46 datasets found

CIFAR-10-C and CIFAR-100-C are robustness benchmarks consisting of 19 corruptions types with five levels of severities.

The Office-10 dataset is a more classic benchmark dataset from [17].

The ImageCLEF-DA1 dataset is also a benchmark dataset for domain adaptation, which contains 12 categories shared by three public datasets, Caltech- 256 (C), ImageNet ILSVRC 2012...

The Office-31 dataset is a standard benchmark for domain adaptation from [16], comprising 4,652 images and 31 categories collected from three distinct domains: Amazon (A), Webcam...

The DenseNet dataset is a dataset for image recognition on handwritten digits composed of 60,000 training data and 10,000 test images.

The dataset used in this paper is the ResNet-50 dataset.

ImageNet trained PyTorch models are evaluated under various simple image transformations.

The dataset used in this paper is a collection of images from the STL-10 dataset, preprocessed and used for training and evaluation of the proposed diffusion spectral entropy...

The dataset used in the paper is CIFAR100, ImageNet100, and ImageNet. CIFAR100 consists of 100 object classes and 60,000 images. ImageNet100 has 100 object classes and 60,000...

The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) dataset is a large-scale image classification dataset containing over 14 million images from 21,841 categories.

The dataset used in the paper is a large collection of small images, each representing a patch of a jigsaw puzzle. The patches are of the same size and orientation, and the goal...

The PASCAL VOC dataset contains 20 classes, including person, animal, vehicle, and indoor, with 9,963 images containing 24,640 annotated objects.

The dataset used in the paper is the YFCC-100M dataset, a large-scale image dataset.

The dataset used for image classification tasks, including ImageNet-1K, Food-101, Birds, and Dogs.

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

The Street View House Numbers (SVHN) dataset used consist of 32x32 10,000 labelled image pool, 30,000 unlabelled pool and 26,032 testing pool.

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

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

The dataset used in the paper is Places, a large dataset of 400k pairs of images from the Places 205 dataset and corresponding spoken audio captions.