Dataset Card
ADE-20K
A public computer-vision dataset of 22,207 images with 396,285 annotations across 150 classes, annotated as polygon.
Pictograph Research · a custom license · Public · Updated 2026-08-08
Overview
This dataset holds 22,207 images annotated as polygon across 150 classes. The most common auto-detected tags are wide angle, panoramic, building, eye-level view, living room, bedroom. It is ready to fork, export in a dozen formats, or train a model from.
At a glance
| Metric | Value |
|---|---|
| Images | 22,207 |
| Annotations | 396,285 |
| Classes | 150 |
| Annotation types | polygon |
| Folders | 2 |
| Storage | Standard |
| Visibility | Public |
What's in these images
Tags auto-detected across the dataset, most common first.
| Tag | Category | Images |
|---|---|---|
| wide angle | Attributes | 4,304 |
| panoramic | Attributes | 3,688 |
| building | Objects | 3,650 |
| eye-level view | Attributes | 3,083 |
| living room | Scenes | 2,700 |
| bedroom | Scenes | 2,448 |
| sidewalk | Objects | 2,225 |
| side view | Attributes | 2,199 |
| bed | Objects | 1,935 |
| house | Objects | 1,838 |
| office | Scenes | 1,826 |
| close-up | Attributes | 1,799 |
| parking lot | Scenes | 1,761 |
| top-down view | Attributes | 1,723 |
| store | Scenes | 1,712 |
Use this dataset
from pictograph import Client, Dataset
client = Client() # reads PICTOGRAPH_API_KEY
dataset: Dataset = client.datasets.get("ADE-20K")
print(dataset.image_count, dataset.annotation_count)- Fork it into your own workspace with one click from its page.
- Pull it with the Pictograph SDK (shown above) or the CLI.
- Export to COCO, YOLO, Darwin V7, Pascal VOC, and more.
- Train a model directly from an export.
License
Released under a custom license.
Custom License Other
ADE20K uses a dual license. Images: non-commercial research and educational use only (bespoke MIT CSAIL agreement; access via request form; MIT does not hold image copyright; commercial use requires permission from MIT CSAIL). Annotations, software, and website: BSD-3-Clause, Copyright 2019 MIT, CSAIL.
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