HockeyAI YOLOv8 Model
Model Overview
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.
Model Performance
The model was evaluated on a holdout set of the HockeyAI dataset, achieving the following performance metrics:
- Mean Average Precision (mAP@0.5): XX.X%
- Precision: 100% for all classes
- Recall: 95% for all classes
- F1-Score: 93% for all classes
Usage
The pretrained model is available in this repository as a .pt file. You can download and use it directly with the YOLOv8 framework for:
- Inference on new hockey videos or images
- Further fine-tuning on your specific use case
- Benchmarking against new approaches
Supported Classes
The model is trained to detect seven classes:
- Center Ice
- Faceoff Dots
- Goal Frame
- Goaltender
- Players
- Puck
- Referee
Requirements
- YOLOv8 framework
- Python 3.7+
- PyTorch 1.7+
Getting Started
- Download the model weights from this repository
- Install the required dependencies
- Load and use the model with YOLOv8's standard API
📩 For any questions regarding this project, or to discuss potential collaboration and joint research opportunities, please contact:
