150 datasets found

The OmniScape dataset consists of 10,000 annotated images captured from four different view angles: Front View (FV), Mirror-View Right (MVR), Mirror-View Left (MVL), and Rear...

The SynWoodScape dataset consists of 10,000 annotated images captured from four different view angles: Front View (FV), Mirror-View Right (MVR), Mirror-View Left (MVL), and Rear...

Semantic segmentation and 3D detection tasks on PartNet and ScanNet datasets

The Vaihingen dataset consists of 1440 scenes with a size of 250×250 pixels. Each scene is a colour-infrared (CIR) true orthophoto and a height grid (digital surface model; DSM)...

A set of labeled images of printed products at various stages of their production, used for training neural networks for semantic segmentation of manufactured parts and their...

Semantic segmentation is a fundamental task in computer vision and enables many downstream applications. It is related to image classification since it produces per-pixel...

The Stanford 2D3DS dataset is a dataset of spherical panoramas with corresponding pixelwise semantic labels.

Real-time semantic segmentation plays an important role in practical applications such as self-driving and robots. Most semantic segmentation research focuses on improving...

The dataset used for ImageNet recognition, ADE20K semantic segmentation, and COCO panoptic segmentation.

The Synthia dataset is a large-scale urban scene understanding dataset, containing 9000 samples. It is used for semantic segmentation tasks.

Few-shot segmentation remains challenging due to the limitations of its labeling information for unseen classes. Most previous approaches rely on extracting high-level feature...

The dataset used in the paper is ADE20K for semantic segmentation.

The GTA dataset consists of 24966 synthetic images synthetically generated from a video game consisting of outdoor scenes with rich variety of variations in lighting and traffic...

A dataset for semantic amodal segmentation, consisting of images with semantic object labels.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used the CIFAR-100 and ImageNet-1k datasets for image classification and semantic...

The NYUD-v2 dataset is a benchmark for indoor scene segmentation and depth estimation. It contains 1449 images with 4 tasks: semantic segmentation, depth estimation, surface...

The proposed dynamic routing framework for semantic segmentation.

The Synthia→Cityscapes task is a domain adaptation task for semantic segmentation, where the source domain is Synthia and the target domain is Cityscapes.

The dataset used in the paper is GTA5 and SYNTHIA, which are used for domain adaptive semantic segmentation (DASS).

A dataset of histopathological images for semantic segmentation