525 datasets found

Tiny Images dataset is a large dataset of images, each with a small size.

Privacy-preserving image classification method that uses ViT and a block-wise encryption method

FER2013 is a crowdsourced dataset. Each image was individually labeled by 40 taggers for the sake of rigor.

Large-scale vision-and-language models trained on curated and web-scrapped data have led to significant improvements over task-specific models when transferred to downstream...

The dataset used in the paper is the ILSVRC2012 dataset, which contains images of size 224x224.

The dataset used in the paper is a cross-domain dataset, consisting of six domains: Real, Painting, Sketch, Clipart, Infograph, and Quickdraw. Each domain contains 345 object...

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 dataset used in the paper is Tiny ImageNet and ImageNet.

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

Instance segmentation (IS) is an important computer vision task, aiming at simultaneously predicting the class label and the binary mask for each instance of interest in an image.

The Cityscapes dataset is a large and famous city street scene semantic segmentation dataset. 19 classes of which 30 classes of this dataset are considered for training and...

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.

Pre-trained language models (PrLM) have to carefully manage input units when training on a very large text with a vocabulary consisting of millions of words. Previous works have...

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

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.