task_categories:image-segmentation language:en license:cc-by-nc-nd-4.0 code region:us
The dataset consists of a collection of photos extracted from **videos of fights**.
It includes **segmentation masks** for **fighters, referees, mats, and the background**.
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.
@InProceedings{huggingface:dataset,
title = {fights-segmentation},
author = {TrainingDataPro},
year = {2023}
}
---
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
---

# Fights Segmentation 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**. 

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.

![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F528c5d5de741e46d8754a5a67ff476fc%2FFrame%2024.png?generation=1695968589650484&alt=media)

# Get the dataset

### This is just an example of the data

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.

# 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

![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F538310907b1e8b4c6f07f456331fe091%2Fcarbon.png?generation=1695969032771522&alt=media)

# Fights Segmentation might be made in accordance with your requirements.

## [**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

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

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