FlowNet2 (PyTorch v0.3.0)
Pytorch implementation of FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks. Most part are from this repo, we made it as a off-the-shelf package:
- After installation, just copy the whole folder FlowNet2_src to your codebase to use. See demo.py for details.
Environment
This code has been test with Python3.6 and PyTorch0.3.0, with a Tesla K80 GPU. The system is Ubuntu 14.04.
Installation
# install custom layers
cd FlowNet2_src
bash install.sh
Note: you might need to modify here, here, and here, according to the GPU you use.
Converted Caffe Pre-trained Models
- FlowNet2[620MB]
- FlowNet2-C[149MB]
- FlowNet2-CS[297MB]
- FlowNet2-CSS[445MB]
- FlowNet2-CSS-ft-sd[445MB]
- FlowNet2-S[148MB]
- FlowNet2-SD[173MB]
Inference mode
First download pre-trained models of FlowNet2 and modify the path, then
python demo.py
If installation is sucessful, you should see the following:
Reference
If you find this implementation useful in your work, please acknowledge it appropriately and cite the paper using:
@InProceedings{IMKDB17,
author = "E. Ilg and N. Mayer and T. Saikia and M. Keuper and A. Dosovitskiy and T. Brox",
title = "FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks",
booktitle = "IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
month = "Jul",
year = "2017",
url = "http://lmb.informatik.uni-freiburg.de//Publications/2017/IMKDB17"
}
Acknowledgments
- NVIDIA/flownet2-pytorch: Most part
- hellock/flownet2-pytorch: Python3.x and PyTorch0.3.0 support