492 datasets found

UC Merced Land Use Dataset (Yang & Newsam, 2010) contains remote sensing satellite images of 21 classes, with 100 images in each class.

The S-CIFAR-100 is constructed by splitting CIFAR-100 into 10 tasks where each one contains 10 classes and 6,000 images.

The S-CIFAR-10 is constructed by splitting CIFAR-10 into 5 sequential tasks where each task contain 2 classes and 12,000 images.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used the CIFAR-100 and ImageNet-1k datasets for image classification and semantic...

BigEarthNet-S2 is a large-scale benchmark archive for remote sensing image classification and retrieval.

The CARPK dataset is a large-scale aerial dataset for counting cars in parking lots. It contains images with a top-down view covering 4 different parking lots.

CAMELYON-17 consists of 145 positive slides and 353 negative slides, where positive patches occupying less than 10% of the tissue area in positive slides.

ImageNet-Subset is a subset of the ImageNet dataset, containing 100 classes with 500 training images, 50 validation images, and 50 test images per class.

The dataset used in the paper is CIFAR100 and ImageNet.

COVID-19 chest X-ray dataset is the largest open access benchmark dataset in terms of the number of COVID-19 positive patient cases.

The dataset used in the paper for attention-oriented data analysis and attention-based adversarial defense.

Three-dimensional cine-MRI dataset for cardiac segmentation and classification

The dataset used in the paper is the MobileNetV2 dataset, which is a pre-trained deep neural network model.

Augmented MNIST dataset with 1000 classes

WebVision-1000 is a large-scale web dataset for image classification, containing 2.4M noisy-labeled training images crawled from Flickr and Google.

Webly supervised learning (WSL) aims at training an optimal deep neural network Mθ from a dataset D = {(x1, y∗N )} collected from the Internet.

Generative adversarial nets (GANs) are a class of deep learning models that consist of two neural networks: a generator and a discriminator.

This dataset contains images of cats and dogs, which is used for training deep neural networks.

The Office-Home dataset is a visual domain adaptation task, where the goal is to adapt a model trained in a source domain to a target domain with different distributions.

ILSVRC 2012-2017 image classification and localization dataset used for cover image generation