---
license: mit
---

# Hockey Rink Keypoint Detection

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🔗 This model is trained on the <span style="color:red">HockeyRink</span> dataset.

- 📊 Access the dataset used for training here: <a href="[DATASET_LINK]" style="color:blue;">https://huggingface.co/datasets/SimulaMet-HOST/HockeyRink</a>
- 🚀 Try the model in action with our interactive <span style="color:red">Hugging Face Space</span>: <a href="https://huggingface.co/spaces/SimulaMet-HOST/HockeyRink" style="color:blue;">https://huggingface.co/spaces/SimulaMet-HOST/HockeyRink</a>

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This repository contains a YOLOv8-based model for detecting and mapping keypoints on ice hockey rinks. The model is trained on the HockeyRink dataset, which comprises precise annotations of hockey rink landmarks.

## Features

- Accurate detection of 56 keypoint landmarks on hockey rinks
- Real-time keypoint visualization with confidence scores
- Support for various camera angles and lighting conditions
- Handles player occlusions and dynamic game situations
- Trained on diverse SHL (Swedish Hockey League) game footage

![image/webp](https://cdn-uploads.huggingface.co/production/uploads/647ceb7936e109abce3e9f1f/vnCRrsk8DP8fI_GcFO0Jt.webp)

![image/webp](https://cdn-uploads.huggingface.co/production/uploads/647ceb7936e109abce3e9f1f/sMbFk-DMBcoQ769FKmaf8.webp)

## Model Details

- Architecture: YOLOv8-Large pose estimation
- Input: RGB images (any resolution)
- Output: 56 keypoint coordinates with confidence scores
- Average Performance:
  - mAP@0.5: 97.48%
  - mAP@0.5:0.95: 76.45%
  - Precision: 96.21%
  - Recall: 96.24%

## Applications

- Camera calibration and homography estimation
- 2D/3D scene mapping
- Player tracking and analysis
- Broadcast overlay generation
- Game analytics and statistics
- AR/VR applications





## Model Performance

- Performance tested across different hardware setups
- 13.64 FPS on Tesla T4 GPU
- 6.4 FPS on M3 MacBook Pro
- Handles varying lighting conditions and occlusions


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<span style="color:black; font-weight:bold;">📩 For any questions regarding this project, or to discuss potential collaboration and joint research opportunities, please contact:</span>

<ul style="color:black;">
  <li><span style="font-weight:bold; color:black;">Mehdi Houshmand</span>: <a href="mailto:mehdi@forzasys.com" style="color:blue; text-decoration:none;">mehdi@forzasys.com</a></li>
  <li><span style="font-weight:bold; color:black;">Cise Midoglu</span>: <a href="mailto:cisemidoglu@gmail.com" style="color:blue; text-decoration:none;">cisemidoglu@gmail.com</a></li>
  <li><span style="font-weight:bold; color:black;">Pål Halvorsen</span>: <a href="mailto:paalh@simula.no" style="color:blue; text-decoration:none;">paalh@simula.no</a></li>
</ul>

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