lance-format/mnist-lance

MNIST (Lance Format)

A Lance-formatted version of the classic MNIST handwritten-digit dataset with 70,000 28×28 grayscale digits stored inline alongside CLIP image embeddings and a pre-built ANN index.

Key features

• All multimodal data (image bytes + embeddings) stored inline in the same Lance dataset — no sidecar files, no external image folders.
• Pre-computed CLIP embeddings (OpenCLIP ViT-B-32 / laion2b_s34b_b79k, 512-dim, L2-normalized) shipped with an IVF_PQ index for instant similarity search.
• BTREE index on label and BITMAP index on label_name for sub-millisecond filtering.
• Standard train/test splits, ready to use with lance.dataset(...) or datasets.load_dataset(...).

Splits

Split	Rows
train	60,000
test	10,000

Schema

Column	Type	Notes
id	int64	Row index within the split
image	large_binary	Inline PNG bytes (28×28 grayscale)
label	int32	Digit class id (0-9)
label_name	string	Human-readable class ("0".."9")
image_emb	fixed_size_list<float32, 512>	CLIP image embedding (cosine-normalized)

Pre-built indices

• IVF_PQ on image_emb — vector similarity search (metric=cosine)
• BTREE on label — fast equality / range filters
• BITMAP on label_name — fast filters on the 10 class names

Load with datasets.load_dataset

import datasets

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

Load directly with Lance (recommended)

import lance

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

Load with LanceDB

import lancedb

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

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/mnist-lance --repo-type dataset --local-dir ./mnist-lance

Then lance.dataset("./mnist-lance/data/train.lance").

Vector search example

import lance
import pyarrow as pa

ds = lance.dataset("hf://datasets/lance-format/mnist-lance/data/train.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, "nprobes": 16, "refine_factor": 30},
    columns=["id", "label", "label_name"],
).to_table().to_pylist()
print(neighbors)

Filter by class

ds = lance.dataset("hf://datasets/lance-format/mnist-lance/data/train.lance")
sevens = ds.scanner(filter="label = 7", columns=["id"], limit=10).to_table()
print(sevens)

Working with images

from pathlib import Path
import lance

ds = lance.dataset("hf://datasets/lance-format/mnist-lance/data/train.lance")
row = ds.take([0], columns=["image", "label"]).to_pylist()[0]
Path("digit_0.png").write_bytes(row["image"])
print("label =", row["label"])

Images are stored inline as PNG bytes; scanning columns like label does not pay the I/O cost of loading image bytes.

Why Lance?

• One dataset for images + embeddings + indices + metadata — no sidecar files to manage.
• On-disk vector and full-text indices live next to the data, so search works on both local copies and the Hub.
• Schema evolution lets you add new columns (fresh embeddings, augmentations, model predictions) without rewriting the data (docs).

Source & license

Converted from ylecun/mnist. MNIST is released under the MIT license. The original dataset is by Yann LeCun, Corinna Cortes, and Christopher J.C. Burges.

Citation

@article{lecun1998mnist,
  title={The MNIST database of handwritten digits},
  author={LeCun, Yann and Cortes, Corinna and Burges, CJ},
  url={http://yann.lecun.com/exdb/mnist/},
  year={1998}
}