Cattle Breeds (5)

• Gir
• Kankrej
• Ongole
• Sahiwal
• Tharparkar

Usage

Using ONNX Runtime

import onnxruntime as ort
import numpy as np
from PIL import Image
import json

# Load model
session = ort.InferenceSession('model.onnx')

# Load breed prototypes
with open('prototypes.json', 'r') as f:
    prototypes = json.load(f)

# Preprocess image
def preprocess_image(image_path):
    from torchvision import transforms
    
    transform = transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize(
            mean=[0.485, 0.456, 0.406],
            std=[0.229, 0.224, 0.225]
        )
    ])
    
    image = Image.open(image_path).convert('RGB')
    tensor = transform(image).unsqueeze(0)
    return tensor.numpy()

# Predict breed
def predict_breed(image_path):
    # Get features
    input_data = preprocess_image(image_path)
    features = session.run(None, {'input': input_data})[0][0]
    
    # Calculate similarities
    similarities = {}
    for breed, prototype in prototypes['prototypes'].items():
        similarity = np.dot(features, prototype)
        similarities[breed] = float(similarity)
    
    # Get top prediction
    predicted_breed = max(similarities, key=similarities.get)
    confidence = similarities[predicted_breed]
    
    return predicted_breed, confidence, similarities

# Example usage
breed, confidence, all_scores = predict_breed('path/to/image.jpg')
print(f"Predicted breed: {breed}")
print(f"Confidence: {confidence:.4f}")

Integration with Mobile Apps

// React Native example
import { ONNX } from 'onnxjs-react-native';

const model = new ONNX.InferenceSession();
await model.loadModel('path/to/model.onnx');

const prediction = await model.run([preprocessedImageTensor]);
// Process with prototypes for final classification

Model Performance

• Inference Time: ~45-50ms (CPU)
• Model Size: 89.6MB
• Accuracy: Optimized for livestock breed recognition
• Platforms: Cross-platform (ONNX Runtime support)

Files Included

• model.onnx: The trained ONNX model
• prototypes.json: Breed prototype vectors for classification
• config.json: Model configuration and metadata
• sample_images/: Example images for testing

Technical Details

• Feature Extraction: ResNet-50 backbone → 2048-dim features
• Normalization: L2 normalization applied to features
• Classification: Cosine similarity with pre-computed breed prototypes
• Preprocessing: ImageNet-style normalization (mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])

License

Apache 2.0

Citation

If you use this model, please cite:

@misc{cattle-breed-classifier,
  title={Cattle and Buffalo Breed Classification Model},
  author={Your Name},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/your-username/cattle-breed-classifier}
}

Contact

For questions or issues, please open an issue in the model repository.