lance-format/ade20k-lance

ADE20K (Lance Format)

Lance-formatted version of the full ADE20K scene parsing benchmark (sourced from 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

import lance

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

Read an image with its segmentation

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

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()

Visual similarity search

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()

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. ADE20K is released under the BSD 3-Clause 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}
}