datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
3dfront_render_viewsRenderedTextThis 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.3dfront-render-viewsolmocr-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-sst2
Rendered SST-2
The Rendered SST-2 Dataset from Open AI.
Rendered SST2 is an image classification dataset used to evaluate the models capability on optical character recognition. This dataset was generated by rendering sentences in the Standford Sentiment Treebank v2 dataset.
This dataset contains two classes (positive and negative) and is divided in three splits: a train split containing 6920 images (3610 positive and 3310 negative), a validation split containing 872 images (444… See the full description on the dataset page: https://huggingface.co/datasets/nateraw/rendered-sst2.3dfront-render-diffuse3dfront_rendereligible-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.dna_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.procgen-renderformer
Procgen RenderFormer Dataset
Procedurally generated indoor scenes with ground-truth path-traced renders and
precomputed 3-slat VAE latents, built for training RenderFormer-style
neural renderers. Each sample is one scene observed from 14 camera poses along
an orbit.
Configs
Config
Scenes
Samples (scene x frame)
Notes
main
~307,000
~4.3 M
primary training set
zoom
~84,000
~1.2 M
tighter framing variant
validation
~1,000
~14 K
held-out assets, not… See the full description on the dataset page: https://huggingface.co/datasets/eternity304/procgen-renderformer.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.rendered-bookcorpus-bigramsObjaverse_2d_renders
2D Image/Depth Rendering of Objaverse Dataset
In total, the rendered split contains 167,857 objects. The object ids are in the completed_renders.txt file. After unzipping, the image/depth renders are in the following folder strunture:
# e.g.,
000-000/000074a334c541878360457c672b6c2e
├── depth.zip
├── image.zip
├── metadata.json
└── transforms_train.json
Camera Intrinsics
72 views per-object, uniformly sampled on the upper hemisphere
Image dimensions: 400×400… See the full description on the dataset page: https://huggingface.co/datasets/ShapeSplats/Objaverse_2d_renders.metrixel-character-renders
Metrixel Animated Character Renders
Multi-view renders, signed-distance-field volumes and per-view mesh tensors produced by Metrixel from a small set of rigged, animated humanoid characters.
Each character is captured from four camera angles (0°, 90°, 180°, 270°) across ~30 sampled frames of its motion clip, at 512×512. Every frame/angle carries a matching 64³ signed-distance-field volume and a per-view mesh tensor, so the geometry, the implicit surface and the image are aligned… See the full description on the dataset page: https://huggingface.co/datasets/EntVista/metrixel-character-renders.rendered-sts17
Dataset Summary
This dataset is rendered to images from STS-17. We envision the need to assess vision encoders' abilities to understand texts. A natural way will be assessing them with the STS protocols, with texts rendered into images.
Examples of Use
Load Arabic to Arabic dataset:
from datasets import load_dataset
dataset = load_dataset("Pixel-Linguist/rendered-sts17", name="ar-ar", split="test")
Load French to English dataset:
from datasets import load_dataset
dataset =… See the full description on the dataset page: https://huggingface.co/datasets/Pixel-Linguist/rendered-sts17.rendered-stsb
Dataset Summary
This dataset is rendered to images from STS-benchmark. We envision the need to assess vision encoders' abilities to understand texts. A natural way will be assessing them with the STS protocols, with texts rendered into images.
Examples of Use
Load English train Dataset:
from datasets import load_dataset
dataset = load_dataset("Pixel-Linguist/rendered-stsb", name="en", split="train")
Load Chinese dev Dataset:
from datasets import load_dataset
dataset =… See the full description on the dataset page: https://huggingface.co/datasets/Pixel-Linguist/rendered-stsb.rendered-bookcorpus-8x8renderobjaverse-1.0-renderingsaudiobench_rendertextspray-drill-renders
Spray Bottle + Drill Grasps
Functional grasp renders + perception outputs for spray bottles and drills.
Previously stored under yianW/mug-grasp-renders/spray_drill_renders/; split
out for clarity.
Layout
<object>_rot<zzz>/ — per-object-rotation case folders
Stage 1 renders: image.png, depth.npy, seg.npy,
mask_object.npy, cam_pose.npy, intrinsic_K.npy, ...
grasp_prompts.json (cases with grasps)
grasp_00/..grasp_04/ — per-grasp perception outputs
(image_grasp.png, hand… See the full description on the dataset page: https://huggingface.co/datasets/yianW/spray-drill-renders.anny-render-corpus-generated-train
anny-render-corpus-generated
Images generated by OmniGen2 from the constructed renders in
chibifire/anny-render-corpus.
Code: weftspun/anny-render-corpus, on the 6-datasource side of the hexagon.
Why this is a separate repository
These are generated synthetic, not constructed. They were sampled from a model rather than
rendered deterministically from a rig, so their labels are inferred and not true by
construction. Our working agreement requires generated data to… See the full description on the dataset page: https://huggingface.co/datasets/chibifire/anny-render-corpus-generated-train.deadline-render-simulation-20260605152225
Deadline render simulation 20260605152225
Generated by simulate-deadline-render-result.ts
This dataset mirrors public data-pack render outputs from Physicl.
gsm8k-rendered-vlm
Rendered GSM8K-VL Dataset
Rendered GSM8K-VL is a multimodal math-reasoning dataset for vision-language model evaluation.Each example links:
a GSM8K word problem (question)
the final numeric answer (answer)
cleaned chain-of-thought style reasoning (reasoning)
a rendered image path (image)
This dataset is intended for controlled experiments comparing text-only and image-based reasoning behavior.
Canonical Dataset Artifact
The official dataset release uses:… See the full description on the dataset page: https://huggingface.co/datasets/RodelaG/gsm8k-rendered-vlm.transcoda-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-wiki_en-bigramswds_renderedsst2rendered-sts13
Dataset Summary
This dataset is rendered to images from STS-13. We envision the need to assess vision encoders' abilities to understand texts. A natural way will be assessing them with the STS protocols, with texts rendered into images.
Examples of Use
Load test split:
from datasets import load_dataset
dataset = load_dataset("Pixel-Linguist/rendered-sts13", split="test")
Languages
English-only; for multilingual and cross-lingual datasets, see… See the full description on the dataset page: https://huggingface.co/datasets/mteb/rendered-sts13.ShapeNet_Renderingamex_render_pairs_v3