facebookresearch/TimeSformer

Health v2 · maintenance only

Flags: no_releases archived no_license

Adoption not part of the score

1863 stars · 246 forks observed · 2026-08-28

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

Official PyTorch implementation of TimeSformer, a space-time attention transformer model for video understanding from an ICML 2021 paper. It provides training and testing code plus pretrained models for video action recognition benchmarks like Kinetics-400.

Use cases

• classify human actions in videos
• train a video transformer on Kinetics-400
• fine-tune a pretrained video model on my own action recognition dataset
• run inference with a pretrained video classification model
• compare video transformer architectures for research
• evaluate video models on Something-Something-V2

When to choose

• you need a transformer-based video action recognition model with pretrained checkpoints
• you want a research baseline for video understanding experiments
• you need PyTorch code for training video classifiers on standard benchmarks

When to avoid

• you need lightweight or real-time video classification on edge devices
• you want a production-ready video analytics service rather than research code
• you need video tasks beyond classification such as detection or tracking

Facets

library · maturity maintenance

machine-learning deep-learning video-processing machine-learning computer-vision python video-understanding transformer action-recognition pytorch research-code icml-2021 video gpu

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For agents

markdown · JSON · MCP: product_card(name="facebookresearch/TimeSformer")