datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
hot3d
HOT3D-Clips
This Hugging Face repository hosts HOT3D-Clips, a set of curated sub-sequences of the HOT3D dataset.
Download instructions for HOT3D-Clips and the full HOT3D dataset can be found here.
See HOT3D Toolkit for documentation of the data format and for Python utilities (for loading, undistorting fisheye images, rendering using fisheye cameras, etc.).
More details can be found in the HOT3D paper and BOP 2024 report.
wds_objectnetGenEvolve-Data-Bench
GenEvolve Data and Bench
This repository contains the open-source data release for GenEvolve:
Config
Directory
Records
Images
Purpose
sft
GenEvolve-Data-SFT/
9,000 trajectories
50,291 reference images
supervised cold-start trajectories
rl
GenEvolve-Data-RL/
3,175 prompts
3,175 GT images
self-evolution / RL training prompts
bench
GenEvolve-Bench/
594 prompts
594 GT images
held-out evaluation benchmarkAll metadata is provided in both JSONL and Parquet. The Hugging Face… See the full description on the dataset page: https://huggingface.co/datasets/MeiGen-AI/GenEvolve-Data-Bench.XLRS-Bench_visual_grounding_en
🐙GitHub
Information or evaluatation on this dataset can be found in this repo: https://github.com/AI9Stars/XLRS-Bench
📜Dataset License
Annotations of this dataset is released under a Creative Commons Attribution-NonCommercial 4.0 International License. For images from:
DOTARGB images from Google Earth and CycloMedia (for academic use only; commercial use is prohibited, and Google Earth terms of use apply).
ITCVDLicensed under CC-BY-NC-SA-4.0.
MiniFrance… See the full description on the dataset page: https://huggingface.co/datasets/initiacms/XLRS-Bench_visual_grounding_en.lab-bench
LAB-Bench
The Language Agent Biology Benchmark, or LAB-Bench, is an evaluation dataset for AI systems intended to benchmark capabilities foundational to scientific research in biology. The dataset currently consists of 8 broad categories, comprising 30 narrower subtasks, including extracting information from the scientific literature (LitQA2), retrieving information from databases (DbQA) and supplementary information (SuppQA), reasoning about scientific figures (FigQA) and tables… See the full description on the dataset page: https://huggingface.co/datasets/futurehouse/lab-bench.GA-Bench
GA-Bench Dataset: 10,000 Source-Linked Graphical Abstracts for Evaluating IMRaD Coverage
GA-Bench is a source-linked dataset of 10,000 open-access paper–graphical-abstract pairs published between 2018 and 2026. The collection spans 34 overlapping subject areas and more than 400 journals from a controlled single-publisher corpus.
Each record connects a publisher-hosted graphical abstract to its source article, bibliographic metadata, extracted full text, IMRaD-structured sections… See the full description on the dataset page: https://huggingface.co/datasets/shafayet217/GA-Bench.agent-reward-bench
AgentRewardBench
💾Code
📄Paper
🌐Website
🤗Dataset
💻Demo
🏆Leaderboard
AgentRewardBench: Evaluating Automatic Evaluations of Web Agent TrajectoriesXing Han Lù, Amirhossein Kazemnejad*, Nicholas Meade, Arkil Patel, Dongchan Shin, Alejandra Zambrano, Karolina Stańczak, Peter Shaw, Christopher J. Pal, Siva Reddy*Core Contributor
Loading dataset
You can use the huggingface_hub library to load the dataset. The dataset is available on Huggingface Hub at… See the full description on the dataset page: https://huggingface.co/datasets/McGill-NLP/agent-reward-bench.terminal-bench-2
Terminal-Bench-2.0 Beta
Welcome to Terminal-Bench-2.0! If you’re reading this you’re a member of the Terminal-Bench community that we’ve selected to get a sneak peek at the latest version of the benchmark.
Getting Started
First, clone Harbor (formerly “Sandboxes”):
git clone https://github.com/laude-institute/harbor.git
From inside the Harbor directory run:
uv sync
This will install Harbor, our new package for running agent evals.
You should now be able to run TB 2.0!… See the full description on the dataset page: https://huggingface.co/datasets/penfever/terminal-bench-2.MultiBanana-Benchmark🍌 MultiBanana: A Challenging Benchmark for Multi-Reference Text-to-Image Generation 🍌
CVPR 2026 (Main)
This repository provides the datasets for
“MultiBanana: A Challenging Benchmark for Multi-Reference Text-to-Image Generation” by Yuta Oshima, Daiki Miyake, Kohsei Matsutani, Yusuke Iwasawa, Masahiro Suzuki, Yutaka Matsuo and Hiroki Furuta
Paper Link
https://arxiv.org/abs/2511.22989
Github Repository
For the usage of this benchmark, please see Github… See the full description on the dataset page: https://huggingface.co/datasets/kohsei/MultiBanana-Benchmark.tokenizers-benchSEED-Bench
Large-scale Multi-modality Models Evaluation Suite
Accelerating the development of large-scale multi-modality models (LMMs) with lmms-eval
🏠 Homepage | 📚 Documentation | 🤗 Huggingface Datasets
This Dataset
This is a formatted version of SEED-Bench. It is used in our lmms-eval pipeline to allow for one-click evaluations of large multi-modality models.
@article{li2023seed,
title={Seed-bench: Benchmarking multimodal llms with generative comprehension}… See the full description on the dataset page: https://huggingface.co/datasets/lmms-lab-encoder/SEED-Bench.wds_imagenet_sketchPDE_Inverse_Problem_Benchmarking
PDEInvBench: A Comprehensive Dataset and Design Space Exploration of Neural Networks for PDE Inverse Problems
This is the official dataset for the paper PDEInvBench: A Comprehensive Dataset and Design Space Exploration of Neural Networks for PDE Inverse Problems.
Code: GitHub - ASK-Berkeley/PDEInvBench
Sample Usage
You can use the provided script from the codebase to batch download the data:
pip install huggingface_hub
python3 huggingface_pdeinv_download.py --dataset… See the full description on the dataset page: https://huggingface.co/datasets/DabbyOWL/PDE_Inverse_Problem_Benchmarking.ALE-Bench
ALE-Bench
Dataset Description
ALE-Bench is a benchmark for evaluating AI systems on score-based algorithmic programming contests.
This dataset is officially provided by AtCoder Inc..
Please be sure to check the "License" section below.
Please read our blog post and our paper for more details.
Related resources:
Preprint paper (arXiv)
Sakana AI Blog (English)
Sakana AI Blog (Japanese)
GitHub repository
Leaderboard
Usage
Our Python library automatically… See the full description on the dataset page: https://huggingface.co/datasets/SakanaAI/ALE-Bench.BLINK
BLINK: Multimodal Large Language Models Can See but Not Perceive
🌐 Homepage | 💻 Code | 📖 Paper | 📖 arXiv | 🔗 Eval AI
This page contains the benchmark dataset for the paper "BLINK: Multimodal Large Language Models Can See but Not Perceive"
Introduction
We introduce BLINK, a new benchmark for multimodal language models (LLMs) that focuses on core visual perception abilities not found in other evaluations. Most of the BLINK tasks can be solved by humans “within a… See the full description on the dataset page: https://huggingface.co/datasets/BLINK-Benchmark/BLINK.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.benchmark
SLM Lab
Modular Deep Reinforcement Learning framework in PyTorch.
Companion library of the book Foundations of Deep Reinforcement Learning.
Documentation · Benchmark Results
NOTE: v5.0 updates to Gymnasium, uv tooling, and modern dependencies with ARM support - see CHANGELOG.md.
Book readers: git checkout v4.1.1 for Foundations of Deep Reinforcement Learning code.
BeamRider
Breakout
KungFuMaster
MsPacman
Pong
Qbert
Seaquest
Sp.Invaders… See the full description on the dataset page: https://huggingface.co/datasets/SLM-Lab/benchmark.wds_imagenet-rGUIGuard-Bench
GUIGuard-Bench (Public Ladder)
GUIGuard-Bench is a cross-platform GUI agent benchmark for studying privacy risks and privacy-preserving execution in multimodal GUI agents.
This public-ladder release contains 121 GUI interaction trajectories (68 Android + 53 PC) for benchmark evaluation, with 26,407 region-level privacy annotations across 2,002 screenshots.
For the anonymous review version of the evaluation toolkit, see GUIGaurd-Bench-CA4F.
Dataset Summary
GUI agents… See the full description on the dataset page: https://huggingface.co/datasets/ShaofantuoshuzhengzhiSha/GUIGuard-Bench.GAIA
GAIA dataset
GAIA is a benchmark which aims at evaluating next-generation LLMs (LLMs with augmented capabilities due to added tooling, efficient prompting, access to search, etc).
We added gating to prevent bots from scraping the dataset. Please do not reshare the validation or test set in a crawlable format.
Data and leaderboard
GAIA is made of more than 450 non-trivial question with an unambiguous answer, requiring different levels of tooling and autonomy to… See the full description on the dataset page: https://huggingface.co/datasets/gaia-benchmark/GAIA.flat-pack-bench
Flat-Pack Bench 🧩
Furniture assembly as a spatio-temporal stress test for large vision-language models.
Flat-Pack Bench is a multiple-choice benchmark for evaluating fine-grained
spatio-temporal understanding in real furniture assembly videos. Each question
asks a model to reason about object parts, contact events, assembly order, final
connectivity, or part identity across time.
Project page: https://flat-pack-bench.github.io
🎯 Benchmark Tasks
The benchmark… See the full description on the dataset page: https://huggingface.co/datasets/justachetan/flat-pack-bench.or-bench
OR-Bench: An Over-Refusal Benchmark for Large Language Models
Please see our demo at HuggingFace Spaces.
Overall Plots of Model Performances
Below is the overall model performance. X axis shows the rejection rate on OR-Bench-Hard-1K and Y axis shows the rejection rate on OR-Bench-Toxic. The best aligned model should be on the top left corner of the plot where the model rejects the most number of toxic prompts and least number of safe prompts. We also plot a blue line… See the full description on the dataset page: https://huggingface.co/datasets/bench-llm/or-bench.Earth-BenchCoW-Bench
CoW-Bench
Dataset | Evaluation Code
Authors: OpenRaiser
CoW-Bench is a comprehensive benchmark for evaluating video and image generation models' understanding of Composition of World (CoW), focusing on spatial relationships, object interactions, and temporal dynamics in multi-modal content generation.
Associated Paper
This dataset is associated with the following paper:
The Trinity of Consistency as a Defining Principle for General World Models
arXiv:… See the full description on the dataset page: https://huggingface.co/datasets/OpenRaiser/CoW-Bench.vsi-bench-qa-v3-hm3d-fullFormStruct-Bench
FormStruct-Bench
Dataset Description
FormStruct-Bench is a multilingual benchmark for extracting the semantic and
spatial structure of forms from document images. The repository combines a
7,000-page main benchmark, a controlled visual-degradation set, and
template-level layout annotations. It supports evaluation of vision-language
models and document AI systems on hierarchical key-value extraction, document
structure recovery, region localization, table and… See the full description on the dataset page: https://huggingface.co/datasets/D2I-CUHK-Shenzhen/FormStruct-Bench.wds_imagenet-aCV-Bench
Cambrian Vision-Centric Benchmark (CV-Bench)
This repository contains the Cambrian Vision-Centric Benchmark (CV-Bench), introduced in Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs.
Files
The test*.parquet files contain the dataset annotations and images pre-loaded for processing with HF Datasets.
These can be loaded in 3 different configurations using… See the full description on the dataset page: https://huggingface.co/datasets/nyu-visionx/CV-Bench.Qwen-Image-Bench
Qwen-Image-Bench
A creator-centric benchmark for evaluating Text-to-Image models beyond semantic alignment.
Links
Resource
Link
📑 Paper
http://arxiv.org/abs/2605.28091
📊 Benchmark Dataset (HuggingFace)
https://huggingface.co/datasets/Qwen/Qwen-Image-Bench
📊 Benchmark Dataset (ModelScope)
https://www.modelscope.cn/datasets/Qwen/Qwen-Image-Bench
💻 GitHub
https://github.com/QwenLM/Qwen-Image-Bench
🧑⚖️ Q-Judger Model… See the full description on the dataset page: https://huggingface.co/datasets/Qwen/Qwen-Image-Bench.CTVid-Bench
CTVid-Bench
CTVid-Bench is an open-source testing benchmark for clear text video restoration. This release packages the public evaluation media for three methods (GT, blur, downsample_x4) together with the latest QA v2 annotations.
Paper: ClearText-Video: A Large-Scale Text-Centric Video Dataset Bridging Video Restoration and Scene-Text Enhancement, accepted at ECCV 2026.
Project Page · arXiv:2608.28784 · Code
Release Scope
This folder is the Hugging Face staging… See the full description on the dataset page: https://huggingface.co/datasets/jinlong17/CTVid-Bench.