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| title: HockeyAI | |
| emoji: 🏒 | |
| colorFrom: blue | |
| colorTo: indigo | |
| sdk: gradio | |
| sdk_version: 5.11.0 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| # 🏒 HockeyAI: A Multi-Class Ice Hockey Dataset for Object Detection | |
| <div style="background-color:#f8f9fa; color:black; border-left: 6px solid #ff9800; padding: 10px; margin: 10px 0;"> | |
| 🔗 This interactive demo is powered by the <span style="color:red">HockeyAI</span> model and dataset. | |
| - 📂 Download the dataset used in this project: <a href="https://huggingface.co/datasets/SimulaMet-HOST/HockeyAI" style="color:blue;">https://huggingface.co/datasets/SimulaMet-HOST/HockeyAI</a> | |
| - 🤖 View details of the trained <span style="color:red">model</span>: <a href="https://huggingface.co/SimulaMet-HOST/HockeyAI" style="color:blue;">https://huggingface.co/SimulaMet-HOST/HockeyAI</a> | |
| </div> | |
| HockeyAI is a specialized dataset and object detection system designed for ice hockey analysis. Built on the YOLOv8 architecture, this project provides accurate detection of key hockey game elements including players, officials, and game-specific features. The project includes both a comprehensive dataset and benchmark implementations using YOLOv8. | |
| ## 🎯 Dataset Classes | |
| Our dataset includes seven key classes essential for hockey game analysis: | |
| - Center Ice (centerIce) | |
| - Face-off Circles (faceoff) | |
| - Goals (goal) | |
| - Goaltenders (goaltender) | |
| - Players (player) | |
| - Pucks (puck) | |
| - Referees (referee) | |
| ## 📊 Model Specifications | |
| - **Architecture**: YOLOv8 Medium | |
| - **Framework**: Ultralytics YOLOv8 | |
| ## 🔧 Usage Guide | |
| 1. Upload any ice hockey game frame | |
| 2. The model will detect and classify: | |
| - Game elements (center ice, face-off circles, goals) | |
| - Personnel (players, goaltenders, referees) | |
| - Equipment (pucks) | |
| 3. View results with bounding boxes and confidence scores | |
| ## 💻 Technical Implementation | |
| - **Backend**: Python 3.9+ | |
| - **Interface**: Gradio 5.11.0 | |
| - **Deep Learning Framework**: PyTorch | |
| - **Hardware Optimization**: GPU-accelerated inference | |
| <div style="background-color:#e7f3ff; color:black; border-left: 6px solid #0056b3; padding: 12px; margin: 10px 0;"> | |
| <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:[email protected]" style="color:blue; text-decoration:none;">[email protected]</a></li> | |
| <li><span style="font-weight:bold; color:black;">Cise Midoglu</span>: <a href="mailto:[email protected]" style="color:blue; text-decoration:none;">[email protected]</a></li> | |
| <li><span style="font-weight:bold; color:black;">Pål Halvorsen</span>: <a href="mailto:[email protected]" style="color:blue; text-decoration:none;">[email protected]</a></li> | |
| </ul> | |
| </div> | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |