ADE20k (v2017)#

Format explanation#

The original ADE20K 2017 dataset is available here.

The consistency set (for checking the annotation consistency) is available here.

Supported annotation types:

Masks

Supported annotation attributes:

occluded (boolean): whether the object is occluded by another object
other arbitrary boolean attributes, which can be specified in the annotation file <image_name>_atr.txt

Import ADE20K 2017 dataset#

A Datumaro project with an ADE20k source can be created in the following way:

datum project create
datum project import --format ade20k2017 <path/to/dataset>

It is also possible to import the dataset using Python API:

import datumaro as dm

ade20k_dataset = dm.Dataset.import_from('<path/to/dataset>', 'ade20k2017')

ADE20K dataset directory should have the following structure:

dataset/
├── dataset_meta.json # a list of non-format labels (optional)
├── subset1/
│   └── super_label_1/
│       ├── img1.jpg
│       ├── img1_atr.txt
│       ├── img1_parts_1.png
│       ├── img1_seg.png
│       ├── img2.jpg
│       ├── img2_atr.txt
│       └── ...
└── subset2/
    ├── img3.jpg
    ├── img3_atr.txt
    ├── img3_parts_1.png
    ├── img3_parts_2.png
    ├── img4.jpg
    ├── img4_atr.txt
    ├── img4_seg.png
    └── ...

The mask images <image_name>_seg.png contain information about the object class segmentation masks and also separate each class into instances. The channels R and G encode the objects class masks. The channel B encodes the instance object masks.

The mask images <image_name>_parts_N.png contain segmentation masks for parts of objects, where N is a number indicating the level in the part hierarchy.

The annotation files <image_name>_atr.txt describe the content of each image. Each line in the text file contains:

column 1: instance number,
column 2: part level (0 for objects),
column 3: occluded (1 for true),
column 4: original raw name (might provide a more detailed categorization),
column 5: class name (parsed using wordnet),
column 6: double-quoted list of attributes, separated by commas. Each column is separated by a #. See example of dataset here.

To add custom classes, you can use dataset_meta.json.

Export to other formats#

Datumaro can convert an ADE20K dataset into any other format Datumaro supports. To get the expected result, convert the dataset to a format that supports segmentation masks.

There are several ways to convert an ADE20k 2017 dataset to other dataset formats using CLI:

datum project create
datum project import -f ade20k2017 <path/to/dataset>
datum project export -f coco -o <output/dir> -- --save-media

datum convert -if ade20k2017 -i <path/to/dataset> \
    -f coco -o <output/dir> -- --save-media

Or, using Python API:

import datumaro as dm

dataset = dm.Dataset.import_from('<path/to/dataset>', 'ade202017')
dataset.export('save_dir', 'coco')

Examples#

Examples of using this format from the code can be found in the format tests