---
license: other
license_name: custom-ade20k-dual-license
pretty_name: "ADE-20K"
task_categories:
  - image-segmentation
tags:
  - computer-vision
  - building
  - sidewalk
  - bed
  - house
  - road
  - tower
  - crosswalk
  - monument
  - pictograph
annotations_creators:
  - expert-generated
size_categories:
  - 10K<n<100K
source_datasets:
  - extended
paperswithcode_id: ade20k
configs:
  - config_name: default
    data_files:
      - split: train
        path: "train/*"
---

[![ADE-20K - annotated computer-vision dataset on Pictograph](https://storage.googleapis.com/pictograph-assets/hf/heroes/ade-20k.png?v=5)](https://pictograph.io/datasets/pictograph-research/ade-20k)

[![Open in](https://img.shields.io/badge/Open_in-Pictograph-2c5aa0)](https://pictograph.io/datasets/pictograph-research/ade-20k) ![task](https://img.shields.io/badge/task-image_segmentation-1a4480) ![images](https://img.shields.io/badge/images-22,207-555) ![license](https://img.shields.io/badge/license-Custom_(ADE20K_dual_license)-3cb44b)

**[View on Pictograph](https://pictograph.io/datasets/pictograph-research/ade-20k)** · [Pictograph Research](https://pictograph.io/org/pictograph-research) · Custom (ADE20K dual license)

## About

**ADE-20K** is a computer-vision dataset curated and annotated on [Pictograph](https://pictograph.io/datasets/pictograph-research/ade-20k). The most common detected objects are building, sidewalk, bed, house, road, tower. On Pictograph you can browse every annotated image, fork it into your own workspace in one click, export it in a dozen formats, or train a model on it directly.

## At a glance

| Metric | Value |
| :--- | ---: |
| Images | 22,207 |
| Annotations | 396,285 |
| Classes | 150 |
| Annotation types | polygon |
| Splits | train |

## Quick start

Load it in one line with the `datasets` library, then read each record's boxes and class names:

```python
from datasets import load_dataset
from PIL import ImageDraw

ds = load_dataset("pictograph/ade-20k", split="train")
example = ds[0]
image = example["image"]            # a PIL image
objects = example["objects"]        # {bbox, categories, category_names}

# draw every bounding box with its class name
draw = ImageDraw.Draw(image)
for (x, y, w, h), name in zip(objects["bbox"], objects["category_names"]):
    draw.rectangle([x, y, x + w, y + h], outline="red", width=3)
    draw.text((x, y - 12), name, fill="red")

# polygon masks live under example["segmentation"]:
#   [{"label": name, "category": idx, "points": [[x, y], ...]}, ...]
image.show()
```

Prefer a full annotation editor, one-click fork, multi-format export, and one-click training? [Open this dataset on Pictograph](https://pictograph.io/datasets/pictograph-research/ade-20k).

## Dataset structure

This dataset uses the Hugging Face `imagefolder` layout: each split directory holds the images plus a `metadata.jsonl` that links every image to its annotations by `file_name`.

| Field | Description |
| :--- | :--- |
| `file_name` | Path to the image within the split directory. |
| `objects.bbox` | Bounding boxes as `[x, y, width, height]` (pixels). |
| `objects.categories` | Integer class index per box (matches the class list below). |
| `objects.category_names` | Human class name per box. |
| `segmentation` | List of `{label, category, points}`; `points` is a polygon ring `[[x, y], ...]`. |

## Data instance

One record (bounding boxes are `[x, y, width, height]` in pixels; the class index maps into the class list below):

```python
{
  "image": <PIL.Image (RGB)>,
  "objects": {
    "bbox": [[172.0, 192.0, 249.4, 152.7]],
    "categories": [0],
    "category_names": ["wall"]
  },
  "segmentation": [
    {"label": "wall", "category": 0, "points": [[176.9, 207.2], [259.0, 274.1], ...]}
  ]
}
```

## Classes

Class index matches `objects.categories` in `metadata.jsonl`.

All 150 classes, in index order: wall, building, sky, floor, tree, ceiling, road, bed, windowpane, grass, cabinet, sidewalk, person, earth, door, table, mountain, plant, curtain, chair, car, water, painting, sofa, shelf, house, sea, mirror, rug, field, armchair, seat, fence, desk, rock, wardrobe, lamp, bathtub, railing, cushion, base, box, column, signboard, chest_of_drawers, counter, sand, sink, skyscraper, fireplace, refrigerator, grandstand, path, stairs, runway, case, pool_table, pillow, screen_door, stairway, river, bridge, bookcase, blind, coffee_table, toilet, flower, book, hill, bench, countertop, stove, palm, kitchen_island, computer, swivel_chair, boat, bar, arcade_machine, hovel, bus, towel, light, truck, tower, chandelier, awning, streetlight, booth, television_receiver, airplane, dirt_track, apparel, pole, land, bannister, escalator, ottoman, bottle, buffet, poster, stage, van, ship, fountain, conveyer_belt, canopy, washer, plaything, swimming_pool, stool, barrel, basket, waterfall, tent, bag, minibike, cradle, oven, ball, food, step, tank, trade_name, microwave, pot, animal, bicycle, lake, dishwasher, screen, blanket, sculpture, hood, sconce, vase, traffic_light, tray, ashcan, fan, pier, crt_screen, plate, monitor, bulletin_board, shower, radiator, glass, clock, flag.

## License

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.

## Source and attribution

This dataset is derived from [ADE20K](https://ade20k.csail.mit.edu/), created by Zhou et al., MIT CSAIL. We are grateful to the original authors. If you use this data, please cite the original source above.

## Citation

```bibtex
@inproceedings{zhou2017scene,
  title={Scene Parsing through ADE20K Dataset},
  author={Zhou, Bolei and Zhao, Hang and Puig, Xavier and Fidler, Sanja and Barriuso, Adela and Torralba, Antonio},
  booktitle={CVPR},
  year={2017}
}
```

---

*Published from [Pictograph](https://pictograph.io/datasets/pictograph-research/ade-20k) - annotate, train, and deploy from one API.*