660 datasets found

The dataset is used for classification of histopathology images of lung cancer using Convolutional Neural Network (CNN).

The existing contrastive learning methods widely adopt one-hot instance discrimination as pretext task for self-supervised learning, which inevitably neglects rich...

The USPS dataset consists of 9298 images of handwritten digits 0-9 (10 classes) of 16x16 pixels in gray scale.

The proposed Multi-label Transformer architecture is designed for multi-label image classification, combining pixel attention and cross-window attention to better excavate the...

The dataset used in the paper is the dSprites dataset, which contains 2D-shape binary images with a size of 64×64, synthetically generated with five independent factors: shape...

The dataset consists of binary images of sprites with variations in the shape (oval, square, and heart) and four geometric factors: scale (6 variation modes), rotation (40), and...

The dataset used in this paper is ImageNet and CIFAR-10 for image classification, and Cityscapes for semantic segmentation.

The ImageNet-32 dataset is a subset of the ImageNet dataset, containing 1,281,167 training samples and 50,000 test samples, distributed across 1,000 labels.

The CIFAR-10 dataset is a collection of 60,000 32x32 color images in 10 classes, and the STL-10 dataset is a collection of 90,000 96x96 color images in 10 classes.

The MNIST dataset is a collection of 70,000 28x28 grayscale images of handwritten digits, and the USPS dataset is a collection of 9,298 16x16 grayscale images of handwritten...

The dataset used in the paper to evaluate the alignment of deep neural networks with human perception.

The SMMNIST dataset is an extension of the MNIST dataset, where the digit images form a temporal sequence, creating dynamic scenes as a result of random movements of the...

The dataset used in the paper is a collection of images of objects with varying viewpoints, used for training and testing the proposed RRB framework.

The dataset used in the paper is OxfordPets, a dataset of 33 animal categories.

Customer temporal behavioral data represented as images to perform churn prediction by leveraging deep learning architectures prominent in image classification.

The MNIST dataset is used for image classification, and the Lending Club Loan dataset is used for tabular data.

OpenImagesv5: A large-scale dataset for multi-label image classification.

The dataset used in the paper is Office-Home, DomainNet, CUB, and iWildCAM2020.

SqueezeNet is a small CNN that performs well on the ImageNet data set.

AlexNet by Krizhevsky et al. (2012), VGG by Simonyan & Zisserman (2015), GoogleNet (Szegedy et al., 2015) and ResNet (He et al., 2015) have seen state-of-the-art...