320 datasets found

The Mutant and LEGO dataset is a dynamic scene dataset. It contains 90% images for training and 10% images for evaluation.

The Tanks and Temples Advanced (T&T) dataset is a benchmark dataset for image-based 3D reconstruction. It contains 90% images for training and 10% images for evaluation.

The Visual Wake Words (VWW) dataset consists of high-resolution images that include visual cues to 'wake-up' AI-powered home assistant devices.

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

The dataset used in the paper is ModelNet40 and ModelNet10, which are subsets of ShapeNet.

The dataset used in the paper is ShapeNet, a large-scale dataset of 3D models, and ModelNet40 and ModelNet10, which are subsets of ShapeNet.

Depth prediction is fundamental for many useful applications on computer vision and robotic systems. On mobile phones, the performance of some useful applications as augmented...

The Street View House Numbers (SVHN) dataset used consist of 32x32 10,000 labelled image pool, 30,000 unlabelled pool and 26,032 testing pool.

Residual Networks (ResNet) is composed of stacked entities referred to as residual blocks. A Residual Block of ResNet contains a module and an identity loop.

The dataset used in this paper is a real-world vehicle point cloud collected from a real vehicle self-driving process.

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

A large-scale, physically unconstrained gaze estimation dataset collected from 238 subjects in indoor and outdoor settings.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used the ImageNet, Places205, and VOC07 datasets for evaluation.

Dataset used for training and testing Convolutional Neural Networks for detecting trypophobia triggers.

The V-COCO dataset contains 2,533 training images, 2,867 validation images, and 4,946 test images, including 24 action classes.

The LSUN Bedroom and LSUN Cat dataset is a large-scale image dataset used for training and testing the proposed approach.

The dataset used in the paper is CIFAR-10, Tiny ImageNet, and ImageNet.

The SUN attribute dataset is a collection of images of scenes.

SUN database: Large-scale scene recognition from abbey to zoo.

The dataset used in the paper is a collection of images of objects in 3D space, with multiple views of each object.