1,082 datasets found

The dataset used in the paper is CIFAR-100 and ImageNet.

A binary imbalanced classification dataset with 32 × 32 color images of 10 classes of natural objects.

The dataset used in this paper for hand-drawn symbol recognition of surgical flowsheet graphs with deep image segmentation.

The FULL1 and FULL2 datasets are subsets of the Oxford RobotCar dataset, with longer route lengths.

The LOOP1 and LOOP2 datasets are subsets of the Oxford RobotCar dataset, with shorter route lengths.

The KITTI360 dataset is used for evaluating the proposed Tuple-Circle loss function for cross-modality feature learning. The dataset consists of image and LiDAR scan pairs.

PhotoBot is a framework for fully automated photo acquisition based on an interplay between high-level human language guidance and a robot photographer.

The dataset used in the paper is the Imagenet dataset, which is a large annotated dataset of images used for image classification tasks.

The Kubric dataset offers training and validation data across various difficulty levels, featuring videos of objects interacting with each other.

The Physion dataset provides training, validation, and testing data for seven scenarios, such as dominoes and support, involving rigid-body objects colliding as well as one...

Monocular 3D object localization in driving scenes is a crucial task, but challenging due to its ill-posed nature. Estimating 3D coordinates for each pixel on the object surface...

Aerial imaging of ground targets is highly challenging because of various factors that affect light propagation through different mediums. Several convolutional neural...

The dataset used in the paper is not explicitly described. However, it is mentioned that the authors used the ImageNet-1K, COCO, and ADE20K datasets for image classification,...

The dataset in this paper comes from the database of the Ultrasound Imaging Department of Peking University Shenzhen Hospital.

The Moving MNIST dataset consists of videos of MNIST digits.

KITTI 2012 is a real-world dataset in the outdoor scenario, and contains 194 training and 195 testing stereo image pairs with the size of 376 × 1240.

Stereo image super-resolution aims to improve the quality of high-resolution stereo image pairs by exploiting complementary information across views.

The Argoverse dataset is a large-scale dataset for autonomous driving, containing 3D point clouds, semantic segmentation masks, and instance segmentation masks.

Contrastive Language-Image Pretraining (CLIP) models are trained with datasets extracted from web crawls, which are of large quantity but limited quality. This paper explores...

A real-world dataset collected in indoor and outdoor scenarios with sparse optical flow labels.