Datasets:
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README.md
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# Note: other available arguments include ''max_samples'', etc
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dataset = fouh.load_from_hub("
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# Launch the App
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# Dataset Card for football-player-segmentation
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### Dataset Description
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<!-- Provide the basic links for the dataset. -->
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the dataset is intended to be used. -->
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### Direct Use
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<!-- This section describes suitable use cases for the dataset. -->
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[More Information Needed]
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### Out-of-Scope Use
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## Dataset Structure
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[More Information Needed]
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## Dataset Creation
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### Curation Rationale
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<!-- Motivation for the creation of this dataset. -->
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[More Information Needed]
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### Source Data
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<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
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#### Data Collection and Processing
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<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
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[More Information Needed]
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#### Who are the source data producers?
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<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
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[More Information Needed]
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### Annotations [optional]
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<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
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#### Annotation process
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<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
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[More Information Needed]
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#### Who are the annotators?
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<!-- This section describes the people or systems who created the annotations. -->
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[More Information Needed]
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#### Personal and Sensitive Information
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<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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**APA:**
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## More Information [optional]
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## Dataset Card Authors [optional]
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## Dataset Card Contact
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[More Information Needed]
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# Note: other available arguments include ''max_samples'', etc
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dataset = fouh.load_from_hub("voxel51/Football-Player-Segmentation")
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# Launch the App
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# Dataset Card for football-player-segmentation
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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.
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### Dataset Description
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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.
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<!-- Provide the basic links for the dataset. -->
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- **Original Source:** [kaggle](https://www.kaggle.com/datasets/ihelon/football-player-segmentation)
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## Uses
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- Object Detection
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- Segmentation
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## Dataset Structure
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The dataset contains two fields, `detections` and `segmentations` across 512 different samples
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