88 datasets found

Multi-label image recognition is a practical and challenging task compared to single-label image classification.

The Middlebury dataset is a benchmark for stereo vision and 3D reconstruction.

The dataset used in this paper is Symsol_reduced and Celeba.

The dataset used in this paper is dSprites2, Cars3D, and SmallNorb.

The dataset used in the paper is a collection of images from the SVHN dataset, along with labels. The dataset is used for image classification.

The MSCOCO 2014 validation dataset is used to evaluate the performance of the proposed self-perceptual objective.

ShanghaiTech Part B is a crowd counting dataset that contains 400 training images and 316 test images.

ShanghaiTech Part A is a crowd counting dataset that contains 300 training images and 182 test images.

The Make3D dataset is a small but challenging dataset for 3D object recognition and scene understanding, which consists of 134 images with aligned depth information.

Food-101 dataset is a large-scale dataset for food image classification, with 101 classes and over 30,000 images.

Kvasir: A multi-class image dataset for computer aided gastrointestinal disease detection.

Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic).

A dataset for terrain-aware autonomous driving on Mars.

A self-supervised and semi-supervised learning for Mars segmentation dataset.

Semantic segmentation is a crucial and challenging task for image understanding. It aims to predict a dense labeling map for the input image, which assigns each pixel a unique...

A real-world dataset for multi-object shape completion, featuring 33 objects (e.g., toy, household, and industrial objects).

The dataset used for 6D object pose estimation, consisting of images of 16 objects with varying levels of occlusion.

Object recognition is arguably the most important problem at the heart of computer vision. Recently, Barbu et al. introduced a dataset called ObjectNet which includes objects in...

Model inversion attacks are a type of privacy attack that reconstructs private data used to train a machine learning model, solely by accessing the model.

CUB-200 is a fine-grained image classification dataset containing high resolution images of 200 different bird species.