lance-format/food101-lance

Food-101 (Lance Format)

Lance-formatted version of Food-101 — 101,000 food photographs across 101 classes — sourced from ethz/food101. Inline JPEG bytes + CLIP image embeddings + IVF_PQ.

Splits

Split	Rows
train.lance	75,750
validation.lance	25,250

Schema

Column	Type	Notes
id	int64	Row index within split
image	large_binary	Inline JPEG bytes
label	int32	Class id (0-100)
label_name	string	One of 101 dish names (apple_pie, baby_back_ribs, …)
image_emb	fixed_size_list<float32, 512>	OpenCLIP ViT-B-32 embedding (cosine-normalized)

Pre-built indices

• IVF_PQ on image_emb — metric=cosine
• BTREE on label
• BITMAP on label_name

Quick start

import lance
ds = lance.dataset("hf://datasets/lance-format/food101-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/food101-lance/data")
tbl = db.open_table("validation")
print(f"LanceDB table opened with {len(tbl)} images")

Filter by class

import lance
ds = lance.dataset("hf://datasets/lance-format/food101-lance/data/validation.lance")
sushi = ds.scanner(filter="label_name = 'sushi'", columns=["id"], limit=5).to_table()

Filter by class with LanceDB

import lancedb

db = lancedb.connect("hf://datasets/lance-format/food101-lance/data")
tbl = db.open_table("validation")
sushi = tbl.search().where("label_name = 'sushi'").select(["id"]).limit(5).to_list()

Visual similarity search

import lance, pyarrow as pa
ds = lance.dataset("hf://datasets/lance-format/food101-lance/data/validation.lance")
emb_field = ds.schema.field("image_emb")
ref = ds.take([0], columns=["image_emb", "label_name"]).to_pylist()[0]
query = pa.array([ref["image_emb"]], type=emb_field.type)
neighbors = ds.scanner(
    nearest={"column": "image_emb", "q": query[0], "k": 5, "nprobes": 16, "refine_factor": 30},
    columns=["id", "label_name"],
).to_table().to_pylist()

LanceDB visual similarity search

import lancedb

db = lancedb.connect("hf://datasets/lance-format/food101-lance/data")
tbl = db.open_table("validation")

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

results = (
    tbl.search(query_embedding)
    .metric("cosine")
    .select(["id", "label_name"])
    .limit(5)
    .to_list()
)

Source & license

Converted from ethz/food101. The Food-101 dataset is by Bossard et al. (ETH Zurich) — see the original dataset page for licensing details.

Citation

@inproceedings{bossard2014food,
  title={Food-101 -- Mining Discriminative Components with Random Forests},
  author={Bossard, Lukas and Guillaumin, Matthieu and Van Gool, Luc},
  booktitle={European Conference on Computer Vision (ECCV)},
  year={2014}
}