datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
svg-benchmark
Rapidata Static SVG Generation Benchmark
Built by Rapidata.
This dataset contains 1,918,367 human responses, collected with the
Rapidata Python SDK, comparing how well 42 frontier LLMs generate
static SVGs from text prompts. Each row is a head-to-head comparison between two models' renders of
the same prompt, scored by human annotators on one of three questions (Preference, Coherence, Alignment).
The SVGs are produced as raw <svg> markup by the models, rasterized to 768×768 PNGs… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/svg-benchmark.AIGC-Detection-Benchmark
AIGC Detection Benchmark Dataset
📝 Dataset Description
Dataset Summary
The AIGC Detection Benchmark Dataset is a high-quality collection of images and associated metadata designed to benchmark models for detecting and identifying the source of artificially generated content. The dataset contains a mix of real-world images and images generated by a wide array of prominent AI models, including diffusion models (like Stable Diffusion, DALL-E 2, Midjourney, ADM) and GANs… See the full description on the dataset page: https://huggingface.co/datasets/TheKernel01/AIGC-Detection-Benchmark.holisafe-bench
⚠️ CONTENT WARNING: This dataset contains potentially harmful and sensitive visual content including violence, hate speech, illegal activities, self-harm, sexual content, and other unsafe materials. Images are intended solely for safety research and evaluation purposes. Viewer discretion is strongly advised.
HoliSafe: Holistic Safety Benchmarking and Modeling for Vision-Language Model (CVPR'26 Findings)
🌐 Website | 📑 Paper
📋 HoliSafe-Bench Dataset… See the full description on the dataset page: https://huggingface.co/datasets/etri-vilab/holisafe-bench.PCF-Bench
PCF-Bench
A photonic-crystal-fiber (PCF) inverse-design benchmark for vision-language models. Each sample bundles geometric parameters, simulated mode-field images, and a four-level expert-style annotation suite supporting tasks across geometry perception, physics understanding, multimodal reasoning, inverse design, and code generation.
Note (anonymous review). This dataset card omits identifying information for double-blind review.
Note (review subset). The full source corpus is… See the full description on the dataset page: https://huggingface.co/datasets/PCF-Bench/PCF-Bench.locus-tag-bench
Locus-Tag Bench
Synthetic benchmark suite for fiducial-tag detection and camera calibration, rendered with render-tag.
Each config corresponds to one campaign (board family × resolution, or a lighting/sensor variant). Images are rendered in Blender Cycles with ground-truth geometry recovered directly from the scene — no detector-in-the-loop, no human labels.
Configs
Config
Purpose
Images
Board/Tag
Resolution
locus_v1_tag36h11_640x480
Detection (low-res)… See the full description on the dataset page: https://huggingface.co/datasets/NoeFontana/locus-tag-bench.MWS-Antifraud-Bench
MWS Antifraud Bench (Validation)
Experimental document-authenticity task for general-purpose multimodal language
models. This is the public validation part of MWS Vision Bench anti-fraud v0.1.
The dataset is released for research and model comparison. It is not a
certification tool, a production fraud-detection system, or a universal
leaderboard that is expected to be resistant to deliberate optimization.
Data
The validation split contains 209 items:
44 ai_gen;… See the full description on the dataset page: https://huggingface.co/datasets/MTSAIR/MWS-Antifraud-Bench.HUGO-Bench-Paper-Reproducibility
HUGO-Bench Paper Reproducibility
Supplementary data and reproducibility materials for the paper:
Vision Transformers for Zero-Shot Clustering of Animal Images: A Comparative Benchmarking Study - https://arxiv.org/abs/2602.03894
Hugo Markoff, Stefan Hein Bengtson, Michael Ørsted
Aalborg University, Denmark
Dataset Description
This repository contains complete experimental results, pre-computed embeddings, and execution logs from our comprehensive benchmarking study… See the full description on the dataset page: https://huggingface.co/datasets/AI-EcoNet/HUGO-Bench-Paper-Reproducibility.svg-benchmark
Rapidata Static SVG Generation Benchmark
Built by Rapidata.
This dataset contains 1,355,161 human responses, collected with the
Rapidata Python SDK, comparing how well 30 frontier LLMs generate
static SVGs from text prompts. Each row is a head-to-head comparison between two models' renders of
the same prompt, scored by human annotators on one of three questions (Preference, Coherence, Alignment).
The SVGs are produced as raw <svg> markup by the models, rasterized to 768×768 PNGs… See the full description on the dataset page: https://huggingface.co/datasets/FForty7/svg-benchmark.MFC-Bench
MFC-Bench: Multimodal Fact-Checking Benchmark
MFC-Bench is a comprehensive Multimodal Fact-Checking testbed designed to evaluate LVLMs in terms of identifying factual inconsistencies and counterfactual scenarios.
Dataset Description
From the paper: "MFC-Bench: Benchmarking Multimodal Fact-Checking with Large Vision-Language Models"
MFC-Bench encompasses a wide range of visual and textual queries, organized into three binary classification tasks:
1. Manipulation… See the full description on the dataset page: https://huggingface.co/datasets/MM-Hallu/MFC-Bench.HUGO-Bench
HUGO-Bench
Hierarchical Unsupervised Grouping of Organisms Benchmark
A comprehensive benchmark dataset for evaluating zero-shot clustering of wildlife camera trap images using Vision Transformer embeddings.
Overview
HUGO-Bench contains 139,111 expert-validated cropped images of 60 animal species (30 birds, 30 mammals), derived from 23 camera trap projects across LILA BC. The dataset enables benchmarking of Vision Transformer models for unsupervised species-level… See the full description on the dataset page: https://huggingface.co/datasets/AI-EcoNet/HUGO-Bench.Turkish-VLM-Mix-BenchmarkThis is a Turkish multimodal (image-text-text triplets) dataset consisting of Turkish translated samples from the datasets google/docci, tomg-group-umd/pixelprose, detection-datasets/coco, rafaelpadilla/coco2017, liuhaotian/LLaVA-Instruct-150K, liuhaotian/LLaVA-CC3M-Pretrain-595K, and HuggingFaceM4/FairFace.
The labels are in Turkish and the dataset is in an instruction-tuning format with separate columns for prompts and completion labels.
The original labels (except… See the full description on the dataset page: https://huggingface.co/datasets/ucsahin/Turkish-VLM-Mix-Benchmark.doc-split-benchmark
Doc-Split Benchmark
The evaluation slice for page-stream segmentation — the exact set behind the
leaderboard and the cloud-VLM
comparison. Self-contained (page images embedded), with a reference scorer so results are reproducible.
This is the benchmark, not the training corpus (which stays private).
🏆 Leaderboard: doc-split-leaderboard
🎯 Demo: doc-split-demo
🟢 Model: doc-split-mini-e5 (open weights)
🌍 OpenPSS cuts: openpss-mirror (SHORT/LONG, self-contained)… See the full description on the dataset page: https://huggingface.co/datasets/nutrientdocs/doc-split-benchmark.AVA-Bench
AVA-Bench
Training dataset for the paper AVA-Bench: Atomic Visual Ability Benchmark for Vision Foundation Models (arXiv:2506.09082) accepted in CVPR 2026.
AVA-Bench is a diagnostic benchmark for evaluating Vision Foundation Models (VFMs) through Atomic Visual Abilities (AVAs): fundamental perceptual skills such as localization, counting, OCR, spatial understanding, depth estimation, color recognition, texture recognition, and fine-grained recognition.
AVA-Bench disentangls visual… See the full description on the dataset page: https://huggingface.co/datasets/act13/AVA-Bench.document-processing-benchmark
Document Processing Benchmark
8 public document datasets (receipts, invoices, forms, bank statements,
multi-page docs, contracts) normalized into one parquet schema. Each row
has the document, ground-truth annotations, and per-row token/latency/cost
numbers from real API calls to one or more reference models. You can
read off a target's cost/latency/quality without re-running it.
from datasets import load_dataset
ds = load_dataset("thoughtworks/document-processing-benchmark"… See the full description on the dataset page: https://huggingface.co/datasets/thoughtworks/document-processing-benchmark.chest-bench-example
ChestBench Example
DICOM-VLM Framework Reference Package v0.2.0
ChestBench Example is a four-case, DICOM-native reference package for developing and validating the data architecture of a medical vision-language model (VLM) pipeline.
It is intentionally small. Its purpose is to demonstrate how medical imaging data, annotations, text, knowledge, retrieval targets, QA, evidence requirements, perturbations, and audit metadata can be represented without confusing… See the full description on the dataset page: https://huggingface.co/datasets/NeeyuHuynh/chest-bench-example.Syncred-Bench
Syncred-Bench
SynCred-Bench is a benchmark designed to evaluate synthetic credibility: AI-generated images that appear trustworthy by imitating authoritative visual forms (e.g., fake notices, credentials, news layouts) and realistic circulation traces.
The benchmark contains 600 AI-generated misinformation images across six credible-form categories and seven circulation styles. It also introduces FP450, a real-image negative set for measuring false positives in detection… See the full description on the dataset page: https://huggingface.co/datasets/thu-coai/Syncred-Bench.document-classification-benchmark
Document Classification Benchmark (open-vocab, zero-shot)
Given a document image and an arbitrary set of text labels, which one is right? A held-out, zero-shot,
open-vocabulary evaluation for document-type classification — labels are supplied at inference, not baked
into a head. Test split only; not for training. Every image is drawn from a permissively-licensed,
redistributable source.
Powers the
document-classification-leaderboard
and evaluates document-classification-v2… See the full description on the dataset page: https://huggingface.co/datasets/nutrientdocs/document-classification-benchmark.PANDA-PLUS-Bench
PANDA-PLUS-Bench
A benchmark dataset for evaluating WSI-specific feature collapse in pathology foundation models.
Dataset Description
PANDA-PLUS-Bench contains expert-annotated prostate biopsy patches from 9 whole slide images (9 unique patients) with pixel-level Gleason pattern annotations.
Dataset Summary
Patches: ~2,770 per augmentation condition
Resolution: 224×224 pixels at 20× magnification
Classes: Benign (0), GP3 (1), GP4 (2), GP5 (3)
Slides: 9 (one… See the full description on the dataset page: https://huggingface.co/datasets/dellacorte/PANDA-PLUS-Bench.WhatFontIs-Bench
WhatFontIs-Bench - A Synthetic Benchmark for Font Family Identification
A synthetic test set for font family identification: a single word, set in a known font, printed or painted on
real surfaces and in real scenes, with the exact font, the text and the position of every letter recorded for each image.
Made by WhatFontIs, the font finder that identifies fonts from images, to measure how
well a tool can find the font in a real-looking photo.
Official page:… See the full description on the dataset page: https://huggingface.co/datasets/whatfontis/WhatFontIs-Bench.design-fto-bench
PatSnap Design FTO Bench
A Bench for evaluating design patent Freedom-To-Operate (FTO) retrieval systems on cross-modal image search. Each sample provides a query product image (or design patent figure) plus the ground truth set of target design patents that constitute infringement risk, as confirmed by patent invalidation proceedings.
🐙 GitHub mirror: This dataset is also published as part of the patsnap/patent-bench monorepo, where you can find the reference metric scripts… See the full description on the dataset page: https://huggingface.co/datasets/gigzjl/design-fto-bench.ai-detector-benchmark-test-data
🎯 AI Detector Benchmark Test Dataset
A comprehensive benchmark dataset for testing AI image detection models.
📊 Dataset Summary
Total Images: 700
AI-Generated: 250 images (from 5 different generators)
Real Images: 450 images (from 9 diverse datasets)
Perfect for:
✅ Testing AI detection models
✅ Creating leaderboards
✅ Comparing model performance
✅ Benchmarking new approaches
🤖 AI Generators Included
Generator
Images
Accuracy Baseline
FLUX… See the full description on the dataset page: https://huggingface.co/datasets/Robo531/ai-detector-benchmark-test-data.benchmark
EditJudge-Bench
EditJudge-Bench is a synthetic benchmark for auditing vision-language models used as
automated judges for image-edit verification. Each row contains a source image,
an edited image, a factual edit instruction, counterfactual instructions, and
ground-truth scene parameters produced by a controlled Blender/Infinigen
generation pipeline.
This repository is an anonymous review release for a NeurIPS Evaluations and
Datasets submission.
Dataset Contents
1… See the full description on the dataset page: https://huggingface.co/datasets/EDAnonSubmission/benchmark.design-fto-bench
PatSnap Design FTO Bench
A Bench for evaluating design patent Freedom-To-Operate (FTO) retrieval systems on cross-modal image search. Each sample provides a query product image (or design patent figure) plus the ground truth set of target design patents that constitute infringement risk, as confirmed by patent invalidation proceedings.
🐙 GitHub mirror: This dataset is also published as part of the patsnap/patent-bench monorepo, where you can find the reference metric scripts… See the full description on the dataset page: https://huggingface.co/datasets/PatSnap/design-fto-bench.diabetic-retinopathy-screening-benchmark-africa
DR-Africa-Benchmark — Screening-Prevalence-Corrected, Fairness-Instrumented DR Evaluation
An evaluation benchmark for diabetic-retinopathy grading under African
screening conditions. It does not introduce new labels; it introduces
evaluation validity — per-record importance weights that reweight a
referral-skewed image set to real Sub-Saharan-Africa population prevalence, plus
synthetic subgroup metadata for fairness reporting.
Version 1.0.0 · core dr_synth 1.0.0 · part of the… See the full description on the dataset page: https://huggingface.co/datasets/macular/diabetic-retinopathy-screening-benchmark-africa.Face_Generation_Benchmark
Rapidata Human Face Generation Alignment
This T2I dataset contains over ~22'000 human responses, collected in less than 1h using the Rapidata Python API, accessible to anyone and ideal for large scale evaluation.
Evaluating 12 different image generation models on which one can generate faces more accurately.
The question that the annotators get asked is: "Which Image follows the description of the human better?"
To evaluate your own models and create leaderboard check out our… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/Face_Generation_Benchmark.doc-openvocab-benchmark
Open-Vocab Document & Figure Classification Benchmark
Given a document or figure image and an arbitrary set of text labels, which one is right? This is a
zero-shot, open-vocabulary image-classification benchmark for the document-AI setting: every image is
scored against a broad ~48-label candidate vocabulary (document types + figure/zone types), and the task
is to pick the correct label. The labels are supplied at inference — which is precisely what a fixed-label
supervised… See the full description on the dataset page: https://huggingface.co/datasets/nutrientdocs/doc-openvocab-benchmark.TFQ-Bench-Full
TFQ-Bench: A Benchmark for Evaluating Image Implication Understanding
TFQ-Bench is a rigorous evaluation benchmark designed to assess the capabilities of MLLMs in understanding visual metaphors, sarcasm, and implicit meanings via True-False Questions.
It serves as a complement to existing benchmarks like II-Bench (Multiple-Choice Question) and CII-Bench (Open-Style Question), offering a lower-bound difficulty check that tests a model's ability to verify specific propositions about… See the full description on the dataset page: https://huggingface.co/datasets/MING-ZCH/TFQ-Bench-Full.ai-detector-benchmark-test-data
🎯 AI Detector Benchmark Test Dataset
A comprehensive benchmark dataset for testing AI image detection models.
📊 Dataset Summary
Total Images: 700
AI-Generated: 250 images (from 5 different generators)
Real Images: 450 images (from 9 diverse datasets)
Perfect for:
✅ Testing AI detection models
✅ Creating leaderboards
✅ Comparing model performance
✅ Benchmarking new approaches
🤖 AI Generators Included
Generator
Images
Accuracy Baseline
FLUX… See the full description on the dataset page: https://huggingface.co/datasets/ash12321/ai-detector-benchmark-test-data.tibetan-script-classification-benchmark
Tibetan Script Classification Benchmark
Holdout benchmark for 6-class Tibetan script classification. Test split only — not used during training.
All images are BDRC manuscript page scans, balanced by subclass.
Class
Images
Subclasses
Danyig
60
DraDring: 25, DraRing: 9, Drathung: 17, Gongshabma: 3, Tsegdrig: 6
Druma
60
Dhumri: 22, DruDring: 20, DruRing: 10, Druchen: 2, Druthung: 6
Gyuyig
60
Khyuyig: 31, Tsumachug: 15, Yigchung: 14
Pedri
60
Peri: 44, Petsuk: 16… See the full description on the dataset page: https://huggingface.co/datasets/BDRC/tibetan-script-classification-benchmark.TFQ-Bench-Lite
TFQ-Bench: A Benchmark for Evaluating Image Implication Understanding
TFQ-Bench is a rigorous evaluation benchmark designed to assess the capabilities of MLLMs in understanding visual metaphors, sarcasm, and implicit meanings via True-False Questions.
It serves as a complement to existing benchmarks like II-Bench (Multiple-Choice Question) and CII-Bench (Open-Style Question), offering a lower-bound difficulty check that tests a model's ability to verify specific propositions about… See the full description on the dataset page: https://huggingface.co/datasets/MING-ZCH/TFQ-Bench-Lite.