29 datasets found

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

The DenseNet dataset is a dataset for image recognition on handwritten digits composed of 60,000 training data and 10,000 test images.

The dataset consists of 60,000 images of objects in 200 categories, with 300 images per category.

The dataset used in the paper for attention-oriented data analysis and attention-based adversarial defense.

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 mini-ImageNet dataset is a subset of the ImageNet dataset, containing 60,000 images from 100 classes.

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 dataset consists of binary images of sprites with variations in the shape (oval, square, and heart) and four geometric factors: scale (6 variation modes), rotation (40), and...

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

The street view house number recognition task involves transcribing an image with house numbers to a string of digits.

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.

Training neural networks on image datasets generally require extensive experimentation to find the optimal learning rate regime.

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.

The paper presents a method for reading digits in natural images using unsupervised feature learning.

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.

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