Documentation Index
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View on Hugging Face
Source dataset card and downloadable files for lance-format/ade20k-lance.
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=cosineBTREE on num_objectsLABEL_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}
}
