492 datasets found

MobileNet is a compact network working accurately and efficiently based on light-weight module of separable convolution layers.

The dataset used in this paper for data-free network quantization.

The ISIT-UMR Colonoscopy Dataset is a dataset used for colonoscopy image classification, consisting of images of colonoscopy and polyps.

The CPC-Paired Dataset is a dataset used for colorectal polyp classification, consisting of paired white-light (WL) and narrow-band imaging (NBI) images.

A large-scale dataset of dermoscopic images for skin lesion diagnosis.

The dataset is a benchmark for self-supervised learning on long-tailed data. It contains 115.8K images with 1000 categories, ranging from 1,280 to 5 in terms of class cardinality.

Semantic segmentation is a crucial and challenging task for image understanding. It aims to predict a dense labeling map for the input image, which assigns each pixel a unique...

Object detection is one of the most foundational computer vision task and is essential for many real-world applications. The object detection pipeline has been developed...

Recent studies show that Transformer has strong capability of building long-range dependencies, yet is incompetent in capturing high frequencies that predomi- nantly convey...

The Galaxy10 dataset is used in the paper to evaluate the performance of the SE(2)-CNN and Sim(2)-CNN.

The Modified National Institute of Standards and Technology database (MNIST), Canadian Institute For Advanced Research (CIFAR) and Street View House Numbers (SVHN) datasets are...

The PASCAL VOC 2010 dataset is an extension of the PASCAL VOC dataset, containing additional images and categories.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors conducted experiments on various vision tasks, including image classification,...

The dataset used in the paper is HAM10000, which is a dermoscopic image classification dataset. It contains 10,015 dermoscopic images categorized into 7 lesion types.

The dataset used in this paper is a Flickr image dataset, which is used to evaluate the proposed deep aesthetic feature learning framework.

VisDA-2017 is a simulation-to-real dataset with two extremely distinct domains: Synthetic renderings of 3D models and Real collected from photo-realistic or real-image datasets.

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

Object recognition is arguably the most important problem at the heart of computer vision. Recently, Barbu et al. introduced a dataset called ObjectNet which includes objects in...

The COCO dataset is a large-scale dataset for object detection, semantic segmentation, and captioning. It contains 80 object categories and 1,000 image instances per category,...

The dataset used in this paper is the WRN28x10 dataset, a deep neural network trained on the CIFAR-10 and CIFAR-100 datasets.