opensportslib 0.1.1.dev6

OpenSportsLib is the professional library, designed for advanced video understanding in sports. It provides state-of-the-art tools for action recognition, spotting, retrieval, and captioning, making it ideal for researchers, analysts, and developers working with sports video data.

OpenSportsLib

OpenSportsLib is a modular Python library for sports video understanding.

It provides a unified framework to train, evaluate, and run inference for key temporal understanding tasks in sports video, including:

• Action classification
• Action localization / spotting
• Action retrieval
• Action description / captioning

OpenSportsLib is designed for researchers, ML engineers, and sports analytics teams who want reproducible and extensible workflows for sports video AI.

Why OpenSportsLib?

• Unified workflow for training and inference
• Modular design for adding new tasks, datasets, and models
• Config driven experiments for reproducibility
• Support for multiple modalities and sports workflows
• Research friendly while still usable in applied settings

Quick links

• Documentation: https://opensportslab.github.io/opensportslib/
• PyPI: https://pypi.org/project/opensportslib/
• Issues: https://github.com/OpenSportsLab/opensportslib/issues

---

Installation

Requires Python 3.12+.
Supports CUDA 12.6 / 12.8 / 13.0 (with CPU fallback).
PyTorch Geometric is supported up to PyTorch 2.10.*.

Stable release

pip install opensportslib

Pre release

pip install --pre opensportslib

Setup Environment (PyTorch, CUDA aware & Optional Dependencies)

# Install PyTorch (CPU/GPU auto-detected)
opensportslib setup

# Optional: install PyTorch Geometric support
opensportslib setup --pyg

# Optional: install for DALI support
opensportslib setup --dali

---

Note:
Run opensportslib setup to automatically configure dependencies.
If issues occur, manually install compatible versions of torch, torchvision, and related libraries according to your CUDA version or system compatibility.

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Data and pretrained models

OpenSportsLib uses external annotation files, datasets, and pretrained checkpoints.

Public assets are hosted under the OpenSportsLab Hugging Face organization:

https://huggingface.co/OpenSportsLab

Use it as the main entry point to find:

• datasets
• annotation files
• extracted features
• pretrained models and checkpoints

--

Quickstart

Import the library

import opensportslib
print("OpenSportsLib imported successfully")

Train a classification model

from opensportslib import model

myModel = model.classification(
    config="/path/to/classification.yaml"
)

myModel.train(
    train_set="/path/to/train_annotations.json",
    valid_set="/path/to/valid_annotations.json",
    pretrained="/path/to/pretrained.pt",  # optional
)

Run inference

from opensportslib import model

myModel = model.classification(
    config="/path/to/classification.yaml"
)

metrics = myModel.infer(
    test_set="/path/to/test_annotations.json",
    pretrained="/path/to/checkpoints/final_model",
    predictions="/path/to/predictions.json"
)

print(metrics)

Localization example

from opensportslib import model

myModel = model.localization(
    config="/path/to/localization.yaml"
)

---

What you can do with OpenSportsLib

Action Classification

Classify clips or event centered samples into predefined categories.

Action Localization / Spotting

Predict when key events happen in long untrimmed sports videos.

Action Retrieval

Search and retrieve relevant clips or moments from a collection of sports videos.

Action Description / Captioning

Generate text descriptions for sports events and temporal segments.

---

Typical workflow

1. Prepare your dataset in the expected format
2. Select or create a YAML config
3. Initialize the task specific model
4. Train on your annotations
5. Run inference on new data
6. Extend the pipeline with your own datasets or models

---

Examples and documentation

Use the README for the fast start, then go deeper through:

• Full documentation: https://opensportslab.github.io/opensportslib/
• Configuration guide: https://opensportslab.github.io/opensportslib/tni/config-guide/
• Example configs: examples/configs/
• Quickstart scripts: examples/quickstart/
• Contribution guide: CONTRIBUTING.md
• Developer guide: DEVELOPERS.md

---

Development setup

For contributors who want to work from source:

git clone https://github.com/OpenSportsLab/opensportslib.git
cd opensportslib
pip install -e .

Conda option

If you prefer conda:

conda create -n osl python=3.12 pip
conda activate osl
pip install -e .

Setup Environment (PyTorch, CUDA aware & Optional Dependencies)

# Install PyTorch (CPU/GPU auto-detected)
opensportslib setup

# Optional: install PyTorch Geometric support
opensportslib setup --pyg

# Optional: install for DALI support
opensportslib setup --dali

Git workflow

1. Make sure you are branching from dev
2. Create your feature or fix branch from dev
3. Open a pull request back into dev

---

Contributing

We welcome contributions to OpenSportsLib.

Please check:

• CONTRIBUTING.md
• DEVELOPERS.md

These documents describe:

• how to add models and datasets
• coding standards
• training pipeline structure
• how to run and test the framework

---

License

OpenSportsLib is available under dual licensing.

Open source license

AGPL 3.0 for research, academic, and community use.

Commercial license

For proprietary or commercial deployment, please refer to LICENSE-COMMERCIAL.

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Citation

If you use OpenSportsLib in your research, please cite the project.

@misc{opensportslib,
  title={OpenSportsLib},
  author={OpenSportsLab},
  year={2026},
  howpublished={\url{https://github.com/OpenSportsLab/opensportslib}}
}

---

Acknowledgments

OpenSportsLib is developed within the broader OpenSportsLab effort for sports video understanding.