660 datasets found

The ImageNet-10 dataset is a subset of the ImageNet-1K dataset, containing images from 10 classes.

Large scale datasets [18, 17, 27, 6] boosted text conditional image generation quality. However, in some domains it could be difficult to make such datasets and usually it could...

The dataset used for training and validation of the proposed approach to combine semantic segmentation and dense outlier detection.

The Visual Domain Decathlon Benchmark consists of 10 image classification tasks that have been explicitly selected to represent different domains.

The dataset used in the paper is Tiny ImageNet and ImageNet.

The dataset used in the paper is CIFAR-10 and ImageNet-100.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used CIFAR-10, CIFAR-100, and ImageNet datasets.

UCMerced land use dataset for remote sensing image scene classification

The Microsoft COCO dataset was used for training and evaluating the CNNs because it has become a standard benchmark for testing algorithms aimed at scene understanding and...

A benchmark for low-shot recognition was proposed by Hariharan & Girshick (2017) and consists of a representation learning phase without access to the low-shot classes and a...

The DOTA dataset is a large aerial image dataset for oriented objects detection which contains 2806 images with the size ranges from 800 × 800 to 4000 × 4000 and 188282...

The dataset used in the paper is the ImageNet-21k dataset, which is a large-scale hierarchical image database.

The Visual Anomaly (VisA) dataset is a large-scale industrial anomaly detection dataset containing 10,821 high-resolution color images with 9,621 normal and 1,200 anomalous...

The dataset used in this paper is a notMNIST dataset, which is a binary image dataset.

The dataset used in this paper is a Caltech-101 dataset, which is a dataset of images.

The MNIST dataset (Lecun et al., 1998), which consists of 60,000 gray-scale images of handwritten digits. Each image has an accompanying label in {0, 1,..., 9}, and is stored as...

The CIFAR-10 dataset consists of 60,000 training images and 10,000 test images. Each image is a 32×32 color image.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used it for language modeling and image classification tasks.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used the CLIP model and the CIFAR-10 and ImageNet datasets.

Large scale datasets [18, 17, 27, 6] boosted text conditional image generation quality. However, in some domains it could be difficult to make such datasets and usually it could...