660 datasets found

The Oracle-MNIST dataset is used to evaluate the proposed architecture for low-resolution image classification tasks.

The dataset used in the paper is a collection of images from the SVHN dataset, along with labels. The dataset is used for image classification.

The CIFAR-10 and CIFAR-100 datasets are used to evaluate the performance of the commentaries curriculum.

Oxford 102 Flowers is a dataset of images of different flower species.

The DomainNet dataset is a benchmark for domain generalization, containing 100,000 images from 6 domains: Clipart, Infograph, Real, Painting, Quickdraw, and Sketch.

The OfficeHome dataset is a benchmark for domain generalization, containing 13,000 images from 6 domains: Clipart, Product, Real World, Art, Product, and Real World.

The dataset used for image classification task, featuring synthetic signatures.

The dataset used for image classification task, featuring high-resolution scans of medieval manuscripts.

The dataset used for image classification task, featuring images from histologic sections, dermatoscopic images, high-resolution scans of historical documents and natural images.

The dataset used in the paper is CIFAR10 and CelebA 64x64.

The Scaled and Translated Image Recognition (STIR) dataset contains four different tasks, which classify emojis, handwritten digits, traffic signs and aerial scenes, respectively.

The CIFAR10 and MNIST datasets are used for image classification tasks.

The Permuted MNIST dataset is a variation of MNIST where new tasks of comparable difficulty to the original MNIST classification task are created by permuting the pixels of...

The Rotated MNIST dataset is a subset of the MNIST dataset with images of handwritten digits rotated by 90 degrees.

Structural Vision Transformer (StructViT) is a vision transformer network that leverages structural self-attention (StructSA) to capture correlation structures in images and...

The dataset used in the paper is CIFAR-10 and CIFAR-100, and CUB-200.

The dataset used in the paper is Caltech101, which is a natural image classification dataset. It contains 101 categories of natural images.

ILSVRC is a large-scale image dataset containing over 1.2 million images across 1,000 classes.

The MNIST dataset consists of 60,000 training samples and 10,000 test samples. Each sample is a 28×28 pixel grayscale handwritten digital image.

A novel deep learning framework based on Fourier neural operators for classifying images with different sizes