> ## Documentation Index
> Fetch the complete documentation index at: https://docs.lancedb.com/llms.txt
> Use this file to discover all available pages before exploring further.
# 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…
Source dataset card and downloadable files for `lance-format/food101-lance`.
A Lance-formatted version of [Food-101](https://www.kaggle.com/datasets/dansbecker/food-101), the fine-grained dish-classification benchmark of 101,000 photos spread evenly across 101 dish classes, sourced from [`ethz/food101`](https://huggingface.co/datasets/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` | 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.
```python theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
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](https://lancedb.com/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.
```python theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
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.
```python theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
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:
>
> ```bash theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
> 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.
```python theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
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.
```python theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
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 `id`s, 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.
```python theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
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`:
```python theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
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](https://lance.org/guide/data_evolution/) 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.
```python theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
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.
```python theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
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:
```python theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
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:
```python theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
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.
```python theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
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`](https://huggingface.co/datasets/ethz/food101). The Food-101 dataset is by Bossard et al. (ETH Zurich) — see the [original dataset page](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/) 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}
}
```