Haochen-Wang409/U2PL
[CVPR'22 & IJCV'24] Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels & Using Unreliable Pseudo-Labels for Label-Efficient Semantic Segmentation
This project helps computer vision engineers and researchers create more accurate pixel-level classifications for images, even when they have a limited amount of precisely labeled data. It takes in a mix of fully labeled images and many more unlabeled images. The output is a more robust image segmentation model that can accurately identify and delineate objects or regions within new images.
474 stars. No commits in the last 6 months.
Use this if you need to perform detailed object segmentation in images but face challenges with the high cost and time required for manual pixel-by-pixel labeling of large datasets.
Not ideal if your primary need is object detection (bounding boxes) or image classification (whole image labels) rather than fine-grained pixel segmentation.
Stars
474
Forks
59
Language
Python
License
Apache-2.0
Category
Last pushed
Aug 20, 2024
Commits (30d)
0
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