README.md

---
annotations_creators: []
language: en
license: cc0-1.0
size_categories:
- n<1K
task_categories:
- object-detection
task_ids: []
pretty_name: football-player-segmentation
tags:
- fiftyone
- image
- object-detection
dataset_summary: '



  ![image/png](dataset_preview.gif)



  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 512 samples.


  ## Installation


  If you haven''t already, install FiftyOne:


  ```bash

  pip install -U fiftyone

  ```


  ## Usage


  ```python

  import fiftyone as fo

  import fiftyone.utils.huggingface as fouh


  # Load the dataset

  # Note: other available arguments include ''max_samples'', etc

  dataset = fouh.load_from_hub("Voxel51/Football-Player-Segmentation")


  # Launch the App

  session = fo.launch_app(dataset)

  ```

  '
---

# Dataset Card for football-player-segmentation

This dataset is specifically designed for computer vision tasks related to player detection and segmentation in foot goalkeeperders, and forwards, captured from various angles and distances.



![image/png](dataset_preview.gif)


This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 512 samples.

## Installation

If you haven't already, install FiftyOne:

```bash
pip install -U fiftyone
```

## Usage

```python
import fiftyone as fo
import fiftyone.utils.huggingface as fouh

# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = fouh.load_from_hub("Voxel51/Football-Player-Segmentation")

# Launch the App
session = fo.launch_app(dataset)
```


## Dataset Details

### Dataset Description

This dataset is specifically designed for computer vision tasks related to player detection and segmentation in football matches. The dataset contains images of players in different playing positions, such as goalkeepers, defenders, midfielders, and forwards, captured from various angles and distances. The images are annotated with pixel-level masks that indicate the player's location and segmentation boundaries, making it ideal for training deep learning models for player segmentation. The dataset is suitable for researchers and developers working on football-related computer vision applications, such as tracking players during a match or analysing player movements and behaviours. It is also useful for sports analysts and enthusiasts who want to explore player performance metrics and trends based on positional data. Overall, this football player segmentation dataset is a valuable resource for anyone interested in advancing computer vision techniques for sports analysis and tracking.


- **Language(s) (NLP):** en
- **License:** cc0-1.0

### Dataset Sources

<!-- Provide the basic links for the dataset. -->

- **Original Source:** [kaggle](https://www.kaggle.com/datasets/ihelon/football-player-segmentation)


## Uses

- Object Detection
- Segmentation

## Dataset Structure

The dataset contains two fields, `detections` and `segmentations` across 512 different samples