10th April 2017 update

You may find the official caffemodel file at: https://lmb.informatik.uni-freiburg.de/resources/software.php

This repo has been tested under Ubuntu 14.04, CUDA 8 on NVIDIA GTX1080.

For those who want to know how to read/write/visualize flow file '.flo', please refer another repo of mine: Optical Flow Toolkit

== Caffe with FlowNet ==

Release: 1.0

Date: 08.02.2016

Based on caffe (GIT hash SHA 8e8d97d6 by Jeff Donahue, 23.11.2015 04:33)

This is a release of FlowNet-S and FlowNet-C. It comes as a fork of the caffe master branch and with a trained network, as well as examples on how to use or train it.

To get started with FlowNet, first compile caffe, by configuring a

"Makefile.config" (example given in Makefile.config.example)

then make with

$ make -j 5 all tools

Go to this folder:

./flownet-release/models/flownet/

From this folder you can execute the scripts we prepared: To try out FlowNetS on a sample image pair, run

./demo_flownet.py S data/0000000-img0.ppm data/0000000-img1.ppm

You can also provide lists of files to run it on multiple image pairs. To train FlowNetS with the 8 sample images that come with this package, just run:

./train_flownet.py S

To extend it, please modify the img1_list.txt and img2_list.txt files accordingly or adapt the python script for your needs. Please use strong image augmentation techniques to obtain satisfactory results.

License and Citation

Please cite this paper in your publications if you use FlowNet for your research:

@inproceedings{DFIB15,
  author       = "A. Dosovitskiy and P. Fischer and E. Ilg and P. H{\"a}usser and C. Haz\ırba\ş and V. Golkov and P. v.d. Smagt and D. Cremers and T. Brox",
  title        = "FlowNet: Learning Optical Flow with Convolutional Networks",
  booktitle    = "IEEE International Conference on Computer Vision (ICCV)",
  month        = "Dec",
  year         = "2015",
  url          = "http://lmb.informatik.uni-freiburg.de/Publications/2015/DFIB15"
}

Caffe

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and community contributors.

Check out the project site for all the details like

• DIY Deep Learning for Vision with Caffe
• Tutorial Documentation
• BVLC reference models and the community model zoo
• Installation instructions

and step-by-step examples.

Please join the caffe-users group or gitter chat to ask questions and talk about methods and models. Framework development discussions and thorough bug reports are collected on Issues.

Happy brewing!

License and Citation

Caffe is released under the BSD 2-Clause license. The BVLC reference models are released for unrestricted use.

Please cite Caffe in your publications if it helps your research:

@article{jia2014caffe,
  Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
  Journal = {arXiv preprint arXiv:1408.5093},
  Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
  Year = {2014}
}