2D Semantic Segmentation task 3 (25 classes)

Various documents dataset. Each of the 65 documents includes scanned ground truth images, both hard and easy distorted photos, and document-centered cropped images. Image Source: DocUNet: Document Image Unwarping via a Stacked U-Net

CaDIS: a Cataract Dataset for Image Segmentation is a dataset for semantic segmentation created by Digital Surgery Ltd. on top of the CATARACTS dataset. CaDIS consists of 4670 images sampled from the 25 videos on CATARACTS' training set. Each pixel in each image is labeled with its respective instrument or anatomical class from a set of 36 identified classes. More details about the dataset could be found in the paper (https://arxiv.org/pdf/1906.11586.pdf).

A Simulated Benchmark for multi-modal SLAM Systems Evaluation in Large-scale Dynamic Environments.

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The datasets used in our WACV paper High-Fidelity Document Stain Removal via A Large-Scale Real-World Dataset and A Memory-Augmented Transformer. StainDoc is a newly created document stain dataset. StainDoc_mark and StainDoc_seal are synthetic datasets.