564 datasets found

The dataset used in the paper is Caltech101, which is a natural image classification dataset. It contains 101 categories of natural images.

The dataset used in this paper is a neural network, and the authors used it to test the performance of their lookahead pruning method.

The dataset is a 4-way array of dimensions 4000 × 90 × 90 × 3, where each pixel gives the intensity for colors red, green and blue, resulting in a multiway array of dimensions X...

The dataset used in the paper is the ImageNet-C dataset, which is a dataset of images corrupted with various types of noise and occlusions.

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.

The dataset used in the paper is not explicitly described, but it is mentioned that it is a large-scale medical image segmentation dataset.

A 1k classes classification task with 1.2M training examples and 50k validation examples. The examples are colour images of various sizes.

The dataset used in this paper is not explicitly described, but it is mentioned that it contains four DAGs representing well-known exposure-outcome relationships in the medical...

The dataset used in the paper is not explicitly described. However, it is mentioned that the authors used a range of datasets, including CIFAR-10, GTSRB, Google Speech Command,...

Optical flow estimation using a deep lightweight network

The dataset used in the paper is a mixture of Gaussians, CIFAR-10, STL-10, CelebA, and ImageNet.

The dataset used in this paper is a ResNet50 model, which is a convolutional neural network. The model is used to evaluate the effects of quantization on the compute...

FMNIST is a dataset of 28x28 grayscale images of handwritten digits.

The Lung Image Database Consortium (LIDC) dataset contains 1609 2D thoracic CT scans belonging to 214 subjects.

MobileNet is a compact network working accurately and efficiently based on light-weight module of separable convolution layers.

The dataset used in this paper for data-free network quantization.

The dataset is used for oceanic eddy detection and classification from Sea Surface Height (SSH) maps.

MRI images super-resolution reconstruction using deep learning techniques

MRI images super-resolution reconstruction using deep learning techniques

The dataset used in the paper is a set of dual-energy CT sinograms and reconstructed images.