74 datasets found

The authors used Tiny-ImageNet, CIFAR100, and CIFAR10 datasets for their experiments.

MERCURY accelerates DNN training by exploiting input similarity.

A comprehensive dataset featuring ten distinct hen breeds, capturing unique characteristics and traits of each breed.

The dataset contains protest images from South Korea and Hong Kong.

Image data provide unique information about political events, actors, and their interactions which are difficult to measure from or not available in text data.

A novel MicroISP model designed specifically for edge devices, taking into account their computational and memory limitations.

A novel MicroISP model designed specifically for edge devices, taking into account their computational and memory limitations.

The dataset used for training and testing the proposed deep single-image intrinsic decomposition model.

Human pose estimation refers to the task of recognizing postures by localizing body keypoints (head, shoulders, elbows, wrists, knees, ankles, etc.) from images.

A deep learning and IoT based computer vision system to process computer vision and natural language in real time in order to address the safety and security of production sites...

The dataset used in the paper to prove the representation defects of a cascaded convolutional decoder network in frequency representation.

The DeepViT dataset is a dataset for vision.

The dataset used for training and testing the proposed method.

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.

Lung nodule malignancy prediction has been enhanced by advanced deep-learning techniques and effective tricks. Nevertheless, current methods are mainly trained with...

Places205 is a dataset of 2.5 million images from 205 categories, with 12,000 images per category.

Brain tumor segmentation is a critical task for patient’s disease management. In order to automate and standardize this task, we trained multiple U-net like neural networks,...

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used VGG-16, ResNet-50, and MobileNet-v2 models on the ImageNet and CIFAR-10...

The dataset used in the paper is the MobileNetV2 dataset, which is a pre-trained deep neural network model.