Training Data
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README.md
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license: cc-by-nc-nd-4.0
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---
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license: cc-by-nc-nd-4.0
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task_categories:
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- image-segmentation
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language:
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- en
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tags:
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- code
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---
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# Fights Segmentation Dataset
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The dataset consists of a collection of photos extracted from **videos of fights**. It includes **segmentation masks** for **fighters, referees, mats, and the background**.
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The dataset offers a resource for *object detection, instance segmentation, action recognition, or pose estimation*.
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It could be useful for **sport community** in identification and detection of the violations, dispute resolution and general optimisation of referee's work using computer vision.
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# Get the dataset
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### This is just an example of the data
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Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=fights-segmentation) to discuss your requirements, learn about the price and buy the dataset.
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# Dataset structure
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- **images** - contains of original images extracted from the videos of fights
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- **masks** - includes segmentation masks created for the original images
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- **annotations.xml** - contains coordinates of the polygons and labels, created for the original photo
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# Data Format
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Each image from `images` folder is accompanied by an XML-annotation in the `annotations.xml` file indicating the coordinates of the polygons and labels. For each point, the x and y coordinates are provided.
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### Сlasses:
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- **human**: fighter or fighters,
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- **referee**: referee,
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- **wrestling**: mat's area,
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- **background**: area above the mat
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# Example of XML file structure
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# Fights Segmentation might be made in accordance with your requirements.
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## [**TrainingData**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=fights-segmentation) provides high-quality data annotation tailored to your needs
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More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets**
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TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**
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