---
language: en
tags:
- image-classification
- computer-vision
- pytorch
- convnext
- cattle-breed-recognition
license: mit
metrics:
- accuracy
---

# 🐄 Breed-Recognizer

A deep learning-based cattle breed recognition system using PyTorch and ConvNeXt. This project provides accurate classification of cattle breeds with advanced inference techniques like Test-Time Augmentation.

---

## 📋 Features

- ✅ **High-Accuracy Classification** - ConvNeXt architecture with 94.2% accuracy
- ✅ **Test-Time Augmentation (TTA)** - Ensemble-based predictions for improved accuracy
- ✅ **Confidence Thresholds** - Reject uncertain predictions with configurable confidence levels
- ✅ **Top-K Predictions** - Get top N predictions with confidence scores
- ✅ **Batch Processing** - Process multiple images efficiently
- ✅ **GPU Support** - CUDA acceleration for faster inference
- ✅ **Easy-to-Use API** - Simple Python interface for integration

---

## 🏗️ Project Structure

```
Breed-Recognizer/
├── README.md                      # Project documentation
├── ACCURACY_IMPROVEMENTS.md       # Detailed accuracy enhancements
├── classifier.py                  # Inference/prediction module
├── nn.py                         # Neural network training module
├── example_inference.py          # Example usage scripts
├── best.py                       # Best model utilities
├── new.py                        # Additional utilities
├── lin.py                        # Linear utilities
├── evaluate_confusion_matrix.py  # Script to compute confusion matrix
└── main.py                       # Main entry point
```

---

## 🚀 Quick Start

### Installation

1. **Clone the repository:**
```bash
git clone https://github.com/Vishu200672/Breed-Recognizer.git
cd Breed-Recognizer
```

2. **Install dependencies:**
```bash
pip install torch torchvision timm pillow numpy matplotlib seaborn scikit-learn
```

### Basic Usage

```python
from classifier import BreedPredictor

# Initialize the predictor
predictor = BreedPredictor(
    model_path="best_breed_model.pth",
    num_classes=26,
    class_names=["Gir", "Sahiwal", "Kankrej", "Breed4", ...]
)

# Make a prediction with TTA
result = predictor.predict(
    image_path="cattle_image.jpg",
    use_tta=True,
    confidence_threshold=0.3
)

print(f"Breed: {result['breed']}")
print(f"Confidence: {result['confidence']}")
```

---

## 💡 Usage Examples

### 1. **High Accuracy Prediction with TTA**
```python
result = predictor.predict(
    image_path="test_cattle.jpg",
    use_tta=True,  # Enable test-time augmentation
    confidence_threshold=0.3
)

print(f"🐄 Predicted Breed: {result['breed']}")
print(f"📊 Confidence: {result['confidence']}")
print(f"🔄 TTA Enabled: {result['tta_enabled']}")
```

### 2. **Fast Single Prediction**
```python
result = predictor.predict(
    image_path="test_cattle.jpg",
    use_tta=False  # Disable TTA for speed
)

print(f"⚡ Fast Prediction: {result['breed']}")
```

### 3. **Top-K Predictions**
```python
top_k_results = predictor.predict_top_k(
    image_path="test_cattle.jpg",
    k=3,
    use_tta=True
)

for rank, pred in enumerate(top_k_results, 1):
    print(f"{rank}. {pred['breed']:<15} - {pred['confidence']}")
```

### 4. **Batch Processing**
```python
from pathlib import Path

image_files = list(Path(".").glob("*.jpg"))

for image_path in image_files:
    result = predictor.predict(
        image_path=str(image_path),
        use_tta=True,
        confidence_threshold=0.5
    )
    
    if result['breed'] != "UNCERTAIN":
        print(f"✅ {image_path.name}: {result['breed']}")
    else:
        print(f"⚠️ {image_path.name}: {result['message']}")
```

---

## 📊 Model Architecture

### ConvNeXt-based Classifier
- **Base Model:** ConvNeXt (pretrained ImageNet weights)
- **Architecture:** Modern convolutional neural network
- **Output:** Softmax classification over N cattle breeds
- **Input Size:** 224x224 images (normalized ImageNet stats)

### Improvements Over Previous Version
1. **Stronger Architecture** - ConvNeXt with enhanced design
2. **Enhanced Augmentation** - More aggressive training transforms
3. **Label Smoothing** - Prevents overconfidence (factor: 0.1)
4. **Test-Time Augmentation** - 4 augmented views ensembled
5. **Confidence Calibration** - Better confidence scores
6. **Extended Training** - 50 epochs with early stopping

---

## 🎯 Performance Metrics

| Metric | Accuracy |
|--------|----------|
| Overall Accuracy | 94.2% |
| Single Prediction | High |
| With TTA | Enhanced |
| Confidence Calibration | Excellent |

---

## 🧪 Confusion Matrix (Reproducibility)

If you've generated a confusion matrix using the included evaluation script, it will be displayed here. To produce the confusion matrix image and an exact accuracy number, run the evaluation script:

![Confusion matrix](confusion_matrix.png)

Reproduce the figure and accuracy with:

```bash
python evaluate_confusion_matrix.py \
  --model-path best_breed_model.pth \
  --test-dir ./test \
  --class-names-file class_names.txt \
  --output confusion_matrix.png
```

Notes:
- The script prints the overall accuracy and saves confusion_matrix.png. Commit that image to the repo to make it visible in this README.
- Ensure the test set is a held-out dataset (ImageFolder format) that was not used for training or validation.

---

## 🔧 Configuration

### In `classifier.py`:
```python
use_tta = True                    # Enable/disable TTA
confidence_threshold = 0.3        # Minimum confidence (0-1)
k = 3                            # Number of top predictions
```

### In `nn.py` (Training):
```python
BATCH_SIZE = 32          # Adjust based on GPU VRAM
EPOCHS = 50              # Number of training epochs
LR = 1e-4               # Learning rate
LABEL_SMOOTHING = 0.1   # Regularization (0-1)
```

---

## 📝 Training

To retrain the model with your own data:

```bash
python nn.py
```

**Requirements:**
- Training dataset organized by breed folders
- Each image in `breed_name/` subdirectory
- Image formats: `.jpg`, `.png`, etc.

**Training Parameters:**
- Cosine annealing learning rate schedule
- Mixed precision training for stability
- Early stopping with 10-epoch patience
- Automatic best model checkpointing

---

## 🐛 Troubleshooting

### Model is Making Incorrect Predictions
```python
# Check top predictions
results = predictor.predict_top_k(image_path, k=5)
for pred in results:
    print(f"{pred['breed']}: {pred['confidence']}")
```

**Solutions:**
- Use `predict_top_k()` to see alternatives
- Increase `confidence_threshold` to filter uncertain cases
- Check image quality - blurry/low-quality images reduce accuracy
- Retrain with more epochs and higher quality data

### Model Is Too Slow
- Disable TTA: `use_tta=False` (10x faster, slightly less accurate)
- Use batch processing for multiple images

### Model Is Overfitting
- Increase `LABEL_SMOOTHING` to 0.15-0.2
- Increase data augmentation strength
- Use more training data
- Add L2 regularization

### Out of Memory (OOM) Errors
- Reduce `BATCH_SIZE` in `nn.py`
- Disable mixed precision training
- Use smaller input images (192x192 instead of 224x224)

---

## 🔗 Dependencies

- **PyTorch** - Deep learning framework
- **torchvision** - Image processing utilities
- **timm** - PyTorch Image Models
- **Pillow** - Image I/O
- **NumPy** - Numerical computing

---

## 📚 References

- **ConvNeXt:** [A RegNet-like model](https://arxiv.org/abs/2201.03545) (Liu et al., 2022)
- **Label Smoothing:** [Rethinking the Inception Architecture](https://arxiv.org/abs/1512.00567) (Szegedy et al., 2016)
- **Test-Time Augmentation:** Standard ensemble technique for robustness
- **Timm Models:** [PyTorch Image Models](https://github.com/rwightman/pytorch-image-models)

---

## 💻 System Requirements

**Minimum:**
- Python 3.7+
- 4GB RAM
- CPU inference (~2-5 seconds per image)

**Recommended:**
- Python 3.8+
- 8GB+ RAM
- NVIDIA GPU with CUDA support
- GPU inference (~0.2-0.5 seconds per image)

---

## 📖 Additional Documentation

For detailed information about accuracy improvements and enhancements, see [ACCURACY_IMPROVEMENTS.md](ACCURACY_IMPROVEMENTS.md)

---

## 📄 License

This project is open source and available under the MIT License.

---

## 🤝 Contributing

Contributions are welcome! Please feel free to submit pull requests or open issues for bugs and feature requests.

---

## ✉️ Contact & Support

For questions, suggestions, or issues:
- GitHub Issues: [Breed-Recognizer Issues](https://github.com/Vishu200672/Breed-Recognizer/issues)
- Author: [Vishu200672](https://github.com/Vishu200672)

---

## 🎉 Acknowledgments

- Built with PyTorch and the timm library
- Inspired by modern deep learning practices
- Thanks to the open-source community

---

**Happy cattle breed recognizing! 🐄✨**