21 datasets found

The MNIST dataset is a collection of images of handwritten digits, with size n = 70,000 and D = 784.

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 the LSUN bedrooms dataset, a large-scale image dataset.

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 this paper is the CIFAR10 dataset, which contains 60,000 32x32 color images in 10 classes, with 6,000 images per class.

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

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.

The dataset used in the paper is the Imagenet dataset, which is a large annotated dataset of images used for image classification tasks.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used a subset of the validation dataset used for the ImageNet Large Scale Visual...

Multi-label image recognition is a practical and challenging task compared to single-label image classification.

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

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.

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 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.