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| author | StevenLiuWen <liuwen@shanghaitech.edu.cn> | 2018-03-13 03:40:16 -0400 |
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| committer | GitHub <noreply@github.com> | 2018-03-13 03:40:16 -0400 |
| commit | dcf61d73d937312c1ae55c0000c2e70a60348ee0 (patch) | |
| tree | 17c3d6efa5c250d5568891141554298784b95cb1 | |
| parent | cc1811da6fb8b9b5b8a36b58baae3378a681ffb1 (diff) | |
Delete README.md
| -rw-r--r-- | Codes/flownet2/README.md | 66 |
1 files changed, 0 insertions, 66 deletions
diff --git a/Codes/flownet2/README.md b/Codes/flownet2/README.md deleted file mode 100644 index 8647723..0000000 --- a/Codes/flownet2/README.md +++ /dev/null @@ -1,66 +0,0 @@ -## FlowNet2 (TensorFlow) - -This repo contains FlowNet2[1] for TensorFlow. It includes FlowNetC, S, CS, CSS, CSS-ft-sd, SD, and 2. - -### Installation -``` -pip install enum -pip install pypng -pip install matplotlib -pip install image -pip install scipy -pip install numpy -pip install tensorflow -``` - -Linux: -`sudo apt-get install python-tk` - -You must have CUDA installed: -`make all` - -### Download weights -To download the weights for all models (4.4GB), run the `download.sh` script in the `checkpoints` directory. All test scripts rely on these checkpoints to work properly. - - -### Flow Generation (1 image pair) - -``` -python -m src.flownet2.test --input_a data/samples/0img0.ppm --input_b data/samples/0img1.ppm --out ./ -``` - -Available models: -* `flownet2` -* `flownet_s` -* `flownet_c` -* `flownet_cs` -* `flownet_css` (can edit test.py to use css-ft-sd weights) -* `flownet_sd` - -If installation is successful, you should predict the following flow from samples/0img0.ppm: - - -### Training -If you would like to train any of the networks from scratch (replace `flownet2` with the appropriate model): -``` -python -m src.flownet2.train -``` -For stacked networks, previous network weights will be loaded and fixed. For example, if training CS, the C weights are loaded and fixed and the S weights are randomly initialized. - - -### Fine-tuning -TODO - -### Benchmarks -Benchmarks are for a forward pass with each model of two 512x384 images. All benchmarks were tested with a K80 GPU and Intel Xeon CPU E5-2682 v4 @ 2.30GHz. Code was executed with TensorFlow-1.2.1 and python 2.7.12 on Ubuntu 16.04. Resulting times were averaged over 10 runs. The first run is always slower as it sets up the Tensorflow Session. - -| | S | C | CS | CSS | SD | 2 -| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | -| First Run | 681.039ms | 898.792ms | 998.584ms | 1063.357ms | 933.806ms | 1882.003ms | -| Subsequent Runs | 38.067ms | 78.789ms | 123.300ms | 161.186ms | 62.061ms | 276.641ms | - - -### Sources -[1] E. Ilg, N. Mayer, T. Saikia, M. Keuper, A. Dosovitskiy, T. Brox -FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks, -IEEE Conference in Computer Vision and Pattern Recognition (CVPR), 2017. |