> ## 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.

# 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/).
LanceDB sits on top of Lance and gives you a
convenient table-style interface to query these datasets straight from the Hub:

```python theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
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](https://docs.lancedb.com/oss/v0-41-0/integrations/ai/huggingface.md).

## Image Classification

> [card] [MNIST](https://docs.lancedb.com/oss/v0-41-0/datasets/mnist.md)
>
> `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.

> [card] [CIFAR-10](https://docs.lancedb.com/oss/v0-41-0/datasets/cifar10.md)
>
> `lance-format/cifar10-lance` — A Lance-formatted version of CIFAR-10 covering 60,000 32×32 RGB images across ten balanced object classes.

> [card] [Fashion-MNIST](https://docs.lancedb.com/oss/v0-41-0/datasets/fashion-mnist.md)
>
> `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.

> [card] [Food-101](https://docs.lancedb.com/oss/v0-41-0/datasets/food101.md)
>
> `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.

> [card] [Oxford-IIIT Pet](https://docs.lancedb.com/oss/v0-41-0/datasets/oxford-pets.md)
>
> `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.

> [card] [Stanford Cars](https://docs.lancedb.com/oss/v0-41-0/datasets/stanford-cars.md)
>
> `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.

> [card] [ImageNet-1k Validation](https://docs.lancedb.com/oss/v0-41-0/datasets/imagenet-1k-val.md)
>
> `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.

> [card] [EuroSAT](https://docs.lancedb.com/oss/v0-41-0/datasets/eurosat.md)
>
> `lance-format/eurosat-lance` — A Lance-formatted version of EuroSAT, the canonical Sentinel-2 RGB land-cover benchmark, sourced from blanchon/EuroSAT\_RGB.

## OCR & Handwriting

> [card] [Handwriting OCR](https://docs.lancedb.com/oss/v0-41-0/datasets/handwriting-ocr.md)
>
> `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.

## Object Detection & Segmentation

> [card] [COCO 2017 Detection](https://docs.lancedb.com/oss/v0-41-0/datasets/coco-detection-2017.md)
>
> `lance-format/coco-detection-2017-lance` — A Lance-formatted version of the COCO 2017 object detection benchmark, sourced from detection-datasets/coco.

> [card] [Pascal VOC 2012 Segmentation](https://docs.lancedb.com/oss/v0-41-0/datasets/pascal-voc-2012-segmentation.md)
>
> `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.

> [card] [ADE20K](https://docs.lancedb.com/oss/v0-41-0/datasets/ade20k.md)
>
> `lance-format/ade20k-lance` — A Lance-formatted version of the full ADE20K scene parsing benchmark, sourced from 1aurent/ADE20K.

> [card] [KITTI 2D Detection](https://docs.lancedb.com/oss/v0-41-0/datasets/kitti-2d-detection.md)
>
> `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.

## Image Retrieval

> [card] [COCO Captions 2017](https://docs.lancedb.com/oss/v0-41-0/datasets/coco-captions-2017.md)
>
> `lance-format/coco-captions-2017-lance` — A Lance-formatted version of the COCO Captions 2017 corpus, redistributed via lmms-lab/COCO-Caption2017.

> [card] [Flickr30k](https://docs.lancedb.com/oss/v0-41-0/datasets/flickr30k.md)
>
> `lance-format/flickr30k-lance` — A Lance-formatted version of Flickr30k, redistributed via lmms-lab/flickr30k.

> [card] [LAION-1M](https://docs.lancedb.com/oss/v0-41-0/datasets/laion-1m.md)
>
> `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 hf://datasets/lance-format/laion-1m/data/train.lance.

## Visual Question Answering

> [card] [ChartQA](https://docs.lancedb.com/oss/v0-41-0/datasets/chartqa.md)
>
> `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.

> [card] [DocVQA](https://docs.lancedb.com/oss/v0-41-0/datasets/docvqa.md)
>
> `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).

> [card] [TextVQA](https://docs.lancedb.com/oss/v0-41-0/datasets/textvqa.md)
>
> `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.

> [card] [VQAv2](https://docs.lancedb.com/oss/v0-41-0/datasets/vqav2.md)
>
> `lance-format/vqav2-lance` — A Lance-formatted version of VQAv2 — open-ended visual question answering on COCO images — sourced from lmms-lab/VQAv2.

> [card] [GQA testdev-balanced](https://docs.lancedb.com/oss/v0-41-0/datasets/gqa-testdev-balanced.md)
>
> `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.

## Text QA

> [card] [SQuAD v2](https://docs.lancedb.com/oss/v0-41-0/datasets/squad-v2.md)
>
> `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 hf://datasets/lance-format/squad-v2-lance/data.

> [card] [TriviaQA](https://docs.lancedb.com/oss/v0-41-0/datasets/trivia-qa.md)
>
> `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 ready for retrieval at hf://datasets/lance-format/trivia-qa-lance/data.

> [card] [HotpotQA distractor](https://docs.lancedb.com/oss/v0-41-0/datasets/hotpotqa-distractor.md)
>
> `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 distractors).

> [card] [Natural Questions Validation](https://docs.lancedb.com/oss/v0-41-0/datasets/natural-questions-val.md)
>
> `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.

> [card] [MS MARCO v2.1](https://docs.lancedb.com/oss/v0-41-0/datasets/ms-marco-v2.md)
>
> `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.

## Text Corpora

> [card] [FineWeb-Edu](https://docs.lancedb.com/oss/v0-41-0/datasets/fineweb-edu.md)
>
> `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 hf://datasets/lance-format/fineweb-edu/data/train.lance.

## Speech

> [card] [LibriSpeech clean](https://docs.lancedb.com/oss/v0-41-0/datasets/librispeech-clean.md)
>
> `lance-format/librispeech-clean-lance` — A Lance-formatted version of the LibriSpeech ASR clean configuration, sourced from openslr/librispeech\_asr.

## Video

> [card] [OpenVid-1M](https://docs.lancedb.com/oss/v0-41-0/datasets/openvid.md)
>
> `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 hf://datasets/lance-format/openvid-lance/data/train.lance.

## Robotics

> [card] [LeRobot PushT](https://docs.lancedb.com/oss/v0-41-0/datasets/lerobot-pusht.md)
>
> `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 code.

> [card] [LeRobot X-VLA Soft-Fold](https://docs.lancedb.com/oss/v0-41-0/datasets/lerobot-xvla-soft-fold.md)
>
> `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 encoded videos.

## 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] [Upload Lance datasets to the Hugging Face Hub](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.

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


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.