78 datasets found

The dataset used in the paper is a collection of autonomous vehicle scenarios, where each scenario is represented as a 3x3 grid encoding. The scenarios are encoded using Pauli-X...

The Animals with Attributes (AwA) dataset consists of 50 classes of animals, with 30,475 images in total.

The Drawing Recognition dataset is used to recognize drawings.

Image Recognition FeedbackLog

The proposed classifier consists of several stages for feature representation, a DS layer to construct mass functions, and an expected utility layer to make set-valued...

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

A set of binary images of 9 classes, including bird, camel, children, elephant, fork, hammer, key, ray and turtle.

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

DollarStreet dataset includes 38k images of household objects across homes from 54 countries and with different income levels.

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

Self-Attention Networks dataset is used for image recognition tasks.

Inception V2 is a deep neural network for image recognition. It uses a convolutional neural network (CNN) architecture and is trained on a large dataset of images.

The iNaturalist dataset is a crowdsourced compendium of living organisms, with fine- and coarse-grained species distinctions, a heavy-tailed class size distribution, and...

A dataset for CAPTCHA recognition

A dataset for CAPTCHA recognition

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

The Stanford40 and HII datasets are used for image recognition. The Stanford40 dataset contains 40 action categories, while the HII dataset contains 10 action images.

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 dataset used for image recognition using deep convolutional neural networks.