datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
KAIST-Multispectral-Pedestrian-Detection-Datasettwaitch-txtKairos_AFEIThe AFEI Corpus: Holonic Scarcity Dynamics & The Shadow Lineage
By Sebastiaan van der Heide (Unityinsight / Kairos_AFEI)
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The AFEI Corpus and the AFEI Research Methodology are meant to be used to analyze societal and institutional dynamics, with a particular focus on institutional iatrogenesis, epistemic entrapment and epistemic entrainment.
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AFEI Origins:
The following commits contain descriptions and files which show the entire creation and operational blueprint of the AFEI Methodology… See the full description on the dataset page: https://huggingface.co/datasets/Unityinsight/Kairos_AFEI.MSVQAThis is a multimodal cross-scenario dataset for continual learning with MLLMs. We provide a simple script to split the dataset in multiple ways.
The dataset format has been adjusted for Qwen. The coordinates in 'train_annfiles.json' and 'val_annfiles.json' are adjusted to Qwen2.5VL format. And 'train_annfiles_ori.json' and 'val_annfiles_ori.json' retain the original coordinates of the bounding box.
You need to adjust the coordinates fit your format.
Detailed information can refer to… See the full description on the dataset page: https://huggingface.co/datasets/Kaij00/MSVQA.YFCC15M
YFCC15M Recaption Dataset
This YFCC15M Dataset is filtered by DeCLIP and recaptioned utilize the diverse description generation framework proposed in RWKV-CLIP.
The text is a list of text tokens with a length of 77, encoded using the CLIP tokenizer. You can use from clip.simple_tokenizer import SimpleTokenizer as _Tokenizer to decode it back into the original text.
Using Dataset
You can easily download and use the arxiver dataset with Hugging Face's datasets library.… See the full description on the dataset page: https://huggingface.co/datasets/Kaichengalex/YFCC15M.Kai0
KAI0
TODO
The advantage label will be coming soon.
Contents
About the Dataset
Load the Dataset
Download the Dataset
Dataset Structure
Folder hierarchy
Details
License and Citation
About the Dataset
~134 hours real world scenarios
Main Tasks
Task_A
Single task
Initial state: T-shirts are randomly tossed onto the table, presenting random crumpled configurations
Manipulation task: Operate… See the full description on the dataset page: https://huggingface.co/datasets/OpenDriveLab-org/Kai0.KAIST-Multispectral-Pedestrian-Benchmarkopacity-marginalization
Amortized opacity marginalization: data
Training sets, trained models, priors and held-out evaluation sets for Amortized Opacity Marginalization Improves C/O Interval Calibration for Brown-Dwarf Retrievals (Heraty 2026, arXiv:2609.01665). Code and paper source: github.com/kaileh57/opacity-marginalization.
Contents
spectra/v7_opmarg: one million simulated NIRSpec G395H spectra generated with randomized molecular opacities (the marginalized training set)… See the full description on the dataset page: https://huggingface.co/datasets/Kaileh57/opacity-marginalization.PCMind-2.1-Kaiyuan-2B
This repository contains the complete pretraining dataset for
PCMind-v2.1-Kaiyuan-2B, a leading fully open-source language model.
Overview
The dataset is organized into 5 training phases, with all phase datasets open-sourced in this repository. Our training methodology employs domain-specific mixing strategies across five primary domains:
English: General English text
Chinese: General Chinese text
Code: Programming and code-related content
Math: Mathematical reasoning and… See the full description on the dataset page: https://huggingface.co/datasets/thu-pacman/PCMind-2.1-Kaiyuan-2B.DeepJEB-PP
DeepJEB++
Foundation Model-Driven Large-Scale 3D Engineering Dataset via 2D Latent Space Augmentation
Soyoung Yoo · Leekyo Jeong · Jinsu Ra · Dongeon Lee · Sunwoong Yang · Hyogu Jeong · Namwoo Kang — KAIST SmartDesignLab
📦 Dataset size & viewer note. DeepJEB++ contains 15,360 deployable, simulation-labeled brackets. The Hugging Face Dataset Viewer above shows only a small preview because the FEA field data are distributed as a compressed archive… See the full description on the dataset page: https://huggingface.co/datasets/KAIST-SmartDesignLab/DeepJEB-PP.coda-lm
CODA-LM Dataset Card
CODA-LM is the multi-modal version of the CODA dataset, used in the CODA-LM paper. Both English and Chinese annotations are available. Check detailed usage in our Github repo.
Citation
@article{li2024automated,
title={Automated Evaluation of Large Vision-Language Models on Self-driving Corner Cases},
author={Li, Yanze and Zhang, Wenhua and Chen, Kai and Liu, Yanxin and Li, Pengxiang and Gao, Ruiyuan and Hong, Lanqing and Tian, Meng and Zhao… See the full description on the dataset page: https://huggingface.co/datasets/KaiChen1998/coda-lm.Techie_Raw_PDFCoT-Collection"""
_LICENSE = "CC BY 4.0"
_HOMEPAGE = "https://github.com/kaistAI/CoT-Collection"
_LANGUAGES = {
"en": "English",
}
# _ALL_LANGUAGES = "all_languages"
class CoTCollectionMultiConfig(datasets.BuilderConfig):Kairos
Kairos — Long-Form Video Annotation and Benchmark
Kairos is an automated annotation pipeline for long-duration videos (10–30 minutes).
This repository hosts a benchmark of 2,870 multiple-choice and 2,870 free-form
(OpenQA) questions across 820 videos, spanning 17 fine-grained capabilities and
5 temporal tiers (T1: single moment, T2: 1–60 s, T3: 60–300 s, T4: 300–900 s, T5: >900 s).
What's inside
.
├── data/
│ ├── kairos_benchmark.jsonl # 2,870 MCQs (bilingual… See the full description on the dataset page: https://huggingface.co/datasets/nips26anonymous159/Kairos.kaifukujutsushinoyarinaoshi
Bangumi Image Base of Kaifuku Jutsushi No Yarinaoshi
This is the image base of bangumi Kaifuku Jutsushi no Yarinaoshi, we detected 86 characters, 4799 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1%… See the full description on the dataset page: https://huggingface.co/datasets/BangumiBase/kaifukujutsushinoyarinaoshi.coda-lm-llava-format
CODA-LM Dataset Card
CODA-LM is the multi-modal version of the CODA dataset, used in the CODA-LM paper. Both English and Chinese annotations are available. Check detailed usage in our Github repo.
This repo contains the CODA-LM dataset, which has been reorganized in the LLaVA data format.
You are also welcome to check the original CODA-LM data which contains more metadata vanilla annotations.
Usage
from datasets import load_dataset
# name can be selected from… See the full description on the dataset page: https://huggingface.co/datasets/KaiChen1998/coda-lm-llava-format.dsrl-offline-v1twaitch-txt-2d4ke-local-aiPPE-GPQA-Best-of-K
Overview
This contains the GPQA correctness preference evaluation set for Preference Proxy Evaluations.
The prompts are sampled from GPQA.
This dataset is meant for benchmarking and evaluation, not for training.
Paper
Code
License
User prompts are licensed under CC BY 4.0, and model outputs are governed by the terms of use set by the respective model providers.
Citation
@misc{frick2024evaluaterewardmodelsrlhf,
title={How to Evaluate Reward Models for… See the full description on the dataset page: https://huggingface.co/datasets/lmarena-ai/PPE-GPQA-Best-of-K.dsrl-offline-costablation-v0RealSyn100M
[ACM MM25] RealSyn: An Effective and Scalable Multimodal Interleaved Document Transformation Paradigm
Tiancheng Gu,
Kaicheng Yang,
Chaoyi Zhang,
Yin Xie,
Xiang An,
Ziyong Feng,
Dongnan Liu,
Weidong Cai,
Jiankang Deng
💡 Introduction
Contrastive Language-Image Pre-training (CLIP) demonstrates promising performance on a wide variety of benchmarks. However, a substantial volume of non-paired data, such as multimodal interleaved documents, remains… See the full description on the dataset page: https://huggingface.co/datasets/Kaichengalex/RealSyn100M.kaist-urban-dataset
KAIST Complex Urban Dataset — Converted ROS Bags
This repository mirrors ROS bag conversions of the KAIST Complex Urban Dataset,
covering sequences urban18 through urban39. Each sequence includes:
urbanXX.bag — ROS bag with the full multi-sensor recording (camera, LiDAR, IMU, GPS, wheel encoders)
urbanXX.csv — ground truth trajectory
urbanXX.txt — ground truth converted to TUM/text format (via kaist_gt_csv2txt.m)
Origin
The underlying sensor data was collected… See the full description on the dataset page: https://huggingface.co/datasets/gladiator7737/kaist-urban-dataset.d1stem-corpusfriends-dialogUDR_CosmosQA
Dataset Card for "UDR_CosmosQA"
More Information needed
WMT-month-splitsPPE-MMLU-Pro-Best-of-K
Overview
This contains the MMLU-Pro correctness preference evaluation set for Preference Proxy Evaluations.
The prompts are sampled from MMLU-Pro.
This dataset is meant for benchmarking and evaluation, not for training.
Paper
Code
License
User prompts are licensed under MIT, and model outputs are governed by the terms of use set by the respective model providers.
Citation
@misc{frick2024evaluaterewardmodelsrlhf,
title={How to Evaluate Reward… See the full description on the dataset page: https://huggingface.co/datasets/lmarena-ai/PPE-MMLU-Pro-Best-of-K.