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---
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
<div style="background-color:#f8f9fa; color:black; border-left: 6px solid #0073e6; padding: 10px; margin: 10px 0;">
🔗 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>
</div>
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
### 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.
<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>
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