yandex-research/ddpm-segmentation
Label-Efficient Semantic Segmentation with Diffusion Models (ICLR'2022)
This project helps anyone working with image datasets to efficiently categorize or 'segment' objects within images. You input a small number of hand-labeled images, and it outputs a model that can automatically apply detailed pixel-level labels to new, unseen images. This is ideal for researchers, analysts, or engineers who need to precisely delineate objects in visuals, such as identifying specific facial features or distinguishing between types of furniture in a room.
715 stars. No commits in the last 6 months.
Use this if you need to perform detailed object segmentation on images but have very few examples of manually labeled data available.
Not ideal if you already have a large, richly annotated dataset for your segmentation task, or if your computational resources are limited as it can be very memory intensive.
Stars
715
Forks
65
Language
Python
License
MIT
Category
Last pushed
Apr 08, 2023
Commits (30d)
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