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README.md

COCO-Stuff 10K dataset v1.1

Holger Caesar, Jasper Uijlings, Vittorio Ferrari

Overview

COCO-Stuff example annotations

Welcome to this release of the COCO-Stuff [1] dataset. COCO-Stuff augments the popular COCO [2] dataset with pixel-level stuff annotations. These annotations can be used for scene understanding tasks like semantic segmentation, object detection and image captioning.

Overview

Highlights

  • 10,000 complex images from COCO [2]
  • Dense pixel-level annotations
  • 91 thing and 91 stuff classes
  • Instance-level annotations for things from COCO [2]
  • Complex spatial context between stuff and things
  • 5 captions per image from COCO [2]

Updates

  • 06 Apr 2017: Dataset version 1.1: Modified label indices
  • 31 Mar 2017: Published annotations in JSON format
  • 09 Mar 2017: Added label hierarchy scripts
  • 08 Mar 2017: Corrections to table 2 in arXiv paper [1]
  • 10 Feb 2017: Added tools extract SLICO superpixels in annotation tool
  • 12 Dec 2016: Dataset version 1.0 and arXiv paper [1] released

Results and Future Plans

The current release of COCO-Stuff-10K publishes both the training and test annotations and users report their performance individually. We invite users to report their results to us to complement this table. In the near future we will extend COCO-Stuff to the 200K images in COCO 2015 and organize an official challenge where the test annotations will only be known to the organizers.

Method Source Class-average accuracy Global accuracy Mean IOU FW IOU
FCN [3] [1] 34.0% 52.0% 22.7% -
Deeplab (no CRF) [4] [1] 38.1% 57.8% 26.9% -
OHE + DC + FCN+ [5] [5] 45.8% 66.6% 34.3% 51.2%
W2V + DC + FCN+ [5] [5] 45.1% 66.1% 34.7% 51.0%

Dataset

Filename Description Size
cocostuff-10k-v1.1.zip COCO-Stuff dataset v. 1.1, images and annotations 2.0 GB
cocostuff-10k-v1.1.json COCO-Stuff dataset v. 1.1, annotations in JSON format (optional) 62.3 MB
cocostuff-labels.txt A list of the 1+91+91 classes in COCO-Stuff 2.3 KB
cocostuff-readme.txt This document 6.5 KB
Older files
cocostuff-10k-v1.0.zip COCO-Stuff dataset version 1.0, including images and annotations 2.6 GB

Usage

To use the COCO-Stuff dataset, please follow these steps:

  1. Download or clone this repository using git: git clone https://github.com/nightrome/cocostuff.git
  2. Open the dataset folder in your shell: cd cocostuff
  3. If you have Matlab, run the following commands:
  • Add the code folder to your Matlab path: startup();
  • Run the demo script in Matlab demo_cocoStuff();
  • The script displays an image, its thing, stuff and thing+stuff annotations, as well as the image captions.
  1. Alternatively run the following Linux commands or manually download and unpack the dataset:
  • wget --directory-prefix=downloads http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/cocostuff-10k-v1.1.zip
  • unzip downloads/cocostuff-10k-v1.1.zip -d dataset/

JSON Format

Alternatively, we also provide annotations in the COCO-style JSON format above. These are created from the .mat file annotations using this Python script. They include stuff and thing annotations. The thing annotations are copied from COCO. We encode every stuff class present in an image as a single annotation using the RLE encoding format of COCO.

Label Hierarchy

The hierarchy of labels is stored in CocoStuffClasses. To visualize it, run CocoStuffClasses.showClassHierarchyStuffThings() (also available for just stuff and just thing classes) in Matlab. The output should look similar to the following figure: COCO-Stuff label hierarchy

Label Names & Indices

To be compatible with COCO, version 1.1 of COCO-Stuff has 91 thing classes (1-91), 91 stuff classes (92-182) and 1 class "unlabeled" (0). Note that 11 of the thing classes from COCO 2015 do not have any segmentation annotations. The classes desk, door and mirror could be either stuff or things and therefore occur in both COCO and COCO-Stuff. To avoid confusion we add the suffix "-stuff" to those classes in COCO-Stuff. The full list of classes can be found here.

The older version 1.0 of COCO-Stuff had 80 thing classes (2-81), 91 stuff classes (82-172) and 1 class "unlabeled" (1).

File Format

The COCO-Stuff annotations are stored in separate .mat files per image. These files follow the same format as used by Tighe et al.. Each file contains the following fields:

  • S: The pixel-wise label map of size [height x width].
  • names: The names of the thing and stuff classes in COCO-Stuff. For more details see Label Names & Indices.
  • captions: Image captions from [2] that are annotated by 5 distinct humans on average.
  • regionMapStuff: A map of the same size as S that contains the indices for the approx. 1000 regions (superpixels) used to annotate the image.
  • regionLabelsStuff: A list of the stuff labels for each superpixel. The indices in regionMapStuff correspond to the entries in regionLabelsStuff.

Semantic Segmentation Models

Note: This section will be updated to v. 1.1 soon...

To encourage further research of stuff and things we provide the trained semantic segmentation model (see Sect. 4.4 in [1]).

DeepLab

Use the following steps to download and setup the DeepLab [4] semantic segmentation model trained on COCO-Stuff. It requires deeplab-public-ver2 and is built on Caffe:

  1. Download deeplab-public-ver2: git submodule update --init models/deeplab-public-ver2
  2. Compile and configure deeplab-public-ver2 following the author's instructions. Depending on your system setup you might have to install additional packages, but a minimum setup could look like this:
  • cd models/deeplab-public-ver2
  • cp Makefile.config.example Makefile.config
  • make
  • cd ../..
  1. Download and unzip the model:
  • wget --directory-prefix=downloads http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/cocostuff-deeplab.zip
  • unzip downloads/cocostuff-deeplab.zip -d models/deeplab-public-ver2/
  1. Configure the COCO-Stuff dataset:
  • Create a symbolic link to the images: mkdir models/deeplab-public-ver2/cocostuff/data && ln -s ../../../../dataset/images models/deeplab-public-ver2/cocostuff/data/images
  • Convert the annotations by running the Matlab script: convertAnnotationsDeeplab();
  1. Run cd models/deeplab-public-ver2 && ./run_cocostuff.sh && cd ../.. to train and test the network on COCO-Stuff.

Annotation Tool

In [1] we present a simple and efficient stuff annotation tool which was used to annotate the COCO-Stuff dataset. It uses a paintbrush tool to annotate SLICO superpixels (precomputed using the code of Achanta et al.) with stuff labels. These annotations are overlaid with the existing pixel-level thing annotations from COCO. We provide a basic version of our annotation tool:

  • Prepare the required data:
    • Specify a username in annotator/data/input/user.txt.
    • Create a list of images in annotator/data/input/imageLists/<user>.list.
    • Extract the thing annotations for all images in Matlab: extractThings().
    • Extract the superpixels for all images in Matlab: extractSLICOSuperpixels().
    • To enable or disable superpixels, thing annotations and polygon drawing, take a look at the flags at the top of CocoStuffAnnotator.m.
  • Run the annotation tool in Matlab: CocoStuffAnnotator();
    • The tool writes the .mat label files to annotator/data/output/annotations.
    • To create a .png preview of the annotations, run annotator/code/exportImages.m in Matlab. The previews will be saved to annotator/data/output/preview.

Misc

References

Licensing

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:

Contact

If you have any questions regarding this dataset, please contact us at holger-at-it-caesar.com.