Football-Player-Segmentation

# 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.  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