datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
RenderedTextThis dataset has been created by Stability AI and LAION.
This dataset contains 12 million 1024x1024 images of handwritten text written on a digital 3D sheet of paper generated using Blender geometry nodes and rendered using Blender Cycles. The text has varying font size, color, and rotation, and the paper was rendered under random lighting conditions.
Note that, the first 10 million examples are in the root folder of this dataset repository and the remaining 2 million are in ./remaining (due… See the full description on the dataset page: https://huggingface.co/datasets/wendlerc/RenderedText.olmocr-pre-rendered
olmOCR-bench Pre-Rendered
Pre-rendered PNG images of the olmOCR-bench benchmark dataset, ready for zero-setup evaluation of any OCR / vision model.
What This Is
The official olmOCR benchmark requires downloading 1,403 PDFs locally and rendering each page to a PNG image before sending it to a model. Every benchmark runner in the official repo does this same rendering step internally — see olmocr/data/renderpdf.py::render_pdf_to_base64png().
This dataset eliminates that… See the full description on the dataset page: https://huggingface.co/datasets/shhdwi/olmocr-pre-rendered.rendered-wikipedia-english
Dataset Card for Team-PIXEL/rendered-wikipedia-english
Dataset Summary
This dataset contains the full English Wikipedia from February 1, 2018, rendered into images of 16x8464 resolution.
The original text dataset was built from a Wikipedia dump. Each example in the original text dataset contained the content of one full Wikipedia article with cleaning to strip markdown and unwanted sections (references, etc.). Each rendered example contains a subset of one full article.… See the full description on the dataset page: https://huggingface.co/datasets/Team-PIXEL/rendered-wikipedia-english.IconStack-48M-Rendered-Traindna_rendering_processed
DNA-Rendering-Processed Dataset
Project Page | Paper | Code | Model
To enable Diffuman4D model training, we meticulously process the DNA-Rendering dataset by recalibrating camera parameters, optimizing image color correction matrices (CCMs), predicting foreground masks, and estimating human skeletons.
To promote future research in the field of human-centric 3D/4D generation, we have open-sourced our re-annotated labels for the DNA-Rendering dataset in this repo, which includes… See the full description on the dataset page: https://huggingface.co/datasets/krahets/dna_rendering_processed.objaverse_rendering_setramanv-image-real-3d-rendersObjaverse-XL-Rigged-Animated-Renders
Objaverse-XL Rigged & Animated — Renders
Visual companion to
Linzhan/Objaverse-XL-Rigged-Animated,
which holds the 7,373 rigged-and-animated GLB assets themselves. This repository holds only what
was rendered from them: a four-view video of every animation clip, and a rest-pose grid per asset.
They live apart from the assets because they are bulky and numerous — 10,355 clip folders — while
the asset repo stays a compact 7,373 GLBs plus two tables. Nothing here is needed to use… See the full description on the dataset page: https://huggingface.co/datasets/Linzhan/Objaverse-XL-Rigged-Animated-Renders.ObjaverseXL_github_rendersrendered-bookcorpus
Dataset Card for Team-PIXEL/rendered-bookcorpus
Dataset Summary
This dataset is a version of the BookCorpus available at https://huggingface.co/datasets/bookcorpusopen with examples rendered as images with resolution 16x8464 pixels.
The original BookCorpus was introduced by Zhu et al. (2015) in Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books and contains 17868 books of various genres. The rendered BookCorpus was used… See the full description on the dataset page: https://huggingface.co/datasets/Team-PIXEL/rendered-bookcorpus.omnidocbench-render-compare
OmniDocBench Render-and-Compare
This dataset contains the rendered HTML reconstructions and comparison images produced
by a render-and-compare pipeline — a reference-free visual similarity evaluation
framework for OCR systems.
Overview
The pipeline processes each page of OmniDocBench through
a Qwen3.5-122B-A10B OCR model, renders the structured output back to a PNG via HTML
(reconstructed.png), and compares it against the original page scan (masked_original.png)
using… See the full description on the dataset page: https://huggingface.co/datasets/gt-free-ocr-metrics/omnidocbench-render-compare.rendertranscoda-rendered-row-343k-full-pipeline-v1
Transcoda Rendered Row 343k Full Pipeline v1
Full-page rendered Transcoda row dataset generated from synthetic and random-notation transcriptions.
Target contents: 343113
Accepted contents: 343027
Failed/dropped contents: 86
Renderings per accepted content: 4
Accepted images: 1372108
Source counts: {'random': 99990, 'synth': 243037}
Staging repo: cminst/transcoda-rendered-row-343k-full-pipeline-v1-shards
Each row contains one transcription and four independently rendered page… See the full description on the dataset page: https://huggingface.co/datasets/cminst/transcoda-rendered-row-343k-full-pipeline-v1.rendered-wikipedia-8x8-withTextveri-render
VeriRender Benchmark Dataset
Causal consistency verification samples for Vision-Language Models.
Layout
manifest.jsonl ← canonical index (one row per sample)
benchmark.yaml ← config used to generate this release
inconsistent/{domain}/{sample_id}/ ← corrupted evaluation samples
consistent/{domain}/{sample_id}/ ← negative controls (clean images)
Splits
Split
Description
Eval image
inconsistent
Symbolic spec is… See the full description on the dataset page: https://huggingface.co/datasets/VietMedTeam/veri-render.audiobench_rendertextdaruma-SFT-renderedText-Render-2M
Text Render 2M Dataset
A large-scale dataset containing 2 million text rendering image-text pairs for training generative models to improve text rendering performance.
Dataset Structure
image: Rendered text image in PNG format
text: Corresponding text content
file_name: Original filename
folder_id: Folder identifier
Usage
This dataset is designed for fine-tuning generative models to improve text rendering capabilities.
from datasets import load_dataset… See the full description on the dataset page: https://huggingface.co/datasets/PosterCraft/Text-Render-2M.rendered-bookcorpus-16x16rendered-wikipedia-en-8x8amex_render_pairs_v5eligible-scroll-atlas-renders
Get one mesh in about twenty seconds
curl -sO https://raw.githubusercontent.com/rodriguescarson/eligible-scroll-atlas/main/scripts/atlas.py
python atlas.py list --ink-pass # the 5 meshes that pass the pre-registered screen
python atlas.py ink PHerc0125 z10544_w020 --preview # a downsampled ink map, about 12 KB
python atlas.py get PHerc0125 z10544_w020 # the surface volume, 31 planes, plane 15 is the surface
from atlas import meshes… See the full description on the dataset page: https://huggingface.co/datasets/rodriguescarson/eligible-scroll-atlas-renders.OriAnyV2_Train_Render
Orient Anything V2 Dataset
Project Page | Paper | GitHub
Orient Anything V2 is an enhanced foundation model for unified understanding of object 3D orientation and rotation from single or paired images. This repository contains the training data (final rendering data) used for the model.
Sample Usage
Below is a snippet to run inference using the model and data logic, as found in the official GitHub repository:
import numpy as np
from PIL importImage
import torch
import… See the full description on the dataset page: https://huggingface.co/datasets/Viglong/OriAnyV2_Train_Render.omnidocbench-render-compare-parquet
OmniDocBench Render-and-Compare — Parquet Edition
Parquet-shard repackaging of
gt-free-ocr-metrics/omnidocbench-render-compare.
Overview
The pipeline processes each page of OmniDocBench
through a Qwen3.5-122B-A10B OCR model, renders the structured output back
to a PNG via HTML (reconstructed), and compares it against the original
page scan (masked_original) using reference-free visual metrics.
Five OCR extraction variants are provided, each targeting a different subset
of… See the full description on the dataset page: https://huggingface.co/datasets/gt-free-ocr-metrics/omnidocbench-render-compare-parquet.mm_rendered_textamex_render_pairs_v3china-city-render
VisNavMap City Offline Render Snapshots
This dataset contains one offline-render cache per city, derived from the
matching city-level OpenStreetMap PBF from the Hugging Face dataset
86Cao/china-city-osm-pbf.
It lets VisNavMap render
global and local map observations without parsing or querying the source PBF
during rollout.
Data provenance
The data-generation chain is:
Hugging Face: 86Cao/china-city-osm-pbf
│
│ download city-level… See the full description on the dataset page: https://huggingface.co/datasets/86Cao/china-city-render.dycheck_render_inputsobjaverse_processed_renders_and_captionsContains rendered views and captions from Objaverse XL objects. the objects are from the alignment and TRELLIS500K (over 1 Millionen processed objects) dataset. We downloaded and rendered 4 views of each object. We added TRELLIS and CAP3D Captions where available. If there were no captions we generated new captions with the large version of Florence 2. This is the base dataset we used to generate MeshFleet which is described in MeshFleet: Filtered and Annotated 3D Vehicle Dataset for Domain… See the full description on the dataset page: https://huggingface.co/datasets/DamianBoborzi/objaverse_processed_renders_and_captions.nayana-rendered