datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
uniocc
UniOcc: A Unified Benchmark for Occupancy Forecasting and Prediction in Autonomous Driving
Paper | Project Page | Code
Autonomous Driving researchers, have you ever been bothered by the fact that popular datasets all have their different
formats, and standardizing them is a pain? Have you ever been frustrated by the difficulty of just understanding
the file semantics? This challenge is even worse in the occupancy domain. But, UniOcc is here to help.
UniOcc is a unified… See the full description on the dataset page: https://huggingface.co/datasets/tasl-lab/uniocc.resultsstandard-chess-games
[!CAUTION]
This dataset is still a work in progress and some breaking changes might occur.
Lichess Rated Standard Chess Games Dataset
Dataset Description
6,771,826,271 standard rated games, played on lichess.org, updated monthly from the database dumps.
This version of the data is meant for data analysis. If you need PGN files you can find those here. That said, once you have a subset of interest, it is trivial to convert it back to PGN as shown in the Dataset Usage… See the full description on the dataset page: https://huggingface.co/datasets/Lichess/standard-chess-games.LLaVA-OneVision-1.5-Mid-Training-85M
🚀 LLaVA-One-Vision-1.5-Mid-Training-85M Dataset is being uploaded 🚀
Upload Status
All Completed: ImageNet-21k、LAIONCN、DataComp-1B、Zero250M、COYO700M、SA-1B、MINT、Obelics
📜 Cite
If you find LLaVA-One-Vision-1.5-Mid-Training-85M useful in your research, please consider to cite the following related papers:
@misc{an2025llavaonevision15fullyopenframework,
title={LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training}… See the full description on the dataset page: https://huggingface.co/datasets/mvp-lab/LLaVA-OneVision-1.5-Mid-Training-85M.latent_worker_early-a2_06nonmyopia_resultslatent_worker_early4_5prophet-mosque-library
Prophet's Mosque Library
📖 Overview
Prophet’s Mosque Library is one of the primary resources for Islamic books. It hosts more than 48,000 PDF books across over 70 categories.
In this dataset, we processed the original PDF files using Google Document AI APIs and extracted their contents into two additional formats: TXT and DOCX.
📊 Dataset Contents
The dataset includes 70,884 PDF files (spanning 23,494,042 pages) representing 48,717 Islamic books. Each book is… See the full description on the dataset page: https://huggingface.co/datasets/ieasybooks-org/prophet-mosque-library.bbh
BIG-bench Hard dataset
homepage: https://github.com/suzgunmirac/BIG-Bench-Hard
@article{suzgun2022challenging,
title={Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them},
author={Suzgun, Mirac and Scales, Nathan and Sch{\"a}rli, Nathanael and Gehrmann, Sebastian and Tay, Yi and Chung, Hyung Won and Chowdhery, Aakanksha and Le, Quoc V and Chi, Ed H and Zhou, Denny and and Wei, Jason},
journal={arXiv preprint arXiv:2210.09261},
year={2022}
}
latent_worker_early-a2_03language_table_lerobotThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.0",
"robot_type": "xarm",
"total_episodes": 442226,
"total_frames": 7045476,
"total_tasks": 127605,
"total_videos": 442226,
"total_chunks": 443,
"chunks_size": 1000,
"fps": 10,
"splits": {
"train": "0:442226"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/IPEC-COMMUNITY/language_table_lerobot.droid_lerobotThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.0",
"robot_type": "franka",
"total_episodes": 92233,
"total_frames": 27044326,
"total_tasks": 31308,
"total_videos": 276699,
"total_chunks": 93,
"chunks_size": 1000,
"fps": 15,
"splits": {
"train": "0:92233"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/IPEC-COMMUNITY/droid_lerobot.FastUMI_100k_lerobot
FastUMI-100K: Advancing Data-Driven Robotic Manipulation with a Large-Scale UMI-Style Dataset
[paper] [dataset]
## Overview
FastUMI-100K is a large-scale, high-quality UMI-style dataset designed for data-driven robotic manipulation learning. Featuring over **100K+ demonstration trajectories** across **54 diverse tasks** and hundreds of object types, the dataset provides multi-view wrist-mounted fisheye images and high-frequency end-effector states. To… See the full description on the dataset page: https://huggingface.co/datasets/IPEC-COMMUNITY/FastUMI_100k_lerobot.latent_worker_early-a2_00latent_worker_early-a2_08latent_worker_early-a2_02ngii-map-full-light
ngii-map-full-light
Light point/line extract from NGII 1/1000 topographic data for Korea.
Not for shipping into GitHub — use this Hugging Face dataset instead.
CRS
Korea_2000_Central_Belt_2010 projected meters [x, y]
Layers (per region under by_region/<region>/)
Layer
Description
C023
poles (전주/통신주)
C022
lights (가로등·보안등)
A002
roads (도로 중심선)
B001_tiny
building footprints <25 m² as centroids
B002
lines (구분/재질 라인)
Also:… See the full description on the dataset page: https://huggingface.co/datasets/SKPark1/ngii-map-full-light.latent_worker_early-a2_01MMLU-Pro
MMLU-Pro Dataset
MMLU-Pro dataset is a more robust and challenging massive multi-task understanding dataset tailored to more rigorously benchmark large language models' capabilities. This dataset contains 12K complex questions across various disciplines.
|Github | 🏆Leaderboard | 📖Paper |
🚀 What's New
[2026.03.11] Added more cutting-edge frontier models to the leaderboard, including the Claude-4.6 series, Seed2.0 series, Qwen3.5 series, and Gemini-3.1-Pro… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/MMLU-Pro.latent_worker_early-a2_04kuka_lerobotThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.0",
"robot_type": "kuka_iiwa",
"total_episodes": 209880,
"total_frames": 2455879,
"total_tasks": 1,
"total_videos": 209880,
"total_chunks": 210,
"chunks_size": 1000,
"fps": 10,
"splits": {
"train": "0:209880"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/IPEC-COMMUNITY/kuka_lerobot.latent_worker_early-a2_07gpic
GPIC: A Giant Permissive Image Corpus for Visual Generation
Keshigeyan Chandrasegaran*1,
Kyle Sargent*1,
Suchir Agarwal1,
Michael Jang1,
Michael Poli1,2,
Juan Carlos Niebles1,4,
Justin Johnson3,
Jiajun Wu1,
Li Fei-Fei1
1 Stanford University
2 Radical Numerics
3 University of Michigan
4 Salesforce… See the full description on the dataset page: https://huggingface.co/datasets/stanford-vision-lab/gpic.ptb-xlLeopard-Instruct
Leopard-Instruct
Paper | Github | Models-LLaVA | Models-Idefics2
Summaries
Leopard-Instruct is a large instruction-tuning dataset, comprising 925K instances, with 739K specifically designed for text-rich, multiimage scenarios. It's been used to train Leopard-LLaVA [checkpoint] and Leopard-Idefics2 [checkpoint].
Loading dataset
to load the dataset without automatically downloading and process the images (Please run the following codes with datasets==2.18.0)… See the full description on the dataset page: https://huggingface.co/datasets/wyu1/Leopard-Instruct.bridge_orig_lerobotThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.0",
"robot_type": "widowx",
"total_episodes": 53192,
"total_frames": 1893026,
"total_tasks": 19974,
"total_videos": 212768,
"total_chunks": 54,
"chunks_size": 1000,
"fps": 5,
"splits": {
"train": "0:53192"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/IPEC-COMMUNITY/bridge_orig_lerobot.latent_worker_early3_2lasa1m-annotate-part-12fractal20220817_data_lerobotThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.0",
"robot_type": "google_robot",
"total_episodes": 87212,
"total_frames": 3786400,
"total_tasks": 599,
"total_videos": 87212,
"total_chunks": 88,
"chunks_size": 1000,
"fps": 3,
"splits": {
"train": "0:87212"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/IPEC-COMMUNITY/fractal20220817_data_lerobot.Berkeley-Function-Calling-Leaderboard
Berkeley Function Calling Leaderboard
The Berkeley function calling leaderboard is a live leaderboard to evaluate the ability of different LLMs to call functions (also referred to as tools).
We built this dataset from our learnings to be representative of most users' function calling use-cases, for example, in agents, as a part of enterprise workflows, etc.
To this end, our evaluation dataset spans diverse categories, and across multiple languages.
Checkout the Leaderboard at… See the full description on the dataset page: https://huggingface.co/datasets/gorilla-llm/Berkeley-Function-Calling-Leaderboard.