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EzFlow

A modular PyTorch library for optical flow estimation using neural networks

Installation

From source (recommended)

git clone https://github.com/neu-vig/ezflow
cd ezflow/
python setup.py install

From PyPI

pip install ezflow

Models supported

Datasets supported


Results and Pre-trained checkpoints

Training Dataset Training Config ckpts Sintel Clean (training) Sintel Final(training) KITTI2015 AEPE KITTI2015 F1-all
FlyingThings3DSubset + Monkaa + Driving config download 1.90 3.35 4.75 23.41%
Training Dataset Training Config ckpts Sintel Clean (training) Sintel Final(training) KITTI2015 AEPE KITTI2015 F1-all
Chairs config download 3.41 4.94 14.84 54.23%
Chairs -> Things config download 2.93 4.48 12.47 45.89%
Kubric config download 3.57 3.96 12.11 36.35%
Training Dataset Training Config ckpts Sintel Clean (training) Sintel Final(training) KITTI2015 AEPE KITTI2015 F1-all
Chairs config download 3.5 4.73 17.81 51.76%
Chairs -> Things config download 2.06 3.43 11.04 32.68%
Kubric config download 3.08 3.31 9.83 21.94%
Training Dataset Training Config ckpts Sintel Clean (training) Sintel Final(training) KITTI2015 AEPE KITTI2015 F1-all
Chairs config download 2.23 4.56 10.45 38.93%
Chairs -> Things config download 1.66 2.75 5.01 16.87%
Kubric config download 2.12 2.54 6.01 17.35%

Additional Information

  • KITTI dataset has been evaluated with a center crop of size 1224 x 370.
  • FlowNetC and PWC-Net uses padding of size 64 for evaluating the KITTI2015 dataset.
  • RAFT and DCVNet uses padding of size 8 for evaluating the Sintel and KITTI2015 datasets.

References


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