This data accompanies the WebUI project (https://dl.acm.org/doi/abs/10.1145/3544548.3581158) For more information, check out the project website: https://uimodeling.github.io/ To download this dataset, you need to install the huggingface-hub package pip install huggingface-hub

Use snapshot_download from huggingface_hub import snapshot_download snapshot_download(repo_id="biglab/webui-test", repo_type="dataset")

IMPORTANT

Before downloading and using, please review the copyright info here:… See the full description on the dataset page: https://huggingface.co/datasets/biglab/webui-test.

You can load the dataset as follows: from huggingface_hub import snapshot_download

snapshot_download(repo_id="shc443/MaternKernel_compositionality", repo_type="dataset")

For more information regarding data generating process, please refer to our paper or github page

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.

Download the SoccerNet Ball Action Spotting dataset in the OSL Action Spotting JSON format

from huggingface_hub import snapshot_download snapshot_download(repo_id="OpenSportsLab/SoccerNet-BallActionSpotting-Videos", repo_type="dataset", revision="main", local_dir="SoccerNet-BallActionSpotting-Videos")

  Download specific subsets





  Download 224p/720p versions

from huggingface_hub import snapshot_download

Download the 224p… See the full description on the dataset page: https://huggingface.co/datasets/OpenSportsLab/SoccerNet-BallActionSpotting-Videos.

Steps to download the data

Installing necessary library

poetry add huggingface_hub

Using the library to download the data into desired directory

from huggingface_hub import snapshot_download path = "./data" # path you want to store your data snapshot_download("Pulsifi/job_embedding", repo_type="dataset", local_dir=path)

A dataset of MIDI images designed for use with diffusion models for music generation, music classification, text-to-music and other purposes

  🤗 Check out Imagen MIDI Images LIVE demo on Hugging Face Spaces 🤗








  Installation

from huggingface_hub import snapshot_download

repo_id = "asigalov61/MIDI-Images" repo_type = 'dataset'

local_dir = "./MIDI-Images"

snapshot_download(repo_id, repo_type=repo_type, local_dir=local_dir)

  MIDI Images… See the full description on the dataset page: https://huggingface.co/datasets/asigalov61/MIDI-Images.

Vector Database

This is a chromadb based vector database containing indexed embeddings from the hugging face dataset: "Somnath01/Birds_Species".

  Usage

import chromadb from huggingface_hub import snapshot_download

vector_db_path = snapshot_download( repo_id=vector_dataset.hf_dataset_path, repo_type="dataset" )

client = chromadb.PersistentClient( path=os.path.join(vector_db_path… See the full description on the dataset page: https://huggingface.co/datasets/imageomics/bird-dataset-vector.

BLIP3o Pretrain JourneyDB Dataset

This collection contains 4 million JourneyDB images.

  Download

from huggingface_hub import snapshot_download

snapshot_download( repo_id="BLIP3o/BLIP3o-Pretrain-JourneyDB", repo_type="dataset" )

  Load Dataset without Extracting

You don’t need to unpack the .tar archives, use WebDataset support in 🤗datasets instead: from datasets import load_dataset import glob

data_files = glob.glob("/your/data/path/*.tar")… See the full description on the dataset page: https://huggingface.co/datasets/BLIP3o/BLIP3o-Pretrain-JourneyDB.

SOS-SFC-200K Dataset Splits

These are the dataset splits for the paper SOS: Synthetic Object Segments Improve Detection, Segmentation, and Grounding. The SOS-SFC-200K collection contains 200k samples, each with:

Segmentation masks
Bounding boxes

All annotations are provided in COCO format.

  Download & Extraction

Clone or download the entire repository.

from huggingface_hub import snapshot_download

snapshot_download( repo_id="weikaih/SOS-SFC-200K"… See the full description on the dataset page: https://huggingface.co/datasets/weikaih/SOS-SFC-200K.

SOS-FC-1M Dataset Splits

These are the dataset splits for the paper SOS: Synthetic Object Segments Improve Detection, Segmentation, and Grounding. The SOS-FC-1M collection contains one million samples, each with:

Segmentation masks
Bounding boxes
Referring expressions

All annotations are provided in COCO format.

  Download & Extraction

Clone or download the entire repository.

from huggingface_hub import snapshot_download

snapshot_download(… See the full description on the dataset page: https://huggingface.co/datasets/weikaih/SOS-FC-1M.

from huggingface_hub import snapshot_download snapshot_download( repo_id="wambugu71/crop_leaf_diseases_vit", local_dir="crop_leaf_model", local_dir_use_symlinks=False )

BLIP3o Pretrain Long-Caption Dataset

This collection contains 27 million images, each paired with a long (~120 token) caption generated by Qwen/Qwen2.5-VL-7B-Instruct.

  Download

from huggingface_hub import snapshot_download

snapshot_download( repo_id="BLIP3o/BLIP3o-Pretrain-Long-Caption", repo_type="dataset" )

  Load Dataset without Extracting

You don’t need to unpack the .tar archives, use WebDataset support in 🤗datasets instead: from datasets import… See the full description on the dataset page: https://huggingface.co/datasets/BLIP3o/BLIP3o-Pretrain-Long-Caption.

ACDC Dataset

This is a pre-processed version of the original Automated Cardiac Diagnosis Challenge (ACDC) dataset. Short-axis view images have been resampled to 1mm x 1mm x 10mm. Images have also been center cropped at the left ventricle mask center. The size of each slice is 192 x 192 pixels. Download the dataset using the following command: from huggingface_hub import snapshot_download data_dir = snapshot_download(repo_id="mathpluscode/ACDC", allow_patterns=["*.nii.gz", "*.csv"]… See the full description on the dataset page: https://huggingface.co/datasets/mathpluscode/ACDC.

SOS-FC-Object-Segments-10M

These are the dataset splits for the paper SOS: Synthetic Object Segments Improve Detection, Segmentation, and Grounding. This dataset contains over 10M object segments in Frequeny-Category (FC) splits.

  Download & Extraction

Clone or download the entire repository.

from huggingface_hub import snapshot_download

snapshot_download( repo_id="weikaih/SOS-FC-Object-Segments-10M", repo_type="dataset"… See the full description on the dataset page: https://huggingface.co/datasets/weikaih/SOS-FC-Object-Segments-10M.

Matter 0.1

Curated top quality records from 35 other datasets. Extracted from prompt-perfect This is just a consolidation of all the score 5s. Fine-tuning models with various subsets and combinations to create a best performing v1 dataset

  ~1.4B Tokens, ~2.5M records

Dataset has been deduped, decontaminated with bagel script from Jon Durbin Download using the below command to avoid unecessary files from huggingface_hub import snapshot_download… See the full description on the dataset page: https://huggingface.co/datasets/0-hero/Matter-0.1.

Original dataset has been acquired through the following link : https://zenodo.org/records/8370566 The dataset is not cleaned yet and any contributions are welcome 🤗

  download instructions

from huggingface_hub import snapshot_download snapshot_download(repo_id="tunis-ai/TunSwitch",repo_type="dataset",local_dir=".")

  Information

This repo contains the data used to develop and test the Tunisian Arabic Automatic Speech Recognition model developed in the following paper : A.… See the full description on the dataset page: https://huggingface.co/datasets/tunis-ai/TunSwitch.

This is BLIP3o-60k Text-to-Image instruction tuning dataset distilled from GPT-4o, including the following categories:

JourneyDB Human (including MSCOCO with human caption, human gestures, occupations) Dalle3 Geneval (no overlap with test set) Common objects Simple text

Here we provide the code guidance to download tar file: from huggingface_hub import snapshot_download snapshot_download(repo_id='BLIP3o/BLIP3o-60k', repo_type=‘dataset’)

And you can use huggingface datasets to read the tar… See the full description on the dataset page: https://huggingface.co/datasets/BLIP3o/BLIP3o-60k.

How to install?

!pip install datasets -q from huggingface_hub import snapshot_download import pandas as pd import matplotlib.pyplot as plt

First step: download an entire datatset

snapshot_download(repo_id="Aborevsky01/CLEVR-BT-DB", repo_type="dataset", local_dir='path-to-your-local-dir')

Second step: unarchive the images for VQA

!unzip [path-to-your-local-dir]/[type-of-task]/images.zip

Example of the triplet (image - question -… See the full description on the dataset page: https://huggingface.co/datasets/Aborevsky01/CLEVR-BT-DB.

GenRef-CoT

We provide 227K high-quality CoT reflections which were used to train our Qwen-based reflection generation model in ReflectionFlow [1]. To know the details of the dataset creation pipeline, please refer to Section 3.2 of [1].

  Dataset loading

We provide the dataset in the webdataset format for fast dataloading and streaming. We recommend downloading the repository locally for faster I/O: from huggingface_hub import snapshot_download

local_dir =… See the full description on the dataset page: https://huggingface.co/datasets/diffusion-cot/GenRef-CoT.

Annotated MIDI Dataset

  Comprehensive annotated MIDI dataset with original lyrics, lyrics summaries, lyrics sentiments, music descriptions, illustrations, pre-trained MIDI classification model and helper Python code









  Annotated MIDI Dataset LIVE demos

Music Sentence Transformer Advanced MIDI Classifer Descriptive Music Transformer

  Installation

from huggingface_hub import snapshot_download

repo_id = "asigalov61/Annotated-MIDI-Dataset"… See the full description on the dataset page: https://huggingface.co/datasets/asigalov61/Annotated-MIDI-Dataset.