Model Hub

Dataset Card for OpenFake OpenFake is a dataset and benchmark for detecting AI-generated images, with a focus on politically and socially salient content where misinformation risk is highest. It pairs real photographs with synthetic counterparts produced by a wide range of frontier proprietary generators, open-source diffusion models, and community fine-tunes. A separate in-the-wild test set is sourced from Reddit to evaluate detector performance on naturally circulated synthetic… See the full description on the dataset page: https://huggingface.co/datasets/ComplexDataLab/OpenFake.

Model Card for HEST-1k What is HEST-1k? A collection of 1,276 spatial transcriptomic profiles, each linked and aligned to a Whole Slide Image (with pixel size < 1.15 µm/px) and metadata. HEST-1k was assembled from 180 public and internal cohorts encompassing: 26 organs 2 species (Homo Sapiens and Mus Musculus) 398 cancer samples from 25 cancer types. HEST-1k processing enabled the identification of >1.5 million expression/morphology pairs and >76 million nuclei… See the full description on the dataset page: https://huggingface.co/datasets/MahmoodLab/hest.

Dataset Card for MVTec AD This dataset originates from MVTec but is provided in a different format. You can easily load it using FiftyOne The total number of samples remains the same as the original: 5,354. Installation If you haven't already, install FiftyOne: pip install -U fiftyone Usage import fiftyone as fo import fiftyone.utils.huggingface as fouh # 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/mvtec-ad.

Dataset Card for Food-101 Dataset Summary This dataset consists of 101 food categories, with 101'000 images. For each class, 250 manually reviewed test images are provided as well as 750 training images. On purpose, the training images were not cleaned, and thus still contain some amount of noise. This comes mostly in the form of intense colors and sometimes wrong labels. All images were rescaled to have a maximum side length of 512 pixels. Supported Tasks and… See the full description on the dataset page: https://huggingface.co/datasets/ethz/food101.

Dataset Card for CIFAR-100 Dataset Summary The CIFAR-100 dataset consists of 60000 32x32 colour images in 100 classes, with 600 images per class. There are 500 training images and 100 testing images per class. There are 50000 training images and 10000 test images. The 100 classes are grouped into 20 superclasses. There are two labels per image - fine label (actual class) and coarse label (superclass). Supported Tasks and Leaderboards image-classification: The… See the full description on the dataset page: https://huggingface.co/datasets/uoft-cs/cifar100.

Dataset Card for DASP Dataset Description The DASP (Distributed Analysis of Sentinel-2 Pixels) dataset consists of cloud-free satellite images captured by Sentinel-2 satellites. Each image represents the most recent, non-partial, and cloudless capture from over 30 million Sentinel-2 images in every band. The dataset provides a near-complete cloudless view of Earth's surface, ideal for various geospatial applications. Images were converted from JPEG2000 to JPEG-XL to… See the full description on the dataset page: https://huggingface.co/datasets/RichardErkhov/DASP.

Stanford Cars Dataset Dataset Overview Splits: Training: 8144 images used for model training. Test: 8041 images used for evaluation. Contrast: 8041 images with high contrast for robustness testing. Gaussian Noise: 8041 images corrupted by Gaussian noise for robustness testing. Impulse Noise: 8041 images corrupted by impulse noise for robustness testing. JPEG Compression: 8041 compressed images for robustness testing. Motion Blur: 8041 images with motion blur for… See the full description on the dataset page: https://huggingface.co/datasets/tanganke/stanford_cars.