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--- |
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license: mit |
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language: |
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- en |
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tags: |
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- dataset |
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- AI |
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- ML |
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- object detection |
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- hockey |
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- puck |
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metrics: |
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- recall |
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- precision |
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- mAP |
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datasets: |
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- HockeyAI |
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--- |
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# HockeyAI YOLOv8 Model |
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<div style="background-color:#f8f9fa; color:black; border-left: 6px solid #28a745; padding: 10px; margin: 10px 0;"> |
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🔗 This model is trained on the <span style="color:red">HockeyAI</span> dataset. |
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- 📊 Access the dataset used for training here: <a href="https://huggingface.co/datasets/SimulaMet-HOST/HockeyAI" style="color:blue;">https://huggingface.co/datasets/SimulaMet-HOST/HockeyAI</a> |
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- 🚀 Try the model in action with our interactive <span style="color:red">Hugging Face Space</span>: <a href="https://huggingface.co/spaces/SimulaMet-HOST/HockeyAI" style="color:blue;">https://huggingface.co/spaces/SimulaMet-HOST/HockeyAI</a> |
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</div> |
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## Model Overview |
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The HockeyAI project provides a **YOLOv8 medium model** fine-tuned on the HockeyAI dataset. This model serves as a benchmark for ice hockey object detection tasks and achieves high performance across all seven classes defined in the dataset. |
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## Model Performance |
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The model was evaluated on a holdout set of the HockeyAI dataset, achieving the following performance metrics: |
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- **Mean Average Precision (mAP@0.5)**: XX.X% |
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- **Precision**: 100% for all classes |
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- **Recall**: 95% for all classes |
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- **F1-Score**: 93% for all classes |
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## Usage |
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The pretrained model is available in this repository as a `.pt` file. You can download and use it directly with the YOLOv8 framework for: |
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- Inference on new hockey videos or images |
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- Further fine-tuning on your specific use case |
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- Benchmarking against new approaches |
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## Supported Classes |
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The model is trained to detect seven classes: |
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- Center Ice |
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- Faceoff Dots |
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- Goal Frame |
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- Goaltender |
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- Players |
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- Puck |
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- Referee |
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## Requirements |
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- YOLOv8 framework |
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- Python 3.7+ |
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- PyTorch 1.7+ |
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## Getting Started |
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1. Download the model weights from this repository |
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2. Install the required dependencies |
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3. Load and use the model with YOLOv8's standard API |
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<div style="background-color:#e7f3ff; color:black; border-left: 6px solid #0056b3; padding: 12px; margin: 10px 0;"> |
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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> |
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<ul style="color:black;"> |
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<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> |
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<li><span style="font-weight:bold; color:black;">Cise Midoglu</span>: <a href="mailto:cise@forzasys.com" style="color:blue; text-decoration:none;">cise@forzasys.com</a></li> |
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<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> |
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</ul> |
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</div> |
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