🐦 Bird Species Classifier

Advanced Bird Classification Model

This model can classify 199 different bird species using advanced deep learning techniques:

Model Details:

  • Architecture: Auto-detected EfficientNet (B4) with enhanced regularization & fine-tuned with Swin Transformer (Emiel/cub-200-bird-classifier-swin)
  • Training Strategy: Progressive training with advanced augmentation
  • Performance: Optimized for accuracy and reliability
  • Dataset: CUB-200-2011 (200 bird species)

How to use:

  1. Upload a clear image of a bird
  2. The model will predict the top 5 most likely species
  3. Confidence scores show the model's certainty

Best Results Tips:

  • Use high-quality, well-lit images
  • Ensure the bird is clearly visible
  • Close-up shots work better than distant ones
  • Natural lighting produces better results

Note: This model was trained on the CUB-200-2011 dataset and works best with North American bird species.

Technical Implementation:

  • Framework: PyTorch with auto-detected EfficientNet backbone
  • Training: Progressive training with advanced augmentation strategies
  • Regularization: Optimized dropout rates and comprehensive validation
  • Image Size: 320x320 pixels for optimal detail capture

About the Model:

This bird classifier was developed using advanced machine learning techniques including:

  • Transfer learning from ImageNet-pretrained EfficientNet
  • Progressive training strategy across multiple stages
  • Advanced data augmentation for improved generalization
  • Comprehensive evaluation and optimization

The model automatically detects the correct architecture (EfficientNet-B2 or B3) from the saved weights, ensuring compatibility and optimal performance.

For more details about the training process and methodology, please refer to the repository documentation.