datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
EmbodiedGenDatahttps://huggingface.co/spaces/HorizonRobotics/EmbodiedGen-Gallery-Explorer
cad-environments
CAD Environments
CAD Environments is a multimodal dataset of complete, human-performed workflows in desktop CAD software. The current release contains 51 task workflows totaling 99.03 hours, covering eight software groups across mechanical design, architecture, MEP, structural design, and general 3D modeling.
Each workflow preserves the full task context—not just the final model—including the problem statement, reference and input files, a gold output, evaluation rubrics, a… See the full description on the dataset page: https://huggingface.co/datasets/markov-ai/cad-environments.textvqa
Large-scale Multi-modality Models Evaluation Suite
Accelerating the development of large-scale multi-modality models (LMMs) with lmms-eval
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This Dataset
This is a formatted version of TextVQA. It is used in our lmms-eval pipeline to allow for one-click evaluations of large multi-modality models.
@inproceedings{singh2019towards,
title={Towards vqa models that can read},
author={Singh, Amanpreet and… See the full description on the dataset page: https://huggingface.co/datasets/lmms-lab-encoder/textvqa.OceanDepths
OceanDepths GeoTIFF Raster and Aligned ARGO Dataset
This dataset package contains the model-ready Ocean variables (ARGO submarine data, sea surface height, sea surface temperature and
salinity, as well as GLORYS reanalysis information for 50 depth levels. The ARGO data has been projected onto the GLORYS grid in order
to build a ML-ready dataset. The intention is that users can create tensors easily for CV-inspired ML approaches to ocean-variable
reconstruction. While… See the full description on the dataset page: https://huggingface.co/datasets/ESA-philab/OceanDepths.GQA
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 GQA. It is used in our lmms-eval pipeline to allow for one-click evaluations of large multi-modality models.
@inproceedings{hudson2019gqa,
title={Gqa: A new dataset for real-world visual reasoning and compositional… See the full description on the dataset page: https://huggingface.co/datasets/lmms-lab-encoder/GQA.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.DocVQA
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 DocVQA. It is used in our lmms-eval pipeline to allow for one-click evaluations of large multi-modality models.
@article{mathew2020docvqa,
title={DocVQA: A Dataset for VQA on Document Images. CoRR abs/2007.00398 (2020)}… See the full description on the dataset page: https://huggingface.co/datasets/lmms-lab-encoder/DocVQA.VQAv2food101
Dataset Card for Food-101
Dataset Summary
This dataset consists of 101 food categories, with 101'000 images. For each class, 250 manually reviewed test images are provided as well as 750 training images. On purpose, the training images were not cleaned, and thus still contain some amount of noise. This comes mostly in the form of intense colors and sometimes wrong labels. All images were rescaled to have a maximum side length of 512 pixels.
Supported Tasks and… See the full description on the dataset page: https://huggingface.co/datasets/ethz/food101.POPE
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 POPE. It is used in our lmms-eval pipeline to allow for one-click evaluations of large multi-modality models.
@article{li2023evaluating,
title={Evaluating object hallucination in large vision-language models}… See the full description on the dataset page: https://huggingface.co/datasets/lmms-lab-encoder/POPE.MMMUThis is a merged version of MMMU/MMMU with all subsets concatenated.
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 MMMU. It is used in our lmms-eval pipeline to allow for one-click evaluations of large multi-modality models.
@article{yue2023mmmu,
title={Mmmu: A… See the full description on the dataset page: https://huggingface.co/datasets/lmms-lab-encoder/MMMU.RGB-Event-ISP-DatasetEasyNegative
Negative Embedding
This is a Negative Embedding trained with Counterfeit. Please use it in the "\stable-diffusion-webui\embeddings" folder.It can be used with other models, but the effectiveness is not certain.
Counterfeit-V2.0.safetensors
AbyssOrangeMix2_sfw.safetensors
anything-v4.0-pruned.safetensors
EO-Data1.5M
🤖 EO-Data-1.5M
A Large-Scale Interleaved Vision-Text-Action Dataset for Embodied AI
The first large-scale interleaved embodied dataset emphasizing temporal dynamics and causal dependencies among vision, language, and action modalities.
📊 Dataset Overview
EO-Data-1.5M is a massive, high-quality multimodal embodied reasoning dataset designed for training generalist robot… See the full description on the dataset page: https://huggingface.co/datasets/IPEC-COMMUNITY/EO-Data1.5M.WSI_Embedding
WSI_Embedding
Patch and slide embeddings for whole-slide images, plus the run records used to produce them.
Original WSIs are not in this repo. They stay on HPC / the source dataset remotes. Do not expect .svs / .tif here.
Repo: thanminh01/WSI_Embedding
Top-level layout
Path
What it is
Download when
embeddings/<dataset>/<model>/<mag>x_<patch>px_<overlap>px_overlap/
Encoder tensors (.h5 / .pt / WSI-LLaVA json) and per-model TRIDENT _config_ / _logs_
You… See the full description on the dataset page: https://huggingface.co/datasets/thanminh01/WSI_Embedding.MME
Evaluation Dataset for MME
EgoIT-99KCheckout the paper EgoLife (https://arxiv.org/abs/2503.03803) for more information.
gaia2_filesystem
GAIA2 Filesystem
This is a dataset containing files for the GAIA2 benchmark. You should not use this dataset on its own, but instead use the Meta Agents Research Environments framework to execute scenarios from that GAIA2 dataset.
Dataset Link
https://huggingface.co/datasets/meta-agents-research-environments/gaia2
Contact Details
Publishing POC: Meta AI Research Team
Affiliation: Meta Platforms, Inc.
Website:… See the full description on the dataset page: https://huggingface.co/datasets/meta-agents-research-environments/gaia2_filesystem.SEED-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.ExtractBench
ExtractBench
Quick links: [🌐 Website] [📜 Paper] [💻 Code]
Given a document and a schema, a system returns structured data with evidence. The input is a full document, born-digital or scanned, and a schema written by the user. The output is a schema-valid JSON object, with the source page and a bounding box for each value as evidence. It must return correct, exhaustive values (including repeated records), correctly use null for absent information, and ground each extracted… See the full description on the dataset page: https://huggingface.co/datasets/llamaindex/ExtractBench.MMBenchimgcmevs-erp-eval
CM-EVS: A Coverage-Curated Panoramic RGB-D Dataset for Indoor Scene Understanding
CM-EVS is a curated panoramic RGB-D dataset built under a single principle: maximize the geometric coverage of a 3D scene with the fewest equirectangular (ERP) frames possible. The release is structured as one redistributable Blender indoor data archive plus four license-aware adapter packages that regenerate matched frames locally from upstream sources whose terms forbid redistribution.
v1.0… See the full description on the dataset page: https://huggingface.co/datasets/anon-cmevs-2026/cmevs-erp-eval.OCRBenchGithub|Paper
OCRBench has been accepted by Science China Information Sciences.
RAG_EvalScienceQA
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 derek-thomas/ScienceQA. It is used in our lmms-eval pipeline to allow for one-click evaluations of large multi-modality models.
@inproceedings{lu2022learn,
title={Learn to Explain: Multimodal Reasoning via Thought… See the full description on the dataset page: https://huggingface.co/datasets/lmms-lab-encoder/ScienceQA.ChartQA
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 ChartQA. It is used in our lmms-eval pipeline to allow for one-click evaluations of large multi-modality models.
@article{masry2022chartqa,
title={ChartQA: A benchmark for question answering about charts with visual and… See the full description on the dataset page: https://huggingface.co/datasets/lmms-lab-encoder/ChartQA.ai2d@misc{kembhavi2016diagram,
title={A Diagram Is Worth A Dozen Images},
author={Aniruddha Kembhavi and Mike Salvato and Eric Kolve and Minjoon Seo and Hannaneh Hajishirzi and Ali Farhadi},
year={2016},
eprint={1603.07396},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
imagenet_1k_resized_256
Dataset Card for "imagenet_1k_resized_256"
Dataset summary
The same ImageNet dataset but all the smaller side resized to 256.
A lot of pretraining workflows contain resizing images to 256 and random cropping to 224x224, this is why 256 is chosen.
The resized dataset can also be downloaded much faster and consume less space than the original one.
See here for detailed readme.
Dataset Structure
Below is the example of one row of data. Note that the labels in… See the full description on the dataset page: https://huggingface.co/datasets/evanarlian/imagenet_1k_resized_256.eMCR
eMCR: A Benchmark for Multi-Condition Product Retrieval in Chinese E-Commerce
This is the official dataset and evaluation code for the paper "eMCR: A Benchmark for Multi-Condition Product Retrieval in Chinese E-Commerce".
Overview
Product search increasingly involves queries that combine multiple requirements — product attributes, brands, prices, exclusions, and visual descriptions. Existing retrieval benchmarks provide limited support for diagnosing which… See the full description on the dataset page: https://huggingface.co/datasets/alibabagroup/eMCR.