UFC/MMA Fights Images Segmentation, Sport Dataset

The dataset consists of a collection of photos extracted from videos of fights. It includes segmentation masks for fighters, referees, mats, and the background.

💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on TrainingData to buy the dataset

The dataset offers a resource for object detection, instance segmentation, action recognition, or pose estimation.
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.

💴 Buy the Dataset: This is just an example of the data. Leave a request on https://trainingdata.pro/datasets to discuss your requirements, learn about the price and buy the dataset

Dataset structure

images - contains of original images extracted from the videos of fights
masks - includes segmentation masks created for the original images
annotations.xml - contains coordinates of the polygons and labels, created for the original photo

Data Format

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.

Сlasses:

human: fighter or fighters,
referee: referee,
wrestling: mat's area,
background: area above the mat

Example of XML file structure

Fights Segmentation might be made in accordance with your requirements.

TrainingData provides high-quality data annotation tailored to your needs

More datasets in TrainingData's Kaggle account: https://www.kaggle.com/trainingdatapro/datasets

TrainingData's GitHub: https://github.com/Trainingdata-datamarket/TrainingData_All_datasets

keywords: body segmentation dataset, human segmentation dataset, human body segmentation, people images dataset, biometric data dataset, biometric dataset, ufc athletes, sports dataset, ultimate fighting championship, semantic segmentation, computer vision, deep learning, machine learning, image dataset, image classification, human images