78 datasets found

The dataset used in this paper is a convolutional neural network (CNN) model, specifically ConvNeXt.

The dataset used in this paper is a convolutional neural network (CNN) model, specifically ResNet-34, and its layers 20 and 28.

Caltech-UCSD Birds (CUB 200-2007) and extended version CUB 200-2011 image collections tagged with keypoints, bounding boxes, coarse segmentation, and attribute labels.

The CIFAR-10 and Tiny ImageNet datasets are used to evaluate the robustness of the proposed defense method.

CIFAR-10-C and CIFAR-100-C are robustness benchmarks consisting of 19 corruptions types with five levels of severities.

Pascal Visual Object Classification challenge consist of 20 image classes, where each image was parsed into an image graph using keypoints as nodes.

The Imagenette dataset used in the paper for class density and dataset quality in high-dimensional, unstructured data.

The proposed approach is evaluated on three different datasets: MNIST, fashionMNIST, and CIFAR10.

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

Convolutional Neural Networks (CNNs) have achieved great success due to the powerful feature learning ability of convolution layers. Specifically, the standard convolution...

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

Skin Cancer detection and classification using CNN

ImageNet trained PyTorch models are evaluated under various simple image transformations.

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

ResNet and WRN datasets used for image classification tasks

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used the CIFAR-100 and ImageNet-1k datasets for image classification and semantic...

CAMELYON-17 consists of 145 positive slides and 353 negative slides, where positive patches occupying less than 10% of the tissue area in positive slides.

The dataset used in the paper is CIFAR100 and ImageNet.

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

Convolution is the main building block of convolutional neural networks (CNN). We observe that an optimized CNN often has highly correlated filters as the number of channels...