facebookresearch/SlowFast

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7410 stars · 1295 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

PySlowFast is a PyTorch-based open-source video understanding codebase from Facebook AI Research (FAIR). It provides implementations of state-of-the-art video classification and detection models such as SlowFast Networks, X3D, and Multiscale Vision Transformers (MViT), designed for efficient training and rapid research experimentation.

Use cases

• train a video classification model on Kinetics
• reproduce state-of-the-art video recognition results
• implement action recognition in videos
• run video action detection on AVA
• pretrain video models with self-supervised learning
• experiment with video transformer architectures like MViTv2
• train efficient video models like X3D

When to choose

• you need state-of-the-art video classification or detection backbones in PyTorch
• you are doing video understanding research and want to build on published FAIR models
• you want efficient training of video models with multigrid methods
• you need self-supervised video pretraining implementations (MAE, MaskFeat)

When to avoid

• you need a production-ready video inference service rather than a research codebase
• you work outside video understanding, e.g., image-only classification
• you need a simple high-level API with minimal configuration
• you don't have GPU resources for training large video models

Facets

library · maturity active

machine-learning deep-learning video-processing computer-vision deep-learning computer-vision machine-learning python cross-platform video-understanding video-classification action-recognition pytorch research fair vision-transformers self-supervised-learning video gpu linux

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