Lecheng-Wang/Awesome-Segmention-Model
A unified semantic segmentation model manager integrating 14 state-of-the-art architectures (U-Net, DeepLab, ENet, PSPNet, HRNet, SegNet, RefineNet, FCN, SegFormer, SETR, UperNet, OCRNet, Mask2Former, SegNeXt).
This project helps scientists, researchers, or analysts who work with images to automatically identify and outline specific features like glaciers, medical anomalies, or road networks. You feed it a collection of images and their corresponding pixel-level labels (masks), and it trains a model that can then automatically segment new, unseen images. This is ideal for those needing to precisely delineate objects in image data for analysis or further processing.
Use this if you need to train a semantic segmentation model to accurately classify every pixel in an image into predefined categories, such as identifying different land cover types in satellite imagery or segmenting organs in medical scans.
Not ideal if your task involves classifying entire images (e.g., 'this is a cat' vs. 'this is a dog') or detecting objects with bounding boxes (e.g., drawing a box around each car) rather than pixel-level outlines.
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Python
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Last pushed
Jan 28, 2026
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