datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
RoboDojobenchmark-bcplusDL3DV-Benchmark
DL3DV Benchmark Download Instructions
This repo contains 140 scenes in the DL3DV-benchmark, which are sampled from DL3DV-10K. The repo includes a README, License, colmaps/images (compatible to nerfstudio and 3D gaussian splatting), scene labels and the performances of methods reported in the paper (ZipNeRF, 3DGS, MipNeRF-360, nerfacto, Instant-NGP). The benchmark preview page can be found here https://dl3dv-10k.github.io/DL3DV-Benchmark-Preview/.
Download
As the whole… See the full description on the dataset page: https://huggingface.co/datasets/DL3DV/DL3DV-Benchmark.wds_objectnethot3d
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.
transformersRoadmapBench
RoadmapBench
A benchmark for evaluating AI coding agents on multi-target, long-horizon software development tasks derived from open-source project version upgrades.
Overview
RoadmapBench contains 115 tasks spanning 17 open-source repositories across 5 programming languages (Python, TypeScript, Go, Rust, C++). Each task requires an agent to implement multiple interdependent features that correspond to a real version upgrade of the target project.
Task Structure… See the full description on the dataset page: https://huggingface.co/datasets/benchmark-anon-2026/RoadmapBench.wds_imagenet_sketchexplicit-edit-benchmark
Explicit Edit Benchmark
226 deterministic exact-edit tasks, run by different agents, harnesses, models and configurations. Every observation records what the harness did and whether the resulting files matched byte for byte.
Source code and benchmark runner: GitHub — Explicit Edit Benchmark
Open the interactive Explorer to compare agents, harnesses, models, versions, reasoning modes, correctness, recovery, time, cost and tokens.
Leaderboard by model route
Score v2… See the full description on the dataset page: https://huggingface.co/datasets/alexshpunt/explicit-edit-benchmark.chess-slm-benchmarkMultiBanana-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.dojo_benchmark_kline
Languages: 简体中文 · English
dojo_benchmark_kline — Benchmark Index Bars
Overview
Daily OHLCV for major broad and representative indices across US, CN, and HK (e.g. ^SPX, ^HSI, 000300.SS). Each row is one index on one trade date.
Files
File
Description
data.parquet
Index daily bars
Key Fields
Field
Description
symbol
Index code (e.g. ^SPX, 000300.SS)
kline_t
Bar interval; "1D" for daily bars
bar_time… See the full description on the dataset page: https://huggingface.co/datasets/AlphaDojo/dojo_benchmark_kline.frames-benchmark
FRAMES: Factuality, Retrieval, And reasoning MEasurement Set
FRAMES is a comprehensive evaluation dataset designed to test the capabilities of Retrieval-Augmented Generation (RAG) systems across factuality, retrieval accuracy, and reasoning.
Our paper with details and experiments is available on arXiv: https://arxiv.org/abs/2409.12941.
Dataset Overview
824 challenging multi-hop questions requiring information from 2-15 Wikipedia articles
Questions span diverse topics… See the full description on the dataset page: https://huggingface.co/datasets/google/frames-benchmark.benchmarking_sbi_runs
Benchmarking SBI Runs
This dataset contains the raw, per-run results underlying the manuscript
"Benchmarking Simulation-Based Inference"
(Lueckmann, Boelts, Greenberg, Goncalves & Macke, AISTATS 2021).
It is a direct migration of the Git LFS data from
mackelab/benchmarking_sbi_runs on GitHub.
For compiled, ready-to-use dataframes built from these raw results (and the code that produced
them), see the companion repository:… See the full description on the dataset page: https://huggingface.co/datasets/mackelab/benchmarking_sbi_runs.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.clamp-benchmark
CLAMP: A Sim-to-Real Benchmark for Closed-Loop Kinematic Pose Estimation and Assembly Reasoning
Closed-Loop Assembly and Mechanism Perception
Accepted at NeurIPS 2026 (Evaluations & Datasets Track)
Kevin Murray1, Randolph Beauregard Robert III2, Petar Z Duric1, Zoran Duric3
1Overlab LLC 2AVA Labs 3George Mason University
📦 Code: https://github.com/overlab-kevin/clamp
Your browser does not support the video tag.
Overview of all 210 labeled real test scenes.… See the full description on the dataset page: https://huggingface.co/datasets/clamp-benchmark/clamp-benchmark.cache_benchmarkVideo-MME-v2
🔥 News
2026.06.11 Videos re-encoded to H265, maintaining consistent evaluation scores. Fixed 2 incorrect MP4s & 3 mismatched URLs. Original data preserved in the original branch.
2026.05.22 Task types are now available for Q1-Q3 in coherence (logic) groups.
🤗 About This Repo
This repository contains annotation data for "Video-MME-v2: Towards the Next Stage in Benchmarks for Comprehensive Video Understanding". It mainly consists of three… See the full description on the dataset page: https://huggingface.co/datasets/MME-Benchmarks/Video-MME-v2.PDE_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.daytrader-benchmarkssai-osworld-v2-benchmark-runs
Sai on OSWorld-V2 — benchmark runs of record
Two complete 108-task runs of the Sai computer-use agent on OSWorld-V2, with full
per-task evidence: scores, trajectories, agent runtime logs, evaluator logs, API
protocol logs, and run manifests.
Run
Date
Tasks scored
Mean score
Perfect (1.0)
Zeros
run1/
2026-08-12
108/108
0.7276
28
7
run2/
2026-08-20
108/108
0.7329
33
5
Model anthropic/claude-opus-5, thinking max, --max_steps 500, screenshot-only
observation… See the full description on the dataset page: https://huggingface.co/datasets/simular-ai/sai-osworld-v2-benchmark-runs.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.wds_imagenet-rKAIST-Multispectral-Pedestrian-Benchmarkbenchmark
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.h3-fewstep-benchmark-20260920
H3 few-step benchmark
Fifteen few-step checkpoints for the MiniMax-H3 video+audio model, each run on the
same 48 English prompts × 2 seeds (0 and 1) × 3 step counts (4, 8, 32) =
288 videos per checkpoint, 4,320 videos in total. Conditioning is text only
(no input image, no camera trajectory). Every video is 1344×768, 5 seconds
(120 frames at 24 fps), with synthesized audio unless noted.
All checkpoints are adapters or distilled variants of MiniMaxAI/MiniMax-H3, except the… See the full description on the dataset page: https://huggingface.co/datasets/hffordata/h3-fewstep-benchmark-20260920.UMI-Benchmark-v1
UMI-Benchmark-v1
UMI-Benchmark-v1 contains 20,000 real-world robot manipulation sessions across 10 tasks:
4 single-arm tasks and 6 dual-arm tasks.
Dataset Summary
Category
Tasks
Sessions
Single-arm
4
9,988
Dual-arm
6
10,012
Total
10
20,000
Tasks
ID
Folder
Type
Task
Sessions
T1
single_arm_task1
Single-arm
Stack Baskets
3,000
T2
single_arm_task2
Single-arm
Trash Bag
1,991
T3
single_arm_task3
Single-arm
Stamp Ink
2… See the full description on the dataset page: https://huggingface.co/datasets/UMIbenchmark/UMI-Benchmark-v1.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.cornetto-benchmarkdataset for Cornetto: A benchmark for LLM-Driven network configuration repair
Paper: https://arxiv.org/abs/2604.22513
wds_imagenet-a