320 datasets found

The AWA2 dataset includes 50 classes of assorted animals totaling 37,322 samples, of which 10 categories are considered unseen classes. Attribute annotations are 85-dimensional.

The SUN dataset has a sample of 717 different scenes totaling 14,340 images, where 72 categories are unseen classes. Attribute annotations are 102-dimensional.

The CUB dataset comprises 200 bird species totaling 11,788 image samples, of which 50 categories are planned as unseen classes. The SUN dataset has a sample of 717 different...

The dataset used in this paper is a collection of images from the STL-10 dataset, preprocessed and used for training and evaluation of the proposed diffusion spectral entropy...

The dataset used in the paper is CIFAR100, ImageNet100, and ImageNet. CIFAR100 consists of 100 object classes and 60,000 images. ImageNet100 has 100 object classes and 60,000...

The dataset consists of four synthetic rendered scenes and four real captured scenes. The synthetic scenes provide control and ground truth for evaluation, while the real...

The DRIVE dataset contains the curvilinear-shaped vessel. This dataset consists of 40 565 × 584 color retinal images, which are split into 20 training images and 20 test images.

A dataset for mobile AI workshop 2021.

The dataset used in the paper is a video prediction dataset with occlusions, which is used to evaluate the proposed Fast Fourier Inception Networks (FFINet) for occluded video...

The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) dataset is a large-scale image classification dataset containing over 14 million images from 21,841 categories.

The CelebAHQ dataset was used with a fixed removal mask located near the image centers [11].

The ETH3D Benchmark dataset contains a set of objects 3D coordinates, images in which these objects can be seen, the intrinsic parameters and the pose of each of the cameras...

The dataset used in the paper is DINOv2, a vision foundation model trained on a large-scale dataset.

The dataset used in the paper is a large collection of small images, each representing a patch of a jigsaw puzzle. The patches are of the same size and orientation, and the goal...

The dataset used in the paper is a MPI3D dataset, which contains 3D images of objects with varying sizes and colors.

The dataset used in the paper is a Shapes3D dataset, which contains 3D shapes with varying sizes and colors.

Inria Aerial Image Labeling dataset contains aerial orthorectified color imagery of 5000 × 5000 pixels with a spatial resolution of 0.3 m.

AICrowd Mapping Challenge dataset contains 300 × 300 pixels RGB images and corresponding annotations in MS-COCO format.

The dataset used in the Amazon Picking Challenge 2016, a vision-based robotic picking system developed by Team Applied Robotics.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used CIFAR-10, CIFAR-100, Tiny ImageNet, and ImageNet datasets.