---
license: mit
language:
- en
tags:
- dataset
- AI
- ML
- object detection
- hockey
- puck
metrics:
- recall
- precision
- mAP
datasets:
- HockeyAI
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: image
    dtype: image
  splits:
  - name: train
    num_bytes: 409449992.92
    num_examples: 1890
  download_size: 363401335
  dataset_size: 409449992.92
---

# HockeyAI: A Multi-Class Ice Hockey Dataset for Object Detection

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🔗 This dataset is part of the <span style="color:red">HockeyAI</span> ecosystem.

- 💻 Check out the corresponding <span style="color:red">Hugging Face Space</span> for a live demo: <a href="https://huggingface.co/spaces/SimulaMet-HOST/HockeyAI" style="color:blue;">https://huggingface.co/spaces/SimulaMet-HOST/HockeyAI</a>
- 🏒 The trained <span style="color:red">model</span> for this dataset is available here: <a href="https://huggingface.co/SimulaMet-HOST/HockeyAI" style="color:blue;">https://huggingface.co/SimulaMet-HOST/HockeyAI</a>

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The **HockeyAI dataset** is an open-source dataset designed specifically for advancing computer vision research in ice hockey. With approximately **2,100 high-resolution frames** and detailed YOLO-format annotations, this dataset provides a rich foundation for tackling the challenges of object detection in fast-paced sports environments. 

The dataset is ideal for researchers, developers, and practitioners seeking to improve object detection and tracking tasks in ice hockey or similar dynamic scenarios.

## Dataset Overview

The HockeyAI dataset includes frames extracted from **broadcasted Swedish Hockey League (SHL) games**. Each frame is manually annotated, ensuring high-quality labels for both dynamic objects (e.g., players, puck) and static rink elements (e.g., goalposts, center ice).

### Classes

The dataset includes annotations for the following seven classes:
- **centerIce**: Center circle on the rink
- **faceoff**: Faceoff dots
- **goal**: Goal frame
- **goaltender**: Goalkeeper
- **player**: Ice hockey players
- **puck**: The small, fast-moving object central to gameplay
- **referee**: Game officials

![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/647ceb7936e109abce3e9f1f/g7GiPlsOnaV1pPKhzb_Pz.jpeg)

### Key Highlights:
- **Resolution**: 1920×1080 pixels
- **Frames**: ~2,100
- **Source**: Broadcasted SHL videos
- **Annotations**: YOLO format, reviewed iteratively for accuracy
- **Challenges Addressed**:
  - Motion blur caused by fast camera movements
  - Small object (puck) detection
  - Crowded scenes with occlusions

## Applications

The dataset supports a wide range of applications, including but not limited to:
- **Player and Puck Tracking**: Enabling real-time tracking for tactical analysis.
- **Event Detection**: Detecting goals, penalties, and faceoffs to automate highlight generation.
- **Content Personalization**: Dynamically reframing videos to suit different screen sizes.
- **Sports Analytics**: Improving strategy evaluation and fan engagement.

## How to Use the Dataset

1. Download the dataset from [Hugging Face](https://huggingface.co/your-dataset-link).

2. The dataset is organized in the following structure:
```
HockeyAI
└── frames
    └── <Unique_ID>.jpg
└── annotations
    └── <Unique_ID>.txt
```

3. Each annotation file follows the YOLO format:
```
<class_id> <x_center> <y_center> <width> <height>
```
All coordinates are normalized to the image dimensions.

4. Use the dataset with your favorite object detection framework, such as YOLOv8 or PyTorch-based solutions.



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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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