datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.resisc45
RESISC45
Overview
Usage
from datasets import load_dataset
# Load the dataset
dataset = load_dataset('tanganke/resisc45')
Dataset Information
The dataset is divided into the following splits:
Training set: Contains 18,900 examples, used for model training.
Test set: Contains 6,300 examples, used for model evaluation and benchmarking.
The dataset also includes the following augmented sets, which can be used for testing the model's robustness to… See the full description on the dataset page: https://huggingface.co/datasets/tanganke/resisc45.ek100_resized_jpgavm_residential_dataresisc45
Description
RESISC45 dataset is a publicly available benchmark for Remote Sensing Image Scene Classification (RESISC), created by Northwestern Polytechnical University (NWPU). This dataset contains 31,500 images, covering 45 scene classes with 700 images in each class.
The dataset does not have any default splits. Train, validation, and test splits were based on these definitions here… See the full description on the dataset page: https://huggingface.co/datasets/timm/resisc45.total-131-lambda02-residual-s_signal_type6-jh-epoch4
total-131-lambda02-residual-s_signal_type6-jh-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.3765625
Action score: 0.4171875
Valid samples: 320/320
lastlayer-residuals-anon-20260630llama-3b-residualsresisc45UniCACOLMo-2_Residual_Streams
OLMo-2_Residual_Streams
This dataset contains approximately 600 million residual streams derived from the FineWeb dataset. The residual streams were extracted using the allenai/OLMo-2-1124-7B-Instruct model and are stored in .parquet format.
Dataset Description
Usage
The dataset should work just fine with load_dataset:
>>> from datasets import load_dataset
# after loading the data, cast to bf16 using torch.view()
>>> dataset =… See the full description on the dataset page: https://huggingface.co/datasets/open-concept-steering/OLMo-2_Residual_Streams.redcaps5m_resizedGalbot_G1_clear_table_residue_1
Galbot_G1_clear_table_residue_1
Dataset Description
This dataset uses an extended format based on LeRobot and is fully compatible with LeRobot.
Task Preview
View Video Directly
Overview
Total Episodes: 84
Total Frames: 111757
FPS: 30
Dataset Size: 9.23 GB
Robot Name: Galbot_G1
End-Effector Type: two_finger_end_effector
Teleoperation Type: master_arm
Sensors: cam_right_head_rgb,
cam_left_head_rgb,
cam_right_wrist_rgb… See the full description on the dataset page: https://huggingface.co/datasets/RoboCOIN/Galbot_G1_clear_table_residue_1.bacbench-antibiotic-resistance-protein-sequences
Dataset for antibiotic resistance prediction from whole-bacterial genomes (protein sequences)
A dataset of 25,032 bacterial genomes across 39 species with antimicrobial resistance labels.
The genome protein sequences have been extracted from GenBank. Each row contains whole bacterial genome, with spaces
separating different contigs present in the genome.
The antimicrobial resistance labels have been extracted from Antibiotic Susceptibility Test (AST) Browser, accessed 23 Oct, 2024.)… See the full description on the dataset page: https://huggingface.co/datasets/macwiatrak/bacbench-antibiotic-resistance-protein-sequences.remote-community-resilience-paper
Resilience in Remote Communities
This repository hosts the simulation output data used in our paper:
Agent-Based Modeling for the Evaluation of Community Resilience In SilicoPaper submitted to Engineering Reports
🔗 Code Repository
Simulation source code is available at:👉 https://github.com/cmudrc/remote-community-resilience-paper
fineweb-llama3b-residualsimagenet_resized_64x64This is an upload of imagenet_resized/64x64 from tensorflow datasets, (but shuffled before uploading).
The homepage of imagenet_resized is: https://patrykchrabaszcz.github.io/Imagenet32/
imagenet_resized is a derivative of imagenet (and also available to download from there): https://image-net.org/index.php
Warning: The integer labels used are defined by the authors and do not match those from the other ImageNet datasets provided by Tensorflow datasets. See the original label list, and the… See the full description on the dataset page: https://huggingface.co/datasets/sradc/imagenet_resized_64x64.rl-game-traces-resident-evil-4-remake
生化危机4重制版
This public dataset repository contains gameplay trace data uploaded from F:\生化危机4重制版.
Contents
Files: 199
Total local size: 131.17 GB
Generated: 2026-06-13T19:51:14+00:00
File Types
.jsonl: 59
.png: 48
.json: 47
.parquet: 15
.mkv: 15
.txt: 15
Notes
This repository may contain gameplay video, Parquet files, JSON/JSONL metadata, and input event logs.
The license is marked as other; review game footage, audio, and asset… See the full description on the dataset page: https://huggingface.co/datasets/yinhuankuang/rl-game-traces-resident-evil-4-remake.vlabench_primitive_rlds_resize224This dataset is an RLDS-format dataset of finetune primitives that has been resized to 224 and compressed with JPEG encoding. For the dataset conversion, I referred to this script: https://github.com/allenzren/open-pi-zero/blob/main/scripts/data/modify_rlds_dataset.py
RESIDE-ITS
RESIDE-ITS: Indoor Training Set for Single Image Dehazing (Unofficial Mirror)
Unofficial redistribution of RESIDE's ITS (Indoor Training Set) subset, from Li et al.'s RESIDE benchmark (IEEE TIP 2019), packaged for direct use with ClearView's dataset pipeline. Synthetic indoor haze pairs: one clean photograph, ten hazed variants rendered against it at different scattering coefficients.
Disclaimer
This repository is not an official release of RESIDE.… See the full description on the dataset page: https://huggingface.co/datasets/dronefreak/RESIDE-ITS.So-Fake-Set-Resized-224resisc45Redistributed from https://drive.google.com/file/d/1DnPSU5nVSN7xv95bpZ3XQ0JhKXZOKgIv without modification. Only converted the RAR file to a ZIP file. Please cite https://doi.org/10.1109/jproc.2017.2675998 if you use this dataset. The train-val-test split files come from https://arxiv.org/abs/1911.06721.
RESIDE-6Kcsgo_252_resized_npy_fileswds_vtab-resisc45qwen3-resize-easyr1-110k-bbox0p05-remove-pixmo-uground-seeclickRESISC45
Remote Sensing Image Scene Classification (RESISC45) Dataset
Paper Remote Sensing Image Scene Classification: Benchmark and State of the Art
Paper with code: RESISC45
Description
The RESISC45 dataset is a scene classification dataset that focuses on RGB images extracted using Google Earth. This dataset comprises a total of 31,500 images, with each image having a resolution of 256x256 pixels. RESISC45 contains 45 different scene classes, with 700 images per… See the full description on the dataset page: https://huggingface.co/datasets/blanchon/RESISC45.birds-57-ProtBySubsystems-resize-512bacbench-antibiotic-resistance-protein-sequences
Dataset for antibiotic resistance prediction from whole-bacterial genomes (protein sequences)
A dataset of 25,032 bacterial genomes across 39 species with antimicrobial resistance labels.
The genome protein sequences have been extracted from GenBank. Each row contains whole bacterial genome, with spaces
separating different contigs present in the genome.
The antimicrobial resistance labels have been extracted from Antibiotic Susceptibility Test (AST) Browser, accessed 23 Oct, 2024.)… See the full description on the dataset page: https://huggingface.co/datasets/mbafca2/bacbench-antibiotic-resistance-protein-sequences.NWPU-RESISC45
Dataset Card for "NWPU-RESISC45"
Licensing Information
[CC-BY-SA]
Citation Information
Remote sensing image scene classification: Benchmark and state of the art
@article{cheng2017remote,
title = {Remote sensing image scene classification: Benchmark and state of the art},
author = {Cheng, Gong and Han, Junwei and Lu, Xiaoqiang},
year = 2017,
journal = {Proceedings of the IEEE},
publisher = {IEEE},
volume = 105… See the full description on the dataset page: https://huggingface.co/datasets/jonathan-roberts1/NWPU-RESISC45.