> ## Documentation Index
> Fetch the complete documentation index at: https://lancedb-bcbb4faf-mintlify-6aacaf8d.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Datasets

> Browse Lance-format datasets ready to query directly from the Hugging Face Hub.

The [`lance-format`](https://huggingface.co/lance-format) organization on Hugging Face publishes a growing
catalog of multimodal datasets in Lance format. Each one bundles the raw data (images, audio, video, or text),
pre-computed embeddings, and on-disk vector / full-text indices as first-class columns in the same dataset —
so vector search, full-text search, and filtered scans work directly via `hf://` URIs without downloading.

This is powered under the hood by the [Lance format's native Hugging Face integration](https://lance.org/integrations/huggingface/)
(via the [`pylance`](https://pypi.org/project/pylance/) library). LanceDB sits on top of Lance and gives you a
convenient table-style interface to query these datasets straight from the Hub:

```python theme={null}
import lancedb

db = lancedb.connect("hf://datasets/lance-format/<dataset-name>/data")
tbl = db.open_table("train")

# Vector search, full-text search, or filtered scans — directly on the Hub
results = tbl.search(query).limit(10).to_list()
```

Click any card below for usage examples, schema, and pre-built indices. For a complete walkthrough of the
integration itself, see the [Hugging Face Hub integration page](/integrations/ai/huggingface).

## Image Classification

<CardGroup cols={2}>
  <Card title="MNIST" href="/datasets/mnist">
    `lance-format/mnist-lance` — A Lance-formatted version of the classic MNIST handwritten-digit dataset covering 70,000 28×28 grayscale digits across ten balanced classes. Each row carries inline PNG bytes, the digit label, the human-readable class name, and a cosine-normalized…
  </Card>

  <Card title="CIFAR-10" href="/datasets/cifar10">
    `lance-format/cifar10-lance` — A Lance-formatted version of CIFAR-10 covering 60,000 32×32 RGB images across ten balanced object classes. Each row carries inline PNG bytes, the integer label, the human-readable class name, and a cosine-normalized CLIP image embedding, all backed…
  </Card>

  <Card title="Fashion-MNIST" href="/datasets/fashion-mnist">
    `lance-format/fashion-mnist-lance` — A Lance-formatted version of Fashion-MNIST covering 70,000 28×28 grayscale clothing images across ten balanced apparel classes. Each row carries inline PNG bytes, the integer label, the human-readable class name, and a cosine-normalized CLIP image…
  </Card>

  <Card title="Food-101" href="/datasets/food101">
    `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…
  </Card>

  <Card title="Oxford-IIIT Pet" href="/datasets/oxford-pets">
    `lance-format/oxford-pets-lance` — A Lance-formatted version of the Oxford-IIIT Pet dataset — 7,390 cat and dog photos across 37 breeds — sourced from pcuenq/oxford-pets. Each row carries the inline JPEG bytes, the breed name, a species flag distinguishing cats from dogs, and a…
  </Card>

  <Card title="Stanford Cars" href="/datasets/stanford-cars">
    `lance-format/stanford-cars-lance` — A Lance-formatted version of the Stanford Cars fine-grained benchmark — 8,144 photographs across 196 make/model/year classes — sourced from Multimodal-Fatima/StanfordCars\_train. Each row carries the inline JPEG bytes, the integer class id, a…
  </Card>

  <Card title="ImageNet-1k Validation" href="/datasets/imagenet-1k-val">
    `lance-format/imagenet-1k-val-lance` — A Lance-formatted version of the canonical 50,000-image ImageNet-1k (ILSVRC2012) validation split, sourced from benjamin-paine/imagenet-1k. Each row is one image with its integer class id, a string class name, and a cosine-normalized OpenCLIP image…
  </Card>

  <Card title="EuroSAT" href="/datasets/eurosat">
    `lance-format/eurosat-lance` — A Lance-formatted version of EuroSAT, the canonical Sentinel-2 RGB land-cover benchmark, sourced from blanchon/EuroSAT\_RGB. Each row is a single 64×64 RGB tile with its integer class id, the human-readable class name, and a cosine-normalized…
  </Card>
</CardGroup>

## OCR

<CardGroup cols={2}>
  <Card title="Handwriting OCR" href="/datasets/handwriting-ocr">
    `lance-format/handwriting-ocr` — This Lance-formatted version of the Doctor's Handwritten Prescription BD dataset contains 4,680 cropped PNG images of handwritten medicine names from Bangladesh. Each row keeps the original image bytes with the medicine and generic-name labels…
  </Card>
</CardGroup>

## Object Detection & Segmentation

<CardGroup cols={2}>
  <Card title="COCO 2017 Detection" href="/datasets/coco-detection-2017">
    `lance-format/coco-detection-2017-lance` — A Lance-formatted version of the COCO 2017 object detection benchmark, sourced from detection-datasets/coco. Each row is one image with its inline JPEG bytes, the full per-image list of bounding boxes, COCO 80-class category ids and names…
  </Card>

  <Card title="Pascal VOC 2012 Segmentation" href="/datasets/pascal-voc-2012-segmentation">
    `lance-format/pascal-voc-2012-segmentation-lance` — A Lance-formatted version of the Pascal VOC 2012 semantic segmentation split, sourced from nateraw/pascal-voc-2012. Each row pairs an inline JPEG image with the per-pixel PNG segmentation mask and a cosine-normalized OpenCLIP ViT-B-32 image…
  </Card>

  <Card title="ADE20K" href="/datasets/ade20k">
    `lance-format/ade20k-lance` — A Lance-formatted version of the full ADE20K scene parsing benchmark, sourced from 1aurent/ADE20K. Each row is one scene image with its inline JPEG bytes, a per-pixel semantic segmentation map encoded as PNG bytes, an optional instance map, scene…
  </Card>

  <Card title="KITTI 2D Detection" href="/datasets/kitti-2d-detection">
    `lance-format/kitti-2d-detection-lance` — A Lance-formatted version of the KITTI 2D Object Detection benchmark, sourced from nateraw/kitti so no manual signup or download from cvlibs.net is required. Each row is a single driving frame with inline JPEG bytes, the full set of 2D and 3D…
  </Card>
</CardGroup>

## Image Retrieval

<CardGroup cols={2}>
  <Card title="COCO Captions 2017" href="/datasets/coco-captions-2017">
    `lance-format/coco-captions-2017-lance` — A Lance-formatted version of the COCO Captions 2017 corpus, redistributed via lmms-lab/COCO-Caption2017. Each row is one image with 5–7 human-written captions, a cosine-normalized CLIP image embedding, and a cosine-normalized CLIP text embedding of…
  </Card>

  <Card title="Flickr30k" href="/datasets/flickr30k">
    `lance-format/flickr30k-lance` — A Lance-formatted version of Flickr30k, redistributed via lmms-lab/flickr30k. Each row is one image with 5 human-written captions, a cosine-normalized CLIP image embedding, and a cosine-normalized CLIP text embedding of the canonical caption — all…
  </Card>

  <Card title="LAION-1M" href="/datasets/laion-1m">
    `lance-format/laion-1m` — A Lance-formatted slice of the LAION image-text corpus (\~1M rows) with inline JPEG bytes, CLIP image embeddings (img\_emb), full metadata, and a pre-built ANN index — all available directly from the Hub at…
  </Card>
</CardGroup>

## Visual Question Answering

<CardGroup cols={2}>
  <Card title="ChartQA" href="/datasets/chartqa">
    `lance-format/chartqa-lance` — A Lance-formatted version of ChartQA, a benchmark for question answering over scientific and business charts that demands a mix of logical and visual reasoning, redistributed via lmms-lab/ChartQA. Each row carries the chart image as inline JPEG…
  </Card>

  <Card title="DocVQA" href="/datasets/docvqa">
    `lance-format/docvqa-lance` — A Lance-formatted version of DocVQA, a benchmark for visual question answering over document images such as industry and government scans, multi-page reports, forms, and receipts, redistributed via lmms-lab/DocVQA (DocVQA config). Each row carries…
  </Card>

  <Card title="TextVQA" href="/datasets/textvqa">
    `lance-format/textvqa-lance` — A Lance-formatted version of TextVQA — visual question answering where the question requires reading text in the image (street signs, product labels, screen captures) — sourced from lmms-lab/textvqa. Each row carries the image bytes, the question…
  </Card>

  <Card title="VQAv2" href="/datasets/vqav2">
    `lance-format/vqav2-lance` — A Lance-formatted version of VQAv2 — open-ended visual question answering on COCO images — sourced from lmms-lab/VQAv2. Each row is one (image, question, 10 annotator answers) triple with paired CLIP image and question embeddings drawn from the…
  </Card>

  <Card title="GQA testdev-balanced" href="/datasets/gqa-testdev-balanced">
    `lance-format/gqa-testdev-balanced-lance` — A Lance-formatted version of the canonical GQA testdev\_balanced slice — 12,578 compositional VQA questions joined against the matching 398 images — sourced from lmms-lab/GQA. The original redistribution ships instructions and images as separate…
  </Card>
</CardGroup>

## Text QA

<CardGroup cols={2}>
  <Card title="SQuAD v2" href="/datasets/squad-v2">
    `lance-format/squad-v2-lance` — A Lance-formatted version of SQuAD v2 — the Stanford Question Answering Dataset with both answerable and deliberately unanswerable questions over Wikipedia passages — with MiniLM question embeddings stored inline and ready for retrieval at…
  </Card>

  <Card title="TriviaQA" href="/datasets/trivia-qa">
    `lance-format/trivia-qa-lance` — A Lance-formatted version of TriviaQA (rc.nocontext config) — a large reading-comprehension dataset of trivia questions paired with a canonical answer, accepted aliases, and entity-type metadata — with MiniLM question embeddings stored inline and…
  </Card>

  <Card title="HotpotQA distractor" href="/datasets/hotpotqa-distractor">
    `lance-format/hotpotqa-distractor-lance` — A Lance-formatted version of HotpotQA using the distractor config — multi-hop reading-comprehension questions where each answer requires combining facts from two Wikipedia paragraphs, with 10 candidate paragraphs per question (gold + 8…
  </Card>

  <Card title="Natural Questions Validation" href="/datasets/natural-questions-val">
    `lance-format/natural-questions-val-lance` — A Lance-formatted version of the Natural Questions validation split — 7,830 real Google search queries paired with the full Wikipedia article a human used to answer them, plus 1–5 annotator labels per question. MiniLM question embeddings are stored…
  </Card>

  <Card title="MS MARCO v2.1" href="/datasets/ms-marco-v2">
    `lance-format/ms-marco-v2.1-lance` — A Lance-formatted version of MS MARCO v2.1 — Microsoft's machine-reading-comprehension benchmark built from anonymized Bing query logs. Each row is one user query, the up-to-10 candidate passages Bing retrieved for it with relevance flags, and the…
  </Card>
</CardGroup>

## Text Corpora

<CardGroup cols={2}>
  <Card title="FineWeb-Edu" href="/datasets/fineweb-edu">
    `lance-format/fineweb-edu` — A Lance-formatted version of FineWeb-Edu — over 1.5 billion educational web passages with cleaned text, source metadata, language detection signals, and 384-dim text embeddings — available directly from the Hub at…
  </Card>
</CardGroup>

## Speech

<CardGroup cols={2}>
  <Card title="LibriSpeech clean" href="/datasets/librispeech-clean">
    `lance-format/librispeech-clean-lance` — A Lance-formatted version of the LibriSpeech ASR clean configuration, sourced from openslr/librispeech\_asr. Each row is one utterance with inline FLAC audio bytes, the reference transcript, a sentence-transformers embedding of that transcript, and…
  </Card>
</CardGroup>

## Video

<CardGroup cols={2}>
  <Card title="OpenVid-1M" href="/datasets/openvid">
    `lance-format/openvid-lance` — A Lance-formatted version of the OpenVid-1M corpus — 937,957 high-quality clips with inline MP4 bytes, 1024-dim video embeddings, captions, and rich per-clip quality signals — available directly from the Hub at…
  </Card>
</CardGroup>

## Robotics

<CardGroup cols={2}>
  <Card title="LeRobot PushT" href="/datasets/lerobot-pusht">
    `lance-format/lerobot-pusht-lance` — A Lance-formatted version of lerobot/pusht — the canonical PushT benchmark from the Diffusion Policy paper — packaged using the same three-table layout as lance-format/lerobot-xvla-soft-fold so consumers can flip between datasets without changing…
  </Card>

  <Card title="LeRobot X-VLA Soft-Fold" href="/datasets/lerobot-xvla-soft-fold">
    `lance-format/lerobot-xvla-soft-fold` — A Lance-formatted version of lerobot/xvla-soft-fold — a multi-camera robotics dataset from the X-VLA project — packaged as three Lance tables for efficient frame-level training, episode-level trajectory loading, and direct access to the original…
  </Card>
</CardGroup>

## Share your own dataset

Got a multimodal dataset you want to publish? Convert it to Lance and push it to the Hub!
Anyone who opens it gets vector search, full-text search, and filtered scans on the data out of the box,
without recreating the embeddings or indexes on their end.

<Card title="Upload Lance datasets to the Hugging Face Hub" icon="upload" href="https://www.lancedb.com/blog/upload-lance-datasets-to-hf-hub">
  A step-by-step walkthrough on the LanceDB blog covering CLI setup, packaging your dataset, pushing to your namespace, and writing a dataset card.
</Card>

Or browse the [latest trending Lance datasets](https://huggingface.co/datasets?format=format:lance\&sort=trending) on Hugging Face.