datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
wds_vtab-cifar10wds_vtab-cifar100decaf-rvos-davis17decaf-rvos-reasonvoswds_cifar100wds_cifar10CION-ALcivi50kCifar10Mnist
Cifar10Mnist Dataset Card
Dataset Summary
Cifar10Mnist is a synthetic image dataset created by overlaying MNIST digit images on top of CIFAR-10 images. Each example contains a 32x32 RGB image and a paired label tuple: the original CIFAR-10 class name plus the MNIST digit label.
Supported Tasks
Image classification
Multi-label classification
Transfer learning
Synthetic data research
Languages
Not language-specific… See the full description on the dataset page: https://huggingface.co/datasets/shotegni/Cifar10Mnist.KADIS700KNoisy-CIFAR-100KADISGlobalGeoTree
GlobalGeoTree Dataset
GlobalGeoTree is a comprehensive global dataset for tree species classification, comprising 6.3 million geolocated tree occurrences spanning 275 families, 2,734 genera, and 21,001 species across hierarchical taxonomic levels. Each sample is paired with Sentinel-2 image time series and 27 auxiliary environmental variables.
Dataset Structure
This repository contains three main components:
1. GlobalGeoTree-6M
Training dataset with around 6M… See the full description on the dataset page: https://huggingface.co/datasets/cindycui/GlobalGeoTree.DSIC-Cityscapes_dataThis is the Cityscapes dataset used to train the models in the GitHub repository https://github.com/abilalk02/D-SIC. The original complete dataset can be found here: https://www.cityscapes-dataset.com/.
The train and test datasets should be downloaded and saved as .tar files in the 'data/train/without_captions' and 'data/test/without_captions' folders in the GitHub repository respectively. D-SIC is trained without text caption conditioning. Thus, the .txt files are empty.
cineleum-training-data