See our GitHub Repository for an overview of how to access and explore ADE20K.
Scene parsing data and part segmentation data derived from ADE20K dataset could be downloaded from MIT Scene Parsing Benchmark.
All images are fully annotated with objects and, many of the images have parts too.
Fully annotated with objects and parts
The annotated images cover the scene categories from the SUN and Places database. Here there are some examples showing the images, object segmentations, and parts segmentations:
The next visualization provides the list of objects and parts and the number of annotated instances. The tree only shows objects with more than 250 annotated instances and parts with more than 10 annotated instances.
Some classes can be both objects and parts. For instance, a "door" can be an object (in an indoor picture), or a part (when it is the "door" of a "car"). Some objects are always parts (e.g., a "leg", a "hand", ...), although, in some cases they can appear detached of the whole (e.g., a car "wheel" inside a garage), and some object are never parts (e.g., a "person", a "truck", ...). The same name class (e.g., "door") can correspond to several visual categories depending on which object it is a part of. For instance a car door is visually different from a cabinet door or a building door. However they share similar affordances. The value proportionClassIsPart(c) can be used to decide if a class behaves mostly as an object or as a part. When an object is not part of another object its segmentation mask will appear inside *_seg.png. If the class behaves as a part, then the segmentation mask will appear inside *_seg_parts.png. Correctly detecting an object requires classifying if the object is behaving as an independent object or if it is a part of another object.
If you find this dataset useful, please cite the following publication: