NVIDIA/flownet2-pytorch

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

3289 stars · 752 forks observed · 2026-08-28

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

A PyTorch implementation of FlowNet 2.0 for deep-learning-based optical flow estimation, released by NVIDIA. It provides multiple network architectures, custom CUDA layers, dataloaders, pretrained models, and multi-GPU training/inference support.

Use cases

• estimate optical flow between video frames with deep networks
• train FlowNet2 on custom optical flow datasets
• run optical flow inference with pretrained FlowNet2 weights
• benchmark optical flow models on MPI-Sintel
• run half-precision fp16 optical flow inference on GPU

When to choose

• you need a proven FlowNet 2.0 implementation with pretrained weights
• you want multi-GPU training and fp16 inference for optical flow
• you are reproducing research results on MPI-Sintel or similar benchmarks

When to avoid

• you need a modern, actively maintained optical flow model (e.g., RAFT)
• you require half-precision custom CUDA layers, which are unsupported
• you cannot pin to old PyTorch versions (0.4.x)

Facets

library · maturity maintenance

machine-learning deep-learning computer-vision image-processing computer-vision deep-learning machine-learning python optical-flow pytorch flownet2 cuda-kernels pretrained-models video-motion-estimation gpu linux

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markdown · JSON · MCP: product_card(name="NVIDIA/flownet2-pytorch")