492 datasets found

The CifarQuick model is a medium-sized convolutional neural network trained on the Cifar-10 dataset.

The dataset consists of 1000 images of 28x28 grayscale images of handwritten digits.

The Stanford CS229 dataset consists of 60,000 images of 28x28 grayscale images of handwritten digits.

Fashion-MNIST, KMNIST and SVHN datasets used for benchmarking GANs

The Landmarks-User-160k dataset is a collection of 164,172 images of 2,028 landmarks from 1,262 users.

The Imagenette dataset used in the paper for class density and dataset quality in high-dimensional, unstructured data.

Human vision is naturally more attracted by some regions within their field of view than others. This intrinsic selectivity mechanism, so-called visual attention, is influenced...

The dataset used in this paper is a pre-trained DNN trained on the Cifar10 and Cifar100 datasets.

The dataset used in this paper is VGG5 and ResNet20, which are commonly used architectures for image classification tasks.

Spatial pattern templates for recognition of objects with regular structure.

Generated ImageNet-100 data for training ResNet-18

Breast cancer dataset where mammograms have been labeled independently by three doctors. Ground-truth has been obtained through a biopsy, not available to the algorithm nor the...

ImageNet Subsets

The dataset used in the paper is ResNet-20, a deep convolutional neural network for image classification.

The dataset used in the paper is MNIST, CIFAR10 and STL10. These are datasets for image classification tasks.

The Digits-DG dataset contains 4 datasets: MNIST, MNIST-M, SVHN, and SYN.

The OfficeHome dataset contains 15,500 images of 65 classes from four domains: Art, Clipart, Product, and Real-World.

The dataset used for domain generalization, including PACS, OfficeHome, and Digits-DG datasets.

A dataset of 9,500 256x256 color images of 20 animal breeds.

The dataset used in the paper is the CIFAR10 and SVHN datasets, which are used to evaluate the performance of the robust models.