datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
RECAP-Libero10-Task0-48succ-DataLLaVA-ReCap-CC12MRecap-DataComp-1B
Dataset Card for Recap-DataComp-1B
Recap-DataComp-1B is a large-scale image-text dataset that has been recaptioned using an advanced LLaVA-1.5-LLaMA3-8B model to enhance the alignment and detail of textual descriptions.
Dataset Details
Dataset Description
Our paper aims to bridge this community effort, leveraging the powerful and open-sourced LLaMA-3, a GPT-4 level LLM.
Our recaptioning pipeline is simple: first, we fine-tune a LLaMA-3-8B powered… See the full description on the dataset page: https://huggingface.co/datasets/UCSC-VLAA/Recap-DataComp-1B.vilhyra-recapture-00VoRA-Recap-GLDv2-1.4MTarsier2-Recap-585K
Dataset Card for Tarsier2-Recap-585K
Introduction
✨Tarsier2-Recap-585K✨ consists of 585K distinct video clips, lasting for 1972 hours in total, from open-source datasets (e.g. VATEX, TGIF, LSMDC, etc.) and each one with a detailed video description annotated by Tarsier2-7B, which beats GPT-4o in generating detailed and accurate video descriptions for video clips of 5~20 seconds (See the DREAM-1K Leaderboard). Experiments demonstrate its effectiveness in enhancing the… See the full description on the dataset page: https://huggingface.co/datasets/omni-research/Tarsier2-Recap-585K.Imagenet21K_RecaptionThis dataset is the entire 21K ImageNet dataset with about 13 million examples and about 19 thousand classes as strings
(for some reason it only had ~19K classes instead of 21K).
If you want an even larger set of images, I have a recaptioned CC12M and ImageNet dataset: https://huggingface.co/datasets/gmongaras/CC12M_and_Imagenet21K_Recap
The images are in PNG format. They can be decoded like in the following example
import io
from PIL import Image
Image.open(io.BytesIO(row["image"]))
where… See the full description on the dataset page: https://huggingface.co/datasets/gmongaras/Imagenet21K_Recaption.midjourney-v6-recap
Midjourney v6 Recaptioned
~1.2M Midjourney v6 images with captions from three VLMs:
llava: Original LLaVA captions from the source dataset
gemini: Gemini Flash 1.5 captions
qwen3: Qwen3 VL 8B captions
Caption coverage
llava: available for all 1,235,432 images (from original dataset)
gemini and qwen3: available for 1,017,105 images (82.3%)
Source
Based on brivangl/midjourney-v6-llava.
cc12m-recaptionedLLaVA-ReCap-CC3Mcc12m-wds-coco-recaptioned
CC12M WebDataset with COCO-style Recaptions
A large-scale image-text dataset containing 3 million images from Conceptual Captions 12M (CC12M) with COCO-style factual descriptions generated using NVIDIA Nemotron Nano 12B v2 VL.
Dataset Overview
Base Dataset: pixparse/cc12m-wds - Conceptual Captions 12M (CC12M)
Images: 3,000,000+ high-quality internet images
Recaption Model: NVIDIA Nemotron Nano 12B v2 VL
Recaption Style: COCO-style factual descriptions (20 words average)… See the full description on the dataset page: https://huggingface.co/datasets/undefined443/cc12m-wds-coco-recaptioned.CC12M_and_Imagenet21K_RecapI removed all low quality data and uploaded it here
This dataset is the entire 21K ImageNet dataset with about 13 million examples and about 19 thousand classes as strings
(for some reason it only had ~19K classes instead of 21K) as well as the entire CC12M dataset, recaptioned.
If you just want the recaptioned Imagenet dataset, I have that here
I obtained the CC12M form others. CC12M is a dataset with 12 million images created in 2021. Unfortunately the downloader provided by Google has many… See the full description on the dataset page: https://huggingface.co/datasets/gmongaras/CC12M_and_Imagenet21K_Recap.recap-datacomp-12m-wdsStable_Diffusion_3_RecaptionThis dataset is the one specified in the stable diffusion 3 paper which is composed of the ImageNet dataset and the CC12M dataset.
I used the ImageNet 2012 train/val data and captioned it as specified in the paper: "a photo of a 〈class name〉" (note all ids are 999,999,999)
CC12M is a dataset with 12 million images created in 2021. Unfortunately the downloader provided by Google has many broken links and the download takes forever.
However, some people in the community publicized the dataset.… See the full description on the dataset page: https://huggingface.co/datasets/gmongaras/Stable_Diffusion_3_Recaption.recap-t2i-evaluation-sample-2026
Recaptioned T2I Supervision Evaluation Sample
This repository is the small reviewer-inspection companion to the full anonymous caption-metadata release. The full release is hosted separately at https://huggingface.co/datasets/Anonymous1477/recap-t2i-evaluation-metadata-2026; this repository stays under the large-dataset sample threshold and gives reviewers a direct way to inspect redacted caption metadata, join structure, and selected image-conditioned audit packages.… See the full description on the dataset page: https://huggingface.co/datasets/Anonymous1477/recap-t2i-evaluation-sample-2026.Recap-OmniCAD
Recap-OmniCAD
Recap-OmniCAD is a large-scale CAD dataset introduced in the CVPR 2026 paper:
Pointer-CAD: Unifying B-Rep and Command Sequences via Pointer-based Edges & Faces SelectionDacheng Qi, Chenyu Wang, Jingwei Xu, Tianzhe Chu, Zibo Zhao, Wen Liu, Wenrui Ding, Yi Ma, Shenghua Gao
Data Extraction
If you have downloaded a set of tar.gz archives under ./data, you can extract each archive into a folder with the same name as the archive file.
For example… See the full description on the dataset page: https://huggingface.co/datasets/Snitro/Recap-OmniCAD.LLaVA-ReCap-558KPenguin-Recap-I
Penguin-Recap-I
Penguin-Recap-I publishes recap metadata only. The repository does not contain
image binaries.
Included subsets
subset
collection
local source roots
expected records
datacomp_coyo_penguin
DataComp + COYO Penguin recap
datamultimodal/IMAGE/datacomp_1b, datamultimodal/IMAGE/coyo_700m
57,618,155
sa1b_penguin
SA-1B Penguin recap
datamultimodal/IMAGE/SA-1B
9,254,501
openimages_penguin
OpenImages Penguin recap… See the full description on the dataset page: https://huggingface.co/datasets/tencent/Penguin-Recap-I.qom_recapsdfsdf
ImgEdit_recap_maskImgEdit: A Unified Image Editing Dataset and Benchmark
🌍 Introduction
ImgEdit is a large-scale, high-quality image-editing dataset comprising 1.2 million carefully curated edit pairs, which contain both novel and complex single-turn edits, as well as challenging multi-turn tasks.
To ensure the data quality, we employ a multi-stage pipeline that integrates a cutting-edge vision-language model, a detection model, a segmentation model, alongside task-specific in-painting procedures… See the full description on the dataset page: https://huggingface.co/datasets/sysuyy/ImgEdit_recap_mask.wikiart_recaptionWikiArt Dataset captioned using vikhyatk/moondream2 model with prompt : Generate a short, simple and only visually descriptive caption for this image.
recap-audio-2007danbooru_2025_recaption
内部暂存数据集 (Internal Temporary Dataset)
English
This is a temporary dataset for internal use.
It might contain:
Items being re-processed or corrected (e.g., some images requiring re-tagging using a distributed cluster, needing a convenient data source for it).
Data to supplement our internal systems (e.g., if a machine accidentally lost some images and we don't want to re-download everything).
Recent updates or experimental data not yet finalized (e.g., the image source… See the full description on the dataset page: https://huggingface.co/datasets/NebulaeWis/danbooru_2025_recaption.recap-audio-2006recap-audio-2005recap-audio-2008recap-audio-2010recap-audio-2009recap-audio-2004IN1k256-AR-buckets-bfl16latents_dc-ae-f32c32-sana-1.0_recap