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README.md

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@@ -74,12 +74,15 @@ bash train_with_sd.sh
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### Evaluate
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```
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python test.py -cfg configs/deeplabv2_r101_tgt_self_distill.yaml resume g2c_tgt_self_distill_r101_model_iter018000.pth
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python test.py -cfg configs/deeplabv2_r101_tgt_self_distill.yaml resume g2c_sd.pth
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```
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Our pretrained model is available via [polybox](https://polybox.ethz.ch/index.php/s/jzckTds5efxbn3n).
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#### Tip: For those who are interested in how performance change during the process of adversarial training, test.py also accepts directory as the input and the results will be stored in a csv file.
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### Pretrained weights
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Our pretrained models for Synthia -> CityScapes task(s2c) and GTA5 -> CityScapes task(g2c) are available via [Google Drive](https://drive.google.com/drive/folders/1M7mwfSX3fx4W9KUevZwdmCo4JISnfCI_).
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### Visualization results
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![Visualization](gifs/output.gif)

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