78 datasets found

The SYSU-MM01 dataset contains 491 identities captured by 4 visible cameras and 2 infrared cameras both including indoor and outdoor environments.

The dataset used in the paper is a collection of images from the SVHN dataset, along with labels. The dataset is used for image classification.

The VGG-16 dataset is a large collection of images of objects.

ILSVRC is a large-scale image dataset containing over 1.2 million images across 1,000 classes.

The MNIST database of handwritten digits is a popular benchmark data set for classification algorithms.

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 dataset used in the paper is a benchmark dataset for vision recognition, consisting of 10 datasets: Tiny ImageNet, Caltech-256, Flowers-102, Food-101, CIFAR-100, CIFAR-10,...

The dataset used in this paper is a neural network, and the authors used it to test the performance of their lookahead pruning method.

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.

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.

An image is worth 16x16 words: Transformers for image recognition at scale.

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...

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 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.

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...