992 datasets found

The proposed rectified binary convolutional networks (RBCNs) are used to improve the performance of 1-bit DCNNs for mobile and AI chips based applications.

This paper uses the ImageNet and COCO2017 validation datasets for testing.

The FastViT dataset is a fast vision transformer model.

The EdgeVITs dataset is a light-weight vision transformer model.

MobileViGv2 uses Mobile Graph Convolution (MGC) to demonstrate the effectiveness of our approach.

3D point clouds are irregular and unordered, hence applying convolution on them can be difficult. PointConv can be applied on point clouds to build deep convolutional networks.

The YCB-Video dataset contains 92 videos of 21 objects with varying textures and sizes under cluttered indoor environments.

The LMO dataset is a subset of the LM dataset consisting of eight objects in more cluttered scenes.

The LM consists of 13 objects with approximately 1.2K images per object. We follow the settings described in [2], which uses 15% of the data for training and the rest for testing.

This dataset is used to evaluate the performance of a Convolutional Neural Network (CNN) on the ImageNet Large Scale Visual Recognition Challenge (ILSVRC2012).

This dataset is used to evaluate the performance of a Convolutional Neural Network (CNN) on the ImageNet Large Scale Visual Recognition Challenge (ILSVRC2012).

This dataset is used to train a Convolutional Neural Network (CNN) to classify objects from web images. The dataset is created by downloading images from the web using a query...

A dataset for pose estimation and tracking for ASIST system

Video-MNIST is a novel variant of the classic MNIST dataset. It contains 70000 sequences, each sequence containing 30 frames showing an affine transformation on a single...

The dataset used in the paper is Fashion-MNIST, MNIST, SVHN, dSprites, and CIFAR-10.

The dataset used in this paper is a sliding window ConvNet dataset, which is a collection of 3D images and their corresponding labels.

The dataset used in this paper is a 3D ConvNet dataset, which is a collection of 3D images and their corresponding labels.

The NYU-Depth V2 dataset contains pairs of RGB and depth images collected from Microsoft Kinect in 464 indoor scenes.

The MIT Indoor Scene Recognition dataset contains 67 categories of indoor scenes.

The dataset used in the paper for 6Dof object pose estimation using RGB-D images.