564 datasets found

Segmentation is essential for medical image analysis to identify and localize diseases, monitor morphological changes, and extract discriminative features for further diagnosis....

The dataset covers four fruit types (avocado, kiwi, persimmon, and mangos). Ripeness is classified into three categories (firmness, sweetness, and overall ripeness).

Hyperspectral recordings approximate the spectrum for each pixel of the image. Therefore, the number of channels is increased (to around 200), and the range of recorded...

The dataset used in the paper is the VGG dataset, which is a collection of images used for training and testing VGG models.

A deep learning-based color image steganography scheme combining convolutional autoencoders and ResNet architecture.

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

The dataset used in the paper is a collection of neural networks trained on different tasks, including scalar functions, two-dimensional vector fields, and images of a rotating...

Automated segmentation of vertebrae on lateral chest radiography using deep learning

Deepercut: A deeper, stronger, and faster multi-person pose estimation model

SatlasPretrain: A large-scale dataset for remote sensing image understanding

The dataset used in the paper is the CIFAR-10 and CIFAR-100 datasets, and the SVHN dataset.

3D bounding box estimation using deep learning and geometry.

DMLab-30 is a benchmark for multitask reinforcement learning in partially observable environments.

Manga109 dataset is a benchmark for Single Image Super-Resolution (SISR) tasks. It contains 109 manga images with randomly selected reference images.

The Urban100 dataset is a benchmark for image denoising, containing 100 images with varying levels of noise.

Single image super-resolution (SR) is the process of infer-

Brain MR dataset for tumor classification and source type classification

The dataset used in the paper is a collection of trained networks and their corresponding datasets.

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

The dataset used in the paper is Tiny-ImageNet-200, which consists of 100k training, 10k validation, and 10k test images of dimensions 64x64x3.