492 datasets found

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 COCO test-dev dataset is used for instance segmentation. It contains 20k test-dev images.

The dataset used in the paper is CIFAR-10 and CIFAR-100, and CUB-200.

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

ILSVRC is a large-scale image dataset containing over 1.2 million images across 1,000 classes.

The MNIST dataset consists of 60,000 training samples and 10,000 test samples. Each sample is a 28×28 pixel grayscale handwritten digital image.

A novel deep learning framework based on Fourier neural operators for classifying images with different sizes

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 this work is a custom synthetic dataset generated using the liquid-dsp library, containing 600000 examples of each of 13.8 million examples, with SNRs...

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 the paper is YFCC100M, a large-scale video dataset. The dataset is used for foreground and background patch extraction and object recognition tasks.

Food-101 dataset is a large-scale dataset for food image classification, with 101 classes and over 30,000 images.

Fine-Grained Visual Classification (FGVC) is an important computer vision prob-lem that involves small diversity within the different classes, and often requires expert...

Medical image analysis is a hot research topic because of its usefulness in different clinical applications, such as early disease diagnosis and treatment. Convolutional neural...

Texture classification is an important and challenging problem in many image processing applications.

The dataset used in the paper is ImageNet-5, ImageNet-20, and ImageNet-100, which are subsets of the ImageNet dataset.

NWPU-RESISC45 dataset was collected from more than 100 countries and regions in the world, consists of 31,500 remote sensing images.

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.