datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
re10k_torch
Torch Version of RE10K Dataset
This repository contains preprocessed data for the paper TriSplat, VolSplat and ZPressor
Citation
If you find TriSplat, VolSplat and ZPressor useful for your research, please consider citing:
@article{wang2026trisplat,
title={TriSplat: Simulation-Ready Feed-Forward 3D Scene Reconstruction},
author={Wang, Weijie and Li, Zimu and Shi, Jinchuan and Zhang, Zeyu and Ye, Botao and Pollefeys, Marc and Chen, Donny Y. and Zhuang, Bohan}… See the full description on the dataset page: https://huggingface.co/datasets/lhmd/re10k_torch.CropClimateX
CropClimateX: A large-scale, multitask, multisensory dataset for climate-aware crop monitoring in the United States from 2018–2022
Adrian Höhl, Stella Ofori-Ampofo, Miguel-Ángel Fernández-Torres, Rıdvan Salih Kuzu, and Xiao Xiang Zhu
Repository: github.com/drnhhl/CropClimateX
Paper: https://doi.org/10.1038/s41597-026-06611-x
License: CC-BY-4.0
Contact: adrian.hoehl@tum.de
The database includes 15,500 small data cubes (i.e., minicubes), each with a spatial coverage of 12x12km… See the full description on the dataset page: https://huggingface.co/datasets/torchgeo/CropClimateX.eurosatRedistributed without modification from https://github.com/phelber/EuroSAT.
EuroSAT100 is a subset of EuroSATallBands containing only 100 images. It is intended for tutorials and demonstrations, not for benchmarking.
acid_torch
Torch Version of ACID Dataset
This repository contains preprocessed data for the paper VolSplat and ZPressor
Citation
If you find VolSplat and ZPressor useful for your research, please consider citing:
@article{wang2025zpressor,
title={ZPressor: Bottleneck-Aware Compression for Scalable Feed-Forward 3DGS},
author={Wang, Weijie and Chen, Donny Y and Zhang, Zeyu and Shi, Duochao and Liu, Akide and Zhuang, Bohan},
journal={arXiv preprint arXiv:2505.23734}… See the full description on the dataset page: https://huggingface.co/datasets/lhmd/acid_torch.btcusdt_spot_1m_03_2023_to_12_2025dl3dv_torch_960harmonized_global_cropscybersecurity-classification-benchmark
TorchSight Cybersecurity Classification Benchmark
A two-tier benchmark dataset for evaluating cybersecurity document
classifiers, released with the TorchSight system. Used in:
Dobrovolskyi, I. Security Document Classification with a Fine-Tuned Local
Large Language Model: Benchmark Data and an Open-Source System. Journal of
Information Security and Applications, 2026.
Canonical per-model numbers live in BENCHMARK_NUMBERS.md,
auto-generated from the per-prediction result JSONs… See the full description on the dataset page: https://huggingface.co/datasets/torchsight/cybersecurity-classification-benchmark.dl3dv_bench_torch_960ChesapeakeRSCdigital_typhoonDigitial Typhoon Dataset:
KITAMOTO, A., HWANG, J., VUILLOD, B., GAUTIER, L., TIAN, Y., & CLANUWAT, T. (2023, December). Digital Typhoon: Long-term Satellite Image Dataset for the Spatio-Temporal Modeling of Tropical Cyclones. NeurIPS 2023 Datasets and Benchmarks (Spotlight).
This dataset was created by the Digital Typhoon project.
Camelyon16_MIL
CAMELYON16 - Multiple Instance Learning (MIL)
Important. This dataset is part of the torchmil library.
This repository provides an adapted version of the CAMELYON16 dataset tailored for Multiple Instance Learning (MIL). It is designed for use with the CAMELYON16Dataset class from the torchmil library. CAMELYON16 is a widely used benchmark in MIL research, making this adaptation particularly valuable for developing and evaluating MIL models.
About the Original CAMELYON16… See the full description on the dataset page: https://huggingface.co/datasets/torchmil/Camelyon16_MIL.lenny-functional-torchfloodnetRehosted from Google drive from Paper Repo.
If you use this dataset, please cite:
https://arxiv.org/abs/2012.02951
bigearthnetdynamic_earthnetDynamic EarthNet dataset redistributed from https://mediatum.ub.tum.de/1650201 and https://cvg.cit.tum.de/webshare/u/toker/dynnet_training_splits/ under a common tarball for simpler download speeds.
Individual zip files were replaced with tarballs instead.
In the mediatum server version the following directories have the wrong name compared to the given split txt files:
/labels/5111_4560_13_38S
/labels/6204_3495_13_46N
/labels/7026_3201_13_52N
/labels/7367_5050_13_54S
/labels/2459_4406_13_19S… See the full description on the dataset page: https://huggingface.co/datasets/torchgeo/dynamic_earthnet.torchvision-domainnetucmercedRedistributed from http://weegee.vision.ucmerced.edu/datasets/landuse.html without modification. See https://www.usgs.gov/faqs/what-are-terms-uselicensing-map-services-and-data-national-map for license.
torchange_xView2diorDataset rehosted for easier and more accesible download capabilities.
If you use this dataset in your research, please cite:
https://arxiv.org/abs/1909.00133
re10k_torch
Torch Version of RE10K Dataset
This repository contains preprocessed data for the paper TriSplat, VolSplat and ZPressor
Citation
If you find TriSplat, VolSplat and ZPressor useful for your research, please consider citing:
@article{wang2026trisplat,
title={TriSplat: Simulation-Ready Feed-Forward 3D Scene Reconstruction},
author={Wang, Weijie and Li, Zimu and Shi, Jinchuan and Zhang, Zeyu and Ye, Botao and Pollefeys, Marc and Chen, Donny Y. and Zhuang, Bohan}… See the full description on the dataset page: https://huggingface.co/datasets/Jathie/re10k_torch.ComfyUI_portable_torch_2.13.0_cu130_cp313_sageattention_tritonI recommend run run_nvidia_gpu.bat for more stability work with this comfyui build
I put Extra_Model_Paths_Maker.bat if you need synchronize models path from current ComfyUI Build. Put this .bat file into your model path, double click on Extra_Model_Paths_Maker.bat and extra_model_paths.yaml will appear in model path. Then copy/cut extra_model_paths.yaml and put into new ComfyUI Build into ComfyUI_windows_portable\ComfyUI . Now the models are visible in the new ComfyUI build
The build includes… See the full description on the dataset page: https://huggingface.co/datasets/StefanFalkok/ComfyUI_portable_torch_2.13.0_cu130_cp313_sageattention_triton.ethusdt_spot_1m_05_2021_to_03_2026
ETHUSDT Spot 1-Minute OHLCV (May 2021 - Mar 2026)
Overview
1-minute OHLCV candlestick data for the ETH/USDT spot pair on Binance, covering May 1, 2021 to February 28, 2026.
Rows: 2,541,600
Completeness: 100.00%
Sources
Period
Source
Notes
Full dataset
Binance Data Collection
Monthly kline ZIPs
2021-08-13 02:00-06:29
Bybit API
270 bars filled from Bybit ETHUSDT spot (Binance maintenance)
2021-09-29 07:00-08:59
Bybit API
120 bars filled from… See the full description on the dataset page: https://huggingface.co/datasets/Torch-Trade/ethusdt_spot_1m_05_2021_to_03_2026.torch-measure-datareconfusion-torchtorchani-tests-pickled-filesai4artic-sea-ice-challengeRehosted dataset from Ready-To-Train AI4Arctic Sea Ice Challenge, to include a common tarball
with faster download speeds. The additional metadata.csv file was created with the generate_metadata.py script
If you use this dataset, please cite:
Buus-Hinkler, Jørgen; Wulf, Tore; Stokholm, Andreas Rønne; Korosov, Anton; Saldo, Roberto; Pedersen, Leif Toudal; et al. (2022). AI4Arctic Sea Ice Challenge Dataset. Technical University of Denmark. Collection. https://doi.org/10.11583/DTU.c.6244065.v2
ssl4eo_lSSL4EO-L: Self-Supervised Learning for Earth Observation for the Landsat family of satellites.
geonrwAttribution: https://ieee-dataport.org/open-access/geonrw
Publication: https://ieeexplore.ieee.org/document/9406194
torchange_s2looking