datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
DomainNetData downloaded from WILDS (Download, paper, project).
This dataset contains some copyrighted material whose use has not been specifically authorized by the copyright owners. In an effort to advance scientific research, we make this material available for academic research. We believe this constitutes a fair use of any such copyrighted material as provided for in section 107 of the US Copyright Law. In accordance with Title 17 U.S.C. Section 107, the material on this site is distributed… See the full description on the dataset page: https://huggingface.co/datasets/wltjr1007/DomainNet.mind2web_multimodal_test_domain
Dataset Card for "Cross-Domain" Test Split in Multimodal Mind2Web
Note: This dataset is the test split of the Cross-Domain dataset introduced in the paper.
This is a FiftyOne dataset with 4050 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset =… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/mind2web_multimodal_test_domain.Afrivoice_Kinyarwanda_Image_Domain_classification
Dataset Description
This dataset is a restructured version of Afrivoice Kinyarwanda, reorganized for image domain classification. The original audio-and-image manifest data was regrouped into a standard Hugging Face imagefolder layout (train/validation/test splits, one subfolder per class) so it can be loaded directly with datasets.load_dataset("imagefolder", ...) for training image classifiers.
No new images were collected and no image content was modified beyond format… See the full description on the dataset page: https://huggingface.co/datasets/Kira-Floris/Afrivoice_Kinyarwanda_Image_Domain_classification.microcolony-domain-adaptationMicrocolony Domain Adaptation (Foodborne Bacteria) is a microscopy image dataset for foodborne bacterial classification under varying imaging conditions. It was created to support research in adversarial domain adaptation, enabling models trained on standard phase contrast microscopy images to generalize across different optical configurations and biological conditions.
This dataset accompanies the publication: Bhattacharya, S., Wasit, A., Earles, M., Nitin, N., & Yi, J. (2025). Enhancing AI… See the full description on the dataset page: https://huggingface.co/datasets/food-ai-nexus/microcolony-domain-adaptation.DomainNetData downloaded from WILDS (Download, paper, project).
This dataset contains some copyrighted material whose use has not been specifically authorized by the copyright owners. In an effort to advance scientific research, we make this material available for academic research. We believe this constitutes a fair use of any such copyrighted material as provided for in section 107 of the US Copyright Law. In accordance with Title 17 U.S.C. Section 107, the material on this site is distributed… See the full description on the dataset page: https://huggingface.co/datasets/Zheng0309/DomainNet.