README.md

---
license: mit
tags:
  - yolov8
  - object-detection
  - image-classification
  - dinov2
  - sports
  - motogp
  - motorcycle-racing
  - computer-vision
  - pytorch
datasets:
  - custom
pipeline_tag: object-detection
library_name: ultralytics
---
<div>
<img src="MotoReID.png" alt="MotoReID" width="900">
A deep learning pipeline for **MotoGP team detection, tracking, and re-identification** from race broadcast footage. This system combines **YOLOv8** for robust object detection with **DINOv3** (Vision Transformer) embeddings for semantic team classification. It addresses specific challenges in high-speed sports computer vision: persistent identity tracking across extreme occlusions, rapid camera cuts, and motion blur. This project is in active development.

  [![GitHub](https://img.shields.io/badge/GitHub-Repository-181717?style=for-the-badge&logo=github)](https://github.com/johnamit/mgp-detect)
</div>



## Model Overview

This repository contains trained model weights for the [MotoReID](https://github.com/johnamit/mgp-detect) project.

### Included Weights

| File | Description | Size |
|------|-------------|------|
| `motogp_yolov8m_detector.pt` | Fine-tuned YOLOv8m for MotoGP prototype (bike) detection | ~50MB |
| `dinov3_team_classifier.pkl` | Logistic Regression classifier trained on DINOv3 embeddings | ~35KB |

## Pipeline Architecture

```
Video Frame → YOLOv8 Detector → Crop → DINOv3 Feature Extraction → Team Classifier → Re-ID Tracking
```

1. **Detection**: YOLOv8m (fine-tuned) localizes motorcycles in each frame
2. **Feature Extraction**: DINOv3 ViT-S/16 extracts dense semantic features from detected regions
3. **Classification**: Logistic Regression head predicts team identity from DINO embeddings
4. **Re-ID & Tracking**: ByteTrack + Cosine Similarity Memory Bank for persistent tracking

## Supported Teams (2025 Season)

| Manufacturer | Teams |
|--------------|-------|
| **Ducati** | Lenovo Team, Gresini Racing, VR46 Racing |
| **Aprilia** | Factory Racing, Trackhouse Racing |
| **KTM** | Factory Racing, Tech3 |
| **Honda** | Repsol HRC, LCR Honda |
| **Yamaha** | Monster Energy, Prima Pramac |

## Usage

### 1. Download Weights

```python
from huggingface_hub import hf_hub_download

# Download YOLOv8 detector
detector_path = hf_hub_download(
    repo_id="johnamit/motogp-team-detection",
    filename="motogp_yolov8m_detector.pt"
)

# Download team classifier
classifier_path = hf_hub_download(
    repo_id="johnamit/motogp-team-detection",
    filename="dinov3_team_classifier.pkl"
)
```

### 2. Prerequisites (Base Models)

These weights require the following base models:

#### YOLOv8 (Ultralytics)
```bash
pip install ultralytics
```
[YOLOv8 Documentation](https://docs.ultralytics.com/models/yolov8/)

#### DINOv3 (Meta AI)
Clone the DINOv3 repository for feature extraction:
```bash
git clone https://github.com/facebookresearch/dinov3.git
```
Download the ViT-S/16 pretrained weights from the [DINOv3 repo](https://github.com/facebookresearch/dinov3).


## Training Details

### YOLOv8 Detector
- **Base Model**: YOLOv8m
- **Dataset**: 501 annotated instances (404 motorcycles, 97 null/background)
- **Augmentation**: Rotations, exposure adjustments, noise injection
- **Platform**: [Roboflow](https://app.roboflow.com)

### DINOv3 Team Classifier
- **Backbone**: DINOv3 ViT-S/16 (frozen, pretrained on LVD-142M)
- **Head**: Scikit-learn Logistic Regression
- **Dataset**: ~700 high-quality crops across 11 teams
- **Features**: 384-dimensional embeddings

## Performance

The system is optimized for broadcast footage with:
- High-speed motion blur handling
- Rapid camera cut recovery
- Persistent identity tracking across occlusions
- Label locking after high-confidence agreement

## Citation

If you use these weights in your research, please cite:

```bibtex
@software{motogp_team_detection_2025,
  author = {Amit John},
  title = {MotoGP Team Detection and Re-Identification},
  year = {2025},
  publisher = {GitHub},
  url = {https://github.com/johnamit/mgp-detect}
}
```

## License

MIT License - See the [GitHub repository](https://github.com/johnamit/mgp-detect) for full details.

## Acknowledgments

- [Ultralytics](https://ultralytics.com/) for YOLOv8
- [Meta AI](https://github.com/facebookresearch/dinov3) for DINOv3
- [Roboflow](https://roboflow.com/) for annotation tools