---
license: bsd-3-clause
task_categories:
- image-segmentation
- image-feature-extraction
language:
- en
tags:
- ade20k
- scene-parsing
- semantic-segmentation
- instance-segmentation
- lance
- clip-embeddings
pretty_name: ade20k-lance
size_categories:
- 10K<n<100K
---
# ADE20K (Lance Format)

Lance-formatted version of the full [ADE20K scene parsing benchmark](https://groups.csail.mit.edu/vision/datasets/ADE20K/) (sourced from [`1aurent/ADE20K`](https://huggingface.co/datasets/1aurent/ADE20K)) — **27,574 scene images** with semantic and instance segmentation maps, scene labels, and per-object metadata, all stored inline.

## Splits

| Split | Rows |
|-------|------|
| `train.lance`      | 25,574 |
| `validation.lance` | 2,000 |

## Schema

| Column | Type | Notes |
|---|---|---|
| `id` | `int64` | Row index within split |
| `image` | `large_binary` | Inline JPEG bytes |
| `segmentation` | `large_binary` | Inline PNG bytes — semantic segmentation map (RGB encoding per ADE20K spec) |
| `instance` | `large_binary?` | Inline PNG bytes — instance map; null if not provided |
| `filename` | `string` | ADE20K relative filename |
| `scene` | `list<string>` | Scene labels (e.g. `["bathroom"]`) |
| `object_names` | `list<string>` | Names of all annotated objects (one entry per polygon) |
| `objects_present` | `list<string>` | Deduped object names — feeds the `LABEL_LIST` index |
| `num_objects` | `int32` | Number of annotated objects |
| `image_emb` | `fixed_size_list<float32, 512>` | OpenCLIP `ViT-B-32` image embedding (cosine-normalized) |

## Pre-built indices

- `IVF_PQ` on `image_emb` — `metric=cosine`
- `BTREE` on `num_objects`
- `LABEL_LIST` on `objects_present` — supports `array_has_any` / `array_has_all`

## Quick start

```python
import lance

ds = lance.dataset("hf://datasets/lance-format/ade20k-lance/data/validation.lance")
print(ds.count_rows(), ds.schema.names, ds.list_indices())
```

## Load with LanceDB

These tables can also be consumed by [LanceDB](https://lancedb.github.io/lancedb/), the multimodal lakehouse and embedded search library built on top of Lance, for simplified vector search and other queries.

```python
import lancedb

db = lancedb.connect("hf://datasets/lance-format/ade20k-lance/data")
tbl = db.open_table("validation")
print(f"LanceDB table opened with {len(tbl)} scene images")
```

## Read an image with its segmentation

```python
import io
import lance
from PIL import Image

ds = lance.dataset("hf://datasets/lance-format/ade20k-lance/data/validation.lance")
row = ds.take([0], columns=["image", "segmentation", "scene", "objects_present"]).to_pylist()[0]

Image.open(io.BytesIO(row["image"])).save("img.jpg")
Image.open(io.BytesIO(row["segmentation"])).save("seg.png")
print("scene:", row["scene"])
print("objects:", row["objects_present"][:10])
```

## Filter by scene / objects

```python
import lance
ds = lance.dataset("hf://datasets/lance-format/ade20k-lance/data/validation.lance")

# Indoor scenes containing both a bed and a window.
rows = ds.scanner(
    filter="array_has_all(objects_present, ['bed', 'window'])",
    columns=["filename", "scene"],
    limit=10,
).to_table().to_pylist()
```

### Filter with LanceDB

```python
import lancedb

db = lancedb.connect("hf://datasets/lance-format/ade20k-lance/data")
tbl = db.open_table("validation")

rows = (
    tbl.search()
    .where("array_has_all(objects_present, ['bed', 'window'])")
    .select(["filename", "scene"])
    .limit(10)
    .to_list()
)
```

## Visual similarity search

```python
import lance
import pyarrow as pa

ds = lance.dataset("hf://datasets/lance-format/ade20k-lance/data/validation.lance")
emb_field = ds.schema.field("image_emb")
ref = ds.take([0], columns=["image_emb"]).to_pylist()[0]["image_emb"]
query = pa.array([ref], type=emb_field.type)

neighbors = ds.scanner(
    nearest={"column": "image_emb", "q": query[0], "k": 5},
    columns=["filename", "scene"],
).to_table().to_pylist()
```

### LanceDB visual similarity search

```python
import lancedb

db = lancedb.connect("hf://datasets/lance-format/ade20k-lance/data")
tbl = db.open_table("validation")

ref = tbl.search().limit(1).select(["image_emb"]).to_list()[0]
query_embedding = ref["image_emb"]

results = (
    tbl.search(query_embedding)
    .metric("cosine")
    .select(["filename", "scene"])
    .limit(5)
    .to_list()
)
```

## Why Lance?

- One dataset for images + segmentation + instance + scene + objects + embeddings + indices — no folder of paired files.
- On-disk vector and label-list indices live next to the data, so search works on local copies and on the Hub.
- Schema evolution: add columns (panoptic ids, fresh embeddings, model predictions) without rewriting the data.

## Source & license

Converted from [`1aurent/ADE20K`](https://huggingface.co/datasets/1aurent/ADE20K). ADE20K is released under the [BSD 3-Clause license](https://github.com/CSAILVision/ADE20K/blob/main/LICENSE) by the MIT CSAIL Computer Vision group.

## Citation

```
@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={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2017}
}
```