LarsDoorenbos/ccdm-stochastic-segmentation
Repository for "Stochastic Segmentation with Conditional Categorical Diffusion Models" (ICCV 2023)
This project helps medical professionals and autonomous driving engineers by providing a tool to generate multiple possible segmentation maps for an image, accounting for situations where there isn't a single 'correct' answer. You input an image, and it outputs several potential label maps, reflecting different plausible interpretations. It's designed for anyone who needs to understand the inherent uncertainty in image segmentation, especially in critical applications.
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Use this if you need to understand the range of possible segmentations for an image, rather than just one definitive outline, particularly in fields like medical imaging or autonomous vehicle perception.
Not ideal if your application strictly requires a single, deterministic segmentation map with no ambiguity or if you are looking for a simple, off-the-shelf image segmentation tool for non-critical tasks.
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Language
Python
License
MIT
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Last pushed
Oct 09, 2023
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