chengtan9907/OpenSTL

Health v2 · maintenance only

Adoption not part of the score

1137 stars · 191 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

OpenSTL is a comprehensive benchmark and modular framework for spatio-temporal predictive learning, covering video prediction methods across synthetic and real-world tasks like human motion, driving scenes, traffic flow, and weather forecasting. It provides a layered PyTorch codebase with a PyTorch Lightning implementation, model zoo, and visualization tools.

Use cases

• benchmark video prediction models
• predict future weather frames from radar data
• forecast traffic flow from video
• train spatio-temporal predictive models in PyTorch
• compare transformer and CNN video prediction methods
• run experiments on moving MNIST and KTH datasets

When to choose

• you need a standardized benchmark for spatio-temporal prediction research
• you want a modular framework to implement and compare video prediction algorithms
• you need pretrained model zoos for weather or traffic forecasting

When to avoid

• you need production video analytics rather than research benchmarking
• your task is single-image prediction without temporal dynamics
• you work outside PyTorch

Facets

framework · maturity active

machine-learning deep-learning benchmarking data-science deep-learning computer-vision artificial-intelligence data-science python video-prediction spatio-temporal weather-forecasting pytorch-lightning self-supervised-learning model-zoo benchmark-suite gpu

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markdown · JSON · MCP: product_card(name="chengtan9907/OpenSTL")