# HuggingFace Hub Integration

Comprehensive examples for using HuggingFace Hub models with AutoTimm.

## Basic Integration (8 examples)

- **`huggingface_hub_models.py`** - Introduction to HF Hub integration
- **`hf_hub_classification.py`** - Classification with HF Hub models
- **`hf_hub_segmentation.py`** - Semantic segmentation with HF Hub
- **`hf_hub_object_detection.py`** - Object detection with HF Hub
- **`hf_hub_instance_segmentation.py`** - Instance segmentation with HF Hub
- **`hf_hub_advanced.py`** - Advanced HF Hub features
- **`hf_hub_lightning_integration.py`** - PyTorch Lightning compatibility
- **`hf_direct_models_lightning.py`** - Direct HF Transformers models

## Advanced Techniques (4 examples)

- **`hf_interpretation.py`** - GradCAM, attention visualization, quantitative metrics
- **`hf_transfer_learning.py`** - LLRD, progressive unfreezing, discriminative fine-tuning
- **`hf_ensemble.py`** - Model ensembles and knowledge distillation
- **`hf_deployment.py`** - ONNX export, quantization, production deployment

## Quick Start

```bash
# Basic HF Hub usage
python huggingface/huggingface_hub_models.py

# Advanced: Model interpretation
python huggingface/hf_interpretation.py

# Advanced: Transfer learning
python huggingface/hf_transfer_learning.py
```

## Key Features

- **1000+ models** from HuggingFace Hub
- **All tasks supported**: Classification, detection, segmentation
- **Advanced techniques**: Interpretation, transfer learning, ensembles, deployment
- **Production-ready**: ONNX export, quantization, serving
