Weakly-Supervised Semantic Segmentation

Cityscapes is a large-scale database which focuses on semantic understanding of urban street scenes. It provides semantic, instance-wise, and dense pixel annotations for 30 classes grouped into 8 categories (flat surfaces, humans, vehicles, constructions, objects, nature, sky, and void). The dataset consists of around 5000 fine annotated images and 20000 coarse annotated ones. Data was captured in 50 cities during several months, daytimes, and good weather conditions. It was originally recorded as video so the frames were manually selected to have the following features: large number of dynamic objects, varying scene layout, and varying background. Source: A Review on Deep Learning Techniques Applied to Semantic Segmentation Image Source: https://www.cityscapes-dataset.com/dataset-overview/

The ADE20K semantic segmentation dataset contains more than 20K scene-centric images exhaustively annotated with pixel-level objects and object parts labels. There are totally 150 semantic categories, which include stuffs like sky, road, grass, and discrete objects like person, car, bed. Source: Cooperative Image Segmentation and Restoration in Adverse Environmental Conditions Image Source: https://groups.csail.mit.edu/vision/datasets/ADE20K/

SCC Data Set

A dataset consisting of 180,662 triplets of dual-pol synthetic aperture radar (SAR) image patches, multi-spectral Sentinel-2 image patches, and MODIS land cover maps. Source: SEN12MS -- A Curated Dataset of Georeferenced Multi-Spectral Sentinel-1/2 Imagery for Deep Learning and Data Fusion

The PASCAL-Scribble Dataset is an extension of the PASCAL dataset with scribble annotations for semantic segmentation. The annotations follow two different protocols. In the first protocol, the PASCAL VOC 2012 set is annotated, with 20 object categories (aeroplane, bicycle, ...) and one background category. There are 12,031 images annotated, including 10,582 images in the training set and 1,449 images in the validation set. In the second protocol, the 59 object/stuff categories and one background category involved in the PASCAL-CONTEXT dataset are used. Besides the 20 object categories in the first protocol, there are 39 extra categories (snow, tree, ...) included. This protocol is followed to annotate the PASCAL-CONTEXT dataset. 4,998 images in the training set have been annotated. Source: https://jifengdai.org/downloads/scribble_sup/ Image Source: https://jifengdai.org/downloads/scribble_sup/

ScribbleKITTI is a scribble-annotated dataset for LiDAR semantic segmentation.

We release expert-made scribble annotations for the medical ACDC dataset [1]. The released data must be considered as extending the original ACDC dataset. The ACDC dataset contains cardiac MRI images, paired with hand-made segmentation masks. It is possible to use the segmentation masks provided in the ACDC dataset to evaluate the performance of methods trained using only scribble supervision. References: [1] Bernard, Olivier, et al. "Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?." IEEE transactions on medical imaging 37.11 (2018): 2514-2525.

CheXlocalize is a radiologist-annotated segmentation dataset on chest X-rays. The dataset consists of two types of radiologist annotations for the localization of 10 pathologies: pixel-level segmentations and most-representative points. Annotations were drawn on images from the CheXpert validation and test sets. The dataset also consists of two separate sets of radiologist annotations: (1) ground-truth pixel-level segmentations on the validation and test sets, drawn by two board-certified radiologists, and (2) benchmark pixel-level segmentations and most-representative points on the test set, drawn by a separate group of three board-certified radiologists. The validation and test sets consist of 234 chest X-rays from 200 patients and 668 chest X-rays from 500 patients, respectively. The 10 pathologies of interest are Atelectasis, Cardiomegaly, Consolidation, Edema, Enlarged Cardiomediastinum, Lung Lesion, Lung Opacity, Pleural Effusion, Pneumothorax, and Support Devices. For more details, please see our paper, Benchmarking saliency methods for chest X-ray interpretation.