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Motoreid

MotoReID provides the trained model assets used for MotoGP team detection and team classification from race broadcast footage. The full project combines YOLOv8 for motorcycle detection, DINOv3 for visual feature...

ArtOtherVersionRevision 6d79823CBewertung

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Other
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49,6–82,5 MB
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vRevision 6d79823
Aktualisiert
16. Sept. 2026
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3
Quelle
Hugging Face

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MotoReID provides the trained model assets used for MotoGP team detection and team classification from race broadcast footage. The full project combines YOLOv8 for motorcycle detection, DINOv3 for visual feature...

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Hugging FacePrimärVersion 6d798238557e6a1c497e24f9018087d367f457f8
huggingface.co/johnamit/motoreid/tree/6d798238557e6a1c497e24f9018087d367f457f8

Verifiziert 2. Sept.

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models/DINO/dinov3_vits16_pretrain_lvd1689m.pth

pth · 82,5 MB · SHA-256 08c60483bc63…941d · Hugging Face

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models/YOLO/motogp_yolov8m/weights/best.pt

pt · 49,6 MB · SHA-256 7bc6460847a0…44f4 · Hugging Face

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--- license: mit tags: - yolov8 - object-detection - image-classification - dinov3 - motogp - motorcycle-racing - computer-vision - pytorch datasets: - custom pipeline_tag: object-detection library_name: ultralytics --- <div> <img src="assets/MotoReID.png" alt="MotoReID" width="900"> MotoReID provides the trained model assets used for MotoGP team detection and team classification from race broadcast footage. The full project combines YOLOv8 for motorcycle detection, DINOv3 for visual feature extraction, a lightweight logistic regression classifier for team prediction, and BoT-SORT configuration for tracked inference in the main pipeline. [![GitHub](https://img.shields.io/badge/GitHub-Repository-181717?style=for-the-badge&logo=github)](https://github.com/johnamit/motoreid) </div> ## Repository Contents This Hugging Face repository stores the model assets used by the main [MotoReID GitHub project](https://github.com/johnamit/motoreid). ### Layout | Path | Description | |------|-------------| | `assets/MotoReID.png` | Project banner image | | `models/DINO/` | DINOv3 backbone weights used for feature extraction | | `models/classifier/` | Trained team-classification model | | `models/YOLO/` | Fine-tuned YOLO detector weights and tracker configuration | ## Pipeline Overview ```text Video Frame -> YOLOv8 Detection -> Crop Extraction -> DINOv3 Embedding -> Team Classification For tracked demo/inference runs, the main project also uses BoT-SORT configuration stored in the YOLO model directory. ``` ## 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 | ## How To Use ### Option 1: Download the full repository Clone or download this Hugging Face repo, then place the models/ folder into your local project root so the code can find the weights using the expected paths. Expected local structure: ```text MotoGP-Team-Detection/ ├── assets/ ├── data/ ├── models/ │ ├── DINO/ │ ├── YOLO/ │ └── classifier/ ├── results/ ├── src/ │ ├── data/ │ ├── inference/ │ ├── scripts/ │ └── training/ ├── README.md └── requirements.txt ``` ### Option 2: Download with huggingface_hub ```python from huggingface_h...

Source context: 3 downloads · 0 likes · Pipeline object-detection · Library ultralytics · Repo johnamit/motoreid