README.md

---
license: mit
language:
- en
tags:
- dataset
- AI
- ML
- object detection
- hockey
- puck
metrics:
- recall
- precision
- mAP
datasets:
- HockeyAI
---

# HockeyAI YOLOv8 Model

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

- 📊 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>
- 🚀 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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## 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

1. Download the model weights from this repository
2. Install the required dependencies
3. Load and use the model with YOLOv8's standard API


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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:cise@forzasys.com" style="color:blue; text-decoration:none;">cise@forzasys.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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