24 datasets found

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

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...

Binary neural networks have attracted numerous attention in recent years. However, mainly due to the information loss stemming from the biased binarization, how to preserve the...

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 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 not explicitly described, but it is mentioned that the authors used four widely used datasets: CIFAR10, CIFAR100, SVHN, and ImageNet.

Data augmentation methods have played an important role in the recent advance of deep learning models, and have become an indispensable component of state-of-the-art models in...

The dataset used in the paper is Occluded CIFAR.

The dataset used in the paper is Cluttered MNIST and CIFAR-10.

The dataset used in the paper to evaluate the alignment of deep neural networks with human perception.

The dataset used in this paper is a large variety of popular CNN models, such as straight-forward, complicated-connected, and grouped architectures.

Real-time intelligent detection and prediction of subjects' behavior particularly their movements or actions is critical in the ward.

The dataset used in the paper is the ResNet-50 dataset, a convolutional neural network model.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used DeiT and ViT models on ImageNet-1k and CIFAR-100 datasets.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used DeiT and ViT models on ImageNet-1k and CIFAR-100 datasets.

The proposed SSIMLayer is a new nonlinear computational layer of high learning capacity to the deep convolutional neural network architectures.

Dataset for waste classification using computer vision and deep learning

Deep Neural Networks (DNNs) are universal function approximators providing state-of-the-art solutions on wide range of applications. Common perceptual tasks such as speech...

The dataset used in this paper is a MobileNetV2 model, which is a type of deep neural network. The dataset is used to evaluate the performance of the proposed heterogeneous system.