Duplicate from SimulaMet-HOST/HockeyAI

Co-authored-by: Mehdi Houshmand Sarkhoosh <MehdiH7@users.noreply.huggingface.co>

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HockeyAI_Dataset.zip

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README.md

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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
+
+![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.
+
+
+
+<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: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>
+
+</div>
+
+

data/train-00000-of-00001.parquet

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dataset-info.json

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+{
+  "classes": [
+    "centerIce",
+    "faceoff",
+    "goal",
+    "goaltender",
+    "player",
+    "puck",
+    "referee"
+  ],
+  "splits": [
+    "train",
+    "valid",
+    "test"
+  ]
+}

dataset-metadata.json

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+{
+  "annotations_creators": [
+    "expert-generated"
+  ],
+  "language": [
+    "en"
+  ],
+  "licenses": [
+    "mit"
+  ],
+  "multilinguality": [
+    "monolingual"
+  ],
+  "pretty_name": "HockeyAI Dataset",
+  "size_categories": [
+    "1K<n<10K"
+  ],
+  "source_datasets": [
+    "original"
+  ],
+  "task_categories": [
+    "object-detection"
+  ],
+  "task_ids": [
+    "object-detection"
+  ]
+}