princeton-vl/RAFT

Health v2 · maintenance only

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4091 stars · 679 forks observed · 2026-08-28

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

Official PyTorch implementation of RAFT (Recurrent All Pairs Field Transforms for Optical Flow), an ECCV 2020 model for estimating dense optical flow between video frames. It includes pretrained models, demos, evaluation scripts, and training pipelines for standard optical flow benchmarks.

Use cases

• estimate optical flow between two image frames
• run a pretrained optical flow model on a sequence of frames
• evaluate optical flow models on Sintel or KITTI benchmarks
• train an optical flow network on FlyingChairs and FlyingThings3D
• reduce GPU memory usage for correlation lookups with an efficient CUDA implementation

When to choose

• you need state-of-the-art dense optical flow estimation in Python
• you want a research-grade, well-cited optical flow model with pretrained weights
• you have a CUDA GPU and want to train or fine-tune optical flow models

When to avoid

• you need real-time optical flow on CPU or mobile devices
• you want a production-ready inference service rather than research code
• you lack a CUDA-capable GPU

Facets

library · maturity stable

computer-vision machine-learning deep-learning computer-vision machine-learning deep-learning python optical-flow pytorch eccv-2020 research-code pretrained-models gpu linux

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