computer-vision-1
Image Segmentation
3260 papers • 126 benchmarks • 313 datasets
Image Segmentation is a computer vision task that involves dividing an image into multiple segments or regions, each of which corresponds to a different object or part of an object. The goal of image segmentation is to assign a unique label or category to each pixel in the image, so that pixels with similar attributes are grouped together.
(Image credit: Papersgraph)
Benchmarks
These leaderboards are used to track progress in image-segmentation-1
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Dataset
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Actions
MSD (Mirror Segmentation Dataset)
MSD (Mirror Segmentation Dataset)
PMD
PMD
Pascal Panoptic Parts
Libraries
iUse these libraries to find image-segmentation-1 models and implementations
20 papers 8,417
Datasets
Subtasks
Most implemented papers
U-Net: Convolutional Networks for Biomedical Image Segmentation
O. Ronneberger, T. Brox, P. Fischer•Sun May 17 2015
It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
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