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@@ -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