git clone https://github.com/neu-vig/ezflow
cd ezflow/
python setup.py install
pip install ezflow
- DICL
- DCVNet (1 checkpoint)
- FlowNetS
- FlowNetC (3 checkpoints)
- PWCNet (3 checkpoints)
- RAFT (3 checkpoints)
- VCN
- AutoFlow
- FlyingChairs
- HD1K
- KITTI
- Kubric
- MPI Sintel
- SceneFlow Monkaa
- SceneFlow Driving
- SceneFlow FlyingThings3D
- SceneFlow FlyingThings3D subset
-
DCVNet | model config | paper
| 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% |
-
FlowNetC | model config | arXiv
| 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% |
-
PWC-Net | model config | arXiv
| 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% |
-
RAFT | model config | arXiv
| 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% |
- KITTI dataset has been evaluated with a center crop of size
1224 x 370. - FlowNetC and PWC-Net uses
paddingof size64for evaluating the KITTI2015 dataset. - RAFT and DCVNet uses
paddingof size8for evaluating the Sintel and KITTI2015 datasets.
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