Food-101

A Lance-formatted version of Food-101, the fine-grained dish-classification benchmark of 101,000 photos spread evenly across 101 dish classes, sourced from ethz/food101. Each row carries the inline JPEG bytes, the integer label, the human-readable…

View on Hugging Face

Source dataset card and downloadable files for lance-format/food101-lance.

A Lance-formatted version of Food-101, the fine-grained dish-classification benchmark of 101,000 photos spread evenly across 101 dish classes, sourced from ethz/food101. Each row carries the inline JPEG bytes, the integer label, the human-readable label_name, and a cosine-normalized CLIP image embedding, all available directly from the Hub at hf://datasets/lance-format/food101-lance/data.

Key features

• Inline JPEG bytes in the image column — no sidecar files, no image folders.
• Pre-computed CLIP image embeddings (image_emb, OpenCLIP ViT-B-32, 512-dim, cosine-normalized) with a bundled IVF_PQ index for similarity search.
• Both numeric and string labels (label, label_name) so filters can target either the class id or the dish name without an external mapping table.
• Scalar indices on both label columns so class-based curation is a quick predicate rather than a full scan.

Splits

Split	Rows	Notes
train.lance	75,750	Canonical Food-101 train split (750 images per class)
validation.lance	25,250	Canonical Food-101 test split (250 images per class)

Schema

Column	Type	Notes
id	int64	Row index within split (natural join key for merges)
image	large_binary	Inline JPEG bytes (256x256, quality 92)
label	int32	Class id (0–100)
label_name	string	One of 101 dish names, underscore-spaced (apple_pie, baby_back_ribs, …)
image_emb	fixed_size_list<float32, 512>	OpenCLIP ViT-B-32 image embedding (cosine-normalized)

Pre-built indices

• IVF_PQ on image_emb — vector similarity search (cosine)
• BTREE on label — fast lookup by class id
• BITMAP on label_name — fast lookup by class name

Why Lance?

1. Blazing Fast Random Access: Optimized for fetching scattered rows, making it ideal for random sampling, real-time ML serving, and interactive applications without performance degradation.
2. Native Multimodal Support: Store text, embeddings, and other data types together in a single file. Large binary objects are loaded lazily, and vectors are optimized for fast similarity search.
3. Native Index Support: Lance comes with fast, on-disk, scalable vector and FTS indexes that sit right alongside the dataset on the Hub, so you can share not only your data but also your embeddings and indexes without your users needing to recompute them.
4. Efficient Data Evolution: Add new columns and backfill data without rewriting the entire dataset. This is perfect for evolving ML features, adding new embeddings, or introducing moderation tags over time.
5. Versatile Querying: Supports combining vector similarity search, full-text search, and SQL-style filtering in a single query, accelerated by on-disk indexes.
6. Data Versioning: Every mutation commits a new version; previous versions remain intact on disk. Tags pin a snapshot by name, so retrieval systems and training runs can reproduce against an exact slice of history.

Load with datasets.load_dataset

You can load Lance datasets via the standard HuggingFace datasets interface, suitable when your pipeline already speaks Dataset / IterableDataset or you want a quick streaming sample without installing anything Lance-specific.

import datasets

hf_ds = datasets.load_dataset("lance-format/food101-lance", split="validation", streaming=True)
for row in hf_ds.take(3):
    print(row["label_name"])

Load with LanceDB

LanceDB is the embedded retrieval library built on top of the Lance format (docs), and is the interface most users interact with. It wraps the dataset as a queryable table with search and filter builders, and is the entry point used by the Search, Curate, Evolve, Train, Versioning, and Materialize-a-subset sections below.

import lancedb

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

Load with Lance

pylance is the Python binding for the Lance format and works directly with the format’s lower-level APIs. Reach for it when you want to inspect or operate on dataset internals — schema, scanner, fragments, and the list of pre-built indices.

import lance

ds = lance.dataset("hf://datasets/lance-format/food101-lance/data/validation.lance")
print(ds.count_rows(), ds.schema.names)
print(ds.list_indices())

Tip — for production use, download locally first. Streaming from the Hub works for exploration, but heavy random access and ANN search are far faster against a local copy:

hf download lance-format/food101-lance --repo-type dataset --local-dir ./food101-lance

Then point Lance or LanceDB at ./food101-lance/data.

Search

The bundled IVF_PQ index on image_emb makes approximate-nearest-neighbor search a single call. In production you would encode a query photo through the same OpenCLIP ViT-B-32 model used at ingest and pass the resulting 512-d vector to tbl.search(...). The example below uses the embedding stored in row 0 as a runnable stand-in so the snippet works without a model loaded; the first hit is expected to be the seed image itself, which is a useful sanity check on the index.

import lancedb

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

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

hits = (
    tbl.search(seed["image_emb"])
    .metric("cosine")
    .select(["id", "label_name"])
    .limit(10)
    .to_list()
)
print("seed dish:", seed["label_name"])
for r in hits:
    print(f"  {r['id']:>6}  {r['label_name']}")

Tune metric, nprobes, and refine_factor to trade recall against latency for your workload.

Curate

A typical curation pass for a fine-grained classifier combines a class-based filter with the bundled vector search to assemble a small, focused candidate set. The BITMAP index on label_name makes the predicate effectively free, and the bounded .limit(200) keeps the result small enough to inspect or hand off to a training run.

import lancedb

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

candidates = (
    tbl.search()
    .where("label_name IN ('sushi', 'sashimi', 'ramen')")
    .select(["id", "label", "label_name"])
    .limit(200)
    .to_list()
)
print(f"{len(candidates)} candidate Japanese-cuisine rows; first: {candidates[0]['label_name']}")

The result is a plain list of dictionaries, ready to inspect, persist as a manifest of ids, or feed into the Evolve and Train workflows below. The image and image_emb columns are never read by this query, so the network traffic is dominated by the small label fields rather than JPEG bytes or vectors.

Evolve

Lance stores each column independently, so a new column can be appended without rewriting the existing data. The lightest form is a SQL expression: derive the new column from columns that already exist, and Lance computes it once and persists it. The example below adds a coarse cuisine bucket and an is_target_dish flag for a focused training run, either of which can then be used directly in where clauses without recomputing the predicate on every query.

Note: Mutations require a local copy of the dataset, since the Hub mount is read-only. See the Materialize-a-subset section at the end of this card for a streaming pattern that downloads only the rows and columns you need, or use hf download to pull the full split first.

import lancedb

db = lancedb.connect("./food101-lance/data")  # local copy required for writes
tbl = db.open_table("validation")

tbl.add_columns({
    "is_target_dish": "label_name IN ('sushi', 'ramen', 'pho')",
    "is_dessert": "label_name IN ('apple_pie', 'cheesecake', 'tiramisu', 'ice_cream', 'donuts')",
})

If the values you want to attach already live in another table (offline labels, a second classifier’s predictions, human-verified taste tags), merge them in by joining on id:

import pyarrow as pa

predictions = pa.table({
    "id": pa.array([0, 1, 2]),
    "model_v2_pred": pa.array(["sushi", "sashimi", "sushi"]),
})
tbl.merge(predictions, on="id")

The original columns and indices are untouched, so existing code that does not reference the new columns continues to work unchanged. New columns become visible to every reader as soon as the operation commits. For column values that require a Python computation (e.g., running a second embedding model over the JPEG bytes), Lance provides a batch-UDF API in the underlying library — see the Lance data evolution docs for that pattern.

Train

Projection lets a training loop read only the columns each step actually needs. LanceDB tables expose this through Permutation.identity(tbl).select_columns([...]), which plugs straight into the standard torch.utils.data.DataLoader so prefetch, shuffling, and batching behave as in any PyTorch pipeline. For a from-scratch image classifier, project the JPEG bytes and the integer label; for a linear probe or reranker on top of frozen CLIP features, swap the projection to the embedding column and skip JPEG decoding entirely.

import lancedb
from lancedb.permutation import Permutation
from torch.utils.data import DataLoader

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

train_ds = Permutation.identity(tbl).select_columns(["image", "label"])
loader = DataLoader(train_ds, batch_size=128, shuffle=True, num_workers=4)

for batch in loader:
    # batch carries only the projected columns; image_emb stays on disk.
    # decode the JPEG bytes, forward, cross-entropy against `label`...
    ...

Switching feature sets is a configuration change: passing ["image_emb", "label"] to select_columns(...) on the next run reads only the cached 512-d vectors and the label, which is the right shape for a linear probe.

Versioning

Every mutation to a Lance dataset, whether it adds a column, merges predictions, or builds an index, commits a new version. Previous versions remain intact on disk. You can list versions and inspect the history directly from the Hub copy; creating new tags requires a local copy since tags are writes.

import lancedb

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

print("Current version:", tbl.version)
print("History:", tbl.list_versions())
print("Tags:", tbl.tags.list())

Once you have a local copy, tag a version for reproducibility:

local_db = lancedb.connect("./food101-lance/data")
local_tbl = local_db.open_table("validation")
local_tbl.tags.create("clip-vitb32-v1", local_tbl.version)

A tagged version can be opened by name, or any version reopened by its number, against either the Hub copy or a local one:

tbl_v1 = db.open_table("validation", version="clip-vitb32-v1")
tbl_v5 = db.open_table("validation", version=5)

Pinning supports two workflows. A retrieval system locked to clip-vitb32-v1 keeps returning stable results while the dataset evolves in parallel; newly added columns or labels do not change what the tag resolves to. A training experiment pinned to the same tag can be rerun later against the exact same images and labels, so changes in metrics reflect model changes rather than data drift. Neither workflow needs shadow copies or external manifest tracking.

Materialize a subset

Reads from the Hub are lazy, so exploratory queries only transfer the columns and row groups they touch. Mutating operations (Evolve, tag creation) need a writable backing store, and a training loop benefits from a local copy with fast random access. Both can be served by a subset of the dataset rather than the full split. The pattern is to stream a filtered query through .to_batches() into a new local table; only the projected columns and matching row groups cross the wire, and the bytes never fully materialize in Python memory.

import lancedb

remote_db = lancedb.connect("hf://datasets/lance-format/food101-lance/data")
remote_tbl = remote_db.open_table("train")

batches = (
    remote_tbl.search()
    .where("label_name IN ('sushi', 'sashimi', 'ramen', 'pho', 'dumplings')")
    .select(["id", "image", "label", "label_name", "image_emb"])
    .to_batches()
)

local_db = lancedb.connect("./food101-asian-subset")
local_db.create_table("train", batches)

The resulting ./food101-asian-subset is a first-class LanceDB database. Every snippet in the Evolve, Train, and Versioning sections above works against it by swapping hf://datasets/lance-format/food101-lance/data for ./food101-asian-subset.

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}
}