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| author | sniklaus <simon.niklaus@outlook.com> | 2017-11-27 10:43:57 -0800 |
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| committer | sniklaus <simon.niklaus@outlook.com> | 2017-11-27 10:43:57 -0800 |
| commit | c95e95774af6473f6866237b3e6f16f0d7ad225e (patch) | |
| tree | 486838b564c35f338d328d85b8e19af7cb2acaa3 /README.md | |
| parent | e73ed68e29365561b1a2dbbc9f79dd6c70c4954a (diff) | |
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Diffstat (limited to 'README.md')
| -rw-r--r-- | README.md | 2 |
1 files changed, 1 insertions, 1 deletions
@@ -11,7 +11,7 @@ For a third-party fork with video support, consider: https://github.com/dagf2101 To build the implementation and download the pre-trained networks, run `bash install.bash` and make sure that you configured the `CUDA_HOME` environment variable. After successfully completing this step, run `python run.py` to test it. Should you receive an error message regarding an invalid device function during execution, configure the utilized CUDA architecture within `install.bash` to something your graphics card supports. ## usage -To run it on your own pair of frames, use the following command. You can either select the `l1` or the `lf` model, please see our paper for more details. +To run it on your own pair of frames, use the following command. You can either select the `l1` or the `lf` model, please see our paper for more details. In short, the `l1` model should be used for quantitative evaluations and the `lf` model for qualitative comparisons. ``` python run.py --model lf --first ./images/first.png --second ./images/second.png --out ./result.png |