COCO-Stuff 164k is a dataset for instance segmentation, semantic segmentation, and object detection tasks. It is applicable or relevant across various domains.

The dataset consists of 163957 images with 4691398 labeled objects belonging to 172 different classes including other, person, tree, and other: sky-other, wall-concrete, clothes, building-other, metal, grass, wall-other, pavement, furniture-other, table, road, window-other, textile-other, chair, car, dining table, light, plastic, fence, ceiling-other, dirt, bush, clouds, paper, plant-other, and 144 more.

Images in the COCO-Stuff 164k dataset have pixel-level instance segmentation and bounding box annotations. Due to the nature of the instance segmentation task, it can be automatically transformed into a semantic segmentation task (only one mask for every class). There are 40677 (25% of the total) unlabeled images (i.e. without annotations). There are 3 splits in the dataset: train2017 (118287 images), test2017 (40670 images), and val2017 (5000 images). Every image has a textual description in caption tag. Additionally, a hierarchy of the objects is contained within the category tag. Explore it in supervisely labeling tool. The dataset was released in 2018 by the University of Edinburgh, UK and Google AI Perception.

COCO-Stuff is a derivative work of the COCO dataset. The authors of COCO do not in any form endorse this work. Different licenses apply:

• COCO images: Flickr Terms of use
• COCO annotations: Creative Commons Attribution 4.0 License
• COCO-Stuff annotations & code: Creative Commons Attribution 4.0 License

Source

If you make use of the COCO-Stuff 164k data, please cite the following reference:

@misc{caesar2018cocostuff,
  title={COCO-Stuff: Thing and Stuff Classes in Context},
  author={Holger Caesar and Jasper Uijlings and Vittorio Ferrari},
  year={2018},
  eprint={1612.03716},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

Source

Dataset COCO-Stuff 164k can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='COCO-Stuff 164k', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here:

• COCO 2017 train images (118K images) [18GB]
• COCO 2017 val images (5K images) [1GB]
• Stuff+thing PNG-style annotations on COCO 2017 trainval [669MB]
• Stuff-only COCO-style annotations on COCO 2017 trainval [554MB]
• Thing-only COCO-style annotations on COCO 2017 trainval [241MB]
• Indices, names, previews and descriptions of the classes in COCO-Stuff
• Machine readable version of the label list