skip113/cli dataset hosted on Hugging Face and contributed by the HF Datasets community

Codyfederer/ml-dataset-cli-test dataset hosted on Hugging Face and contributed by the HF Datasets community

MmCows: A Multimodal Dataset for Dairy Cattle Monitoring

Details of the dataset and benchmarks are available here. For a quick overview of the dataset, please check this video.

  Instruction for downloading





  1. Install requirements

pip install huggingface_hub

See the file structure here for the next step.

  2. Download a file individually

To download visual_data.zip to your local-dir, use command line: huggingface-cli download
neis-lab/mmcows \… See the full description on the dataset page: https://huggingface.co/datasets/neis-lab/mmcows.

AmirMohseni/CLI-v2 dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset Card for aiornot

Dataset for the aiornot competition. By accessing this dataset, you accept the rules of the AI or Not competition. Please note that dataset may contain images which are not considered safe for work.

  Usage





  With Hugging Face Datasets 🤗

You can download and use this dataset using the datasets library. 📝 Note: You must be logged in to you Hugging Face account for the snippet below to work. You can do this with huggingface-cli login or… See the full description on the dataset page: https://huggingface.co/datasets/competitions/aiornot.

Experienced

ML training data for the Roman Microlensing Data CHallenge 2025 - Beginner tier.

  Uploading

CLI:

Install the Hugging Face CLI

brew install huggingface-cli

Login with your Hugging Face credentials

hf auth login

Push your dataset files

hf upload RGES-PIT/Beginner . --repo-type=dataset

Python: from huggingface_hub import HfApi

api = HfApi(token=os.getenv("HF_TOKEN")) api.upload_folder( folder_path="/path/to/local/dataset"… See the full description on the dataset page: https://huggingface.co/datasets/RGES-PIT/Beginner.

BM25, embedding index used in BrowseComp-Plus. For downloading the index: huggingface-cli download Tevatron/browsecomp-plus-indexes --repo-type=dataset --include="bm25/*" --local-dir ./indexes huggingface-cli download Tevatron/browsecomp-plus-indexes --repo-type=dataset --include="qwen3-embedding-0.6b/*" --local-dir ./indexes huggingface-cli download Tevatron/browsecomp-plus-indexes --repo-type=dataset --include="qwen3-embedding-4b/*" --local-dir ./indexes huggingface-cli download… See the full description on the dataset page: https://huggingface.co/datasets/Tevatron/browsecomp-plus-indexes.

Wireframe Dataset

This is the Wireframe dataset hosted on Hugging Face Hub.

  Summary

Wireframe dataset with image annotations including line segments.The dataset is stored as jsonl files (train/metadata.jsonl, test/metadata.jsonl) and images. Number of samples:

Train: 5,000 Test: 462

  Download

Download with huggingface-hub

python3 -m pip install huggingface-hub huggingface-cli download --repo-type dataset lh9171338/Wireframe --local-dir ./

Download with Git… See the full description on the dataset page: https://huggingface.co/datasets/lh9171338/Wireframe.

MOSEv2: A More Challenging Dataset for Video Object Segmentation in Complex Scenes

🔥 Evaluation Server | 🏠 Homepage | 📄 Paper | 🔗 GitHub

  Download

We recommend using huggingface-cli to download: pip install -U "huggingface_hub[cli]" huggingface-cli download FudanCVL/MOSEv2 --repo-type dataset --local-dir ./MOSEv2 --local-dir-use-symlinks False --max-workers 16

  Dataset Summary

MOSEv2 is a comprehensive video object segmentation dataset designed to advance… See the full description on the dataset page: https://huggingface.co/datasets/FudanCVL/MOSEv2.

GuardReasonerTrain

GuardReasonerTrain is the training data for R-SFT of GuardReasoner, as described in the paper GuardReasoner: Towards Reasoning-based LLM Safeguards. Code: https://github.com/yueliu1999/GuardReasoner/

  Usage

from datasets import load_dataset

Login using e.g. huggingface-cli login to access this dataset

ds = load_dataset("yueliu1999/GuardReasonerTrain")

  Citation

If you use this dataset, please cite our paper. @article{GuardReasoner… See the full description on the dataset page: https://huggingface.co/datasets/yueliu1999/GuardReasonerTrain.

KTDA-Datasets

This dataset card aims to describe the datasets used in the KTDA.

  Install

pip install huggingface-hub

  Usage

Step 1: Download datasets

huggingface-cli download --repo-type dataset XavierJiezou/ktda-datasets --local-dir data --include grass.zip huggingface-cli download --repo-type dataset XavierJiezou/ktda-datasets --local-dir data --include cloud.zip

Step 2: Extract datasets

unzip grass.zip -d grass unzip cloud.zip -d l8_biome… See the full description on the dataset page: https://huggingface.co/datasets/XavierJiezou/ktda-datasets.

from datasets import load_dataset

  Login using e.g. huggingface-cli login to access this dataset

ds = load_dataset("huggingface/transformers-metadata", "frameworks")

Quick start

The easiest way to download the dataset to your local is to use huggingface-cli. The specific command you can use is huggingface-cli download zifeng-ai/TrialPanorama-database --local-dir LOCAL_DIR --repo-type dataset

where LOCAL_DIR should be replaced with the target directory you want to save your dataset to.

  Update history

Aug.4 2025: updated tables with the full set of studies

Dataset website: https://ryanwangzf.github.io/projects/trialpanorama… See the full description on the dataset page: https://huggingface.co/datasets/zifeng-ai/TrialPanorama-database.

Experienced

ML training data for the Roman Microlensing Data CHallenge 2025 - Experienced tier.

  Uploading

CLI:

Install the Hugging Face CLI

brew install huggingface-cli

Login with your Hugging Face credentials

hf auth login

Push your dataset files

hf upload RGES-PIT/Experienced . --repo-type=dataset

Python: from huggingface_hub import HfApi

api = HfApi(token=os.getenv("HF_TOKEN")) api.upload_folder( folder_path="/path/to/local/dataset"… See the full description on the dataset page: https://huggingface.co/datasets/RGES-PIT/Experienced.

Dataset Summary

Healthy CT data for abdominal organs (liver, pancreas and kidney) are filtered out from public dataset.

  Downloading Instructions





  1- Install the Hugging Face library:

pip install -U "huggingface_hub[cli]"

  2- Download the dataset:

mkdir HealthyCT cd HealthyCT huggingface-cli download qicq1c/HealthyCT --repo-type dataset --local-dir . --cache-dir ./cache

[Optional] Resume downloading

In case you had a previous interrupted download… See the full description on the dataset page: https://huggingface.co/datasets/qicq1c/HealthyCT.

Fastmap evaluation suite.

You only need the databases to run fastmap. Download the images if you want to produce colored point cloud. Download the subset of data you want to your local directory. huggingface-cli download whc/fastmap_sfm --repo-type dataset --local-dir ./ --include 'databases/tnt_*' 'ground_truths/tnt_*'

or use the python interface from huggingface_hub import hf_hub_download, snapshot_download snapshot_download( repo_id="whc/fastmap_sfm", repo_type='dataset'… See the full description on the dataset page: https://huggingface.co/datasets/whc/fastmap_sfm.

Description

This is the dataset repository used in the pyiqa toolbox. Please refer to Awesome Image Quality Assessment for details of each dataset Example commandline script with huggingface-cli: huggingface-cli download chaofengc/IQA-PyTorch-Datasets live.tgz --local-dir ./datasets --repo-type dataset cd datasets tar -xzvf live.tgz

  Disclaimer for This Dataset Collection

This collection of datasets is compiled and maintained for academic, research, and educational… See the full description on the dataset page: https://huggingface.co/datasets/chaofengc/IQA-PyTorch-Datasets.

1X World Model Compression Challenge Dataset

This repository hosts the dataset for the 1X World Model Compression Challenge. huggingface-cli download 1x-technologies/worldmodel --repo-type dataset --local-dir data

  Updates Since v1.1

Train/Val v2.0 (~100 hours), replacing v1.1 Test v2.0 dataset for the Compression Challenge Faces blurred for privacy New raw video dataset (CC-BY-NC-SA 4.0) at worldmodel_raw_data Example scripts now split into: cosmos_video_decoder.py —… See the full description on the dataset page: https://huggingface.co/datasets/1x-technologies/world_model_tokenized_data.

Pansharpening-Datasets

This dataset card aims to describe the datasets used in the Pansharpening.

  Install

pip install huggingface-hub

  Usage

Step 1: Download datasets

huggingface-cli download --repo-type dataset XavierJiezou/pansharpening-datasets --local-dir data --include PanBench.zip

Step 2: Extract datasets

unzip PanBench.zip -d PanBench

  Citation

@Article{cmfnet, AUTHOR = {Wang, Shiying and Zou, Xuechao and Li, Kai and Xing, Junliang and… See the full description on the dataset page: https://huggingface.co/datasets/XavierJiezou/pansharpening-datasets.

COCO 2017 mirror

This is a just mirror of the raw COCO dataset files, for convenience. You have to download it using something like: pip install huggingface_hub

huggingface-cli download --local-dir coco-2017 pcuenq/coco-2017-mirror

And then unzip the files before use.