shahaf1313/ProCST
Official implementation of ProCST image-to-image translation for UDA-SS.
This project helps computer vision researchers and developers improve the accuracy of semantic segmentation models when applying them to new visual environments. It takes synthetic images (like from video games) and transforms their visual style to closely resemble real-world images, such as urban street scenes. The output is a set of 'style-adapted' images that can then be used with standard unsupervised domain adaptation techniques to achieve better segmentation results in the real-world target domain.
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Use this if you need to bridge the visual gap between a simulated or synthetic dataset and a real-world target dataset for semantic segmentation tasks.
Not ideal if your image segmentation problem does not involve adapting models from a source domain to a visually different target domain, or if you are not working with semantic segmentation.
Stars
29
Forks
3
Language
Python
License
MIT
Category
Last pushed
Jan 14, 2023
Commits (30d)
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