language:
- en
license: cc-by-nc-nd-4.0
task_categories:
- image-segmentation
tags:
- code
dataset_info:
- config_name: video_01
features:
- name: id
dtype: int32
- name: name
dtype: string
- name: image
dtype: image
- name: mask
dtype: image
- name: width
dtype: uint16
- name: height
dtype: uint16
- name: shapes
sequence:
- name: label
dtype:
class_label:
names:
'0': referee
'1': background
'2': wrestling
'3': human
- name: type
dtype: string
- name: points
sequence:
sequence: float32
- name: rotation
dtype: float32
- name: occluded
dtype: uint8
- name: z_order
dtype: int16
- name: attributes
sequence:
- name: name
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 45562
num_examples: 10
download_size: 16130822
dataset_size: 45562
- config_name: video_02
features:
- name: id
dtype: int32
- name: name
dtype: string
- name: image
dtype: image
- name: mask
dtype: image
- name: width
dtype: uint16
- name: height
dtype: uint16
- name: shapes
sequence:
- name: label
dtype:
class_label:
names:
'0': referee
'1': background
'2': wrestling
'3': human
- name: type
dtype: string
- name: points
sequence:
sequence: float32
- name: rotation
dtype: float32
- name: occluded
dtype: uint8
- name: z_order
dtype: int16
- name: attributes
sequence:
- name: name
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 61428
num_examples: 10
download_size: 14339242
dataset_size: 61428
- config_name: video_03
features:
- name: id
dtype: int32
- name: name
dtype: string
- name: image
dtype: image
- name: mask
dtype: image
- name: width
dtype: uint16
- name: height
dtype: uint16
- name: shapes
sequence:
- name: label
dtype:
class_label:
names:
'0': referee
'1': background
'2': wrestling
'3': human
- name: type
dtype: string
- name: points
sequence:
sequence: float32
- name: rotation
dtype: float32
- name: occluded
dtype: uint8
- name: z_order
dtype: int16
- name: attributes
sequence:
- name: name
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 42854
num_examples: 9
download_size: 13763862
dataset_size: 42854
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