660 datasets found

MiniImageNet, Caltech-UCSD Birds 200-2011, TieredImageNet, OfficeHome

AWA2 is an animal dataset containing 37,322 images from 50 classes, with 85 attributes provided by experts to describe the semantic feature of each class.

The CUB dataset is a collection of images of landbirds and waterbirds from the CUB dataset, combined with images from Places dataset as background.

BigEarthNet is a large-scale Sentinel-2 dataset collected from a total of 125 Sentinel-2 tiles covering areas of 10 countries in Europe. The dataset was prepared with data from...

The dataset used in the paper is ImageNet-22k, a large-scale image classification dataset.

Visual recognition in a low-data regime is challenging and often prone to overfitting. To mitigate this issue, several data augmentation strategies have been proposed. However,...

Polarimetric synthetic aperture radar (PolSAR) images are widely used in disaster detection and military reconnaissance and so on. However, their interpretation faces some...

A dataset for camera model identification using convolutional neural networks.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used the ImageNet dataset and a subset of the CC12M dataset for training.

The miniImagenet and tieredImageNet datasets are used for few-shot classification experiments.

The dataset used for training and testing Binarized Convolutional Neural Network architectures (LeNet-4, Network-In-Network, AlexNet) on various datasets.

Three public datasets were employed throughout the experiments: MNIST, Fashion-MNIST, and Kuzushiji-MNIST.

The MNIST dataset is a well-known image dataset for the 10-class classification problem. Following the evaluation setting of existing works, we transform this classification...

The ImageNet dataset is a large-scale image database that contains over 14 million images, each labeled with one of 21,841 categories.

The dataset used in the paper is CIFAR-10 and NIPS-W, which are benchmark datasets for image classification and adversarial attacks.

The dataset used in this paper is CINIC-10, a dataset compiled by combining downsampled images from the Imagenet database with CIFAR-10.

The Galaxy Zoo dataset is used to test the ability of TARGET-VAE to predict rotation and translation of objects in images.

The JFT-300M dataset is used for training and evaluation of the proposed Circulant Channel-Specific (CCS) token-mixing MLP.

The ImageNet21K dataset is used for training and evaluation of the proposed Circulant Channel-Specific (CCS) token-mixing MLP.

The dataset contains 30 multi-instance classification datasets generated from the MNIST, FashionMNIST, and KuzushijiMNIST datasets.