README.md

metadata

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 (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())

Load with LanceDB

These tables can also be consumed by LanceDB, the multimodal lakehouse and embedded search library built on top of Lance, for simplified vector search and other queries.

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

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()

Filter with LanceDB

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

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

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. 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}
}