47 datasets found

The authors used the CIFAR-10 and Fashion-MNIST datasets for semi-supervised federated learning-based UAV image recognition tasks.

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

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.

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 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.

The dataset used for image recognition using deep convolutional neural networks.

Conformer is a dual network structure that combines CNN-based local features with transformer-based global representations for enhanced representation learning.

The PASCAL VOC dataset contains 20 classes, including person, animal, vehicle, and indoor, with 9,963 images containing 24,640 annotated objects.

The MIT Indoor Scene Recognition dataset contains 67 categories of indoor scenes.

The dataset used in this paper is the VGG Network E, a deep convolutional neural network for image recognition.

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...

Deep Image: Scaling up image recognition

A dataset of images of holy places (Kaaba, Zamzam, Maqam Ibrahim) for training a deep learning model.