73 datasets found

Aerial images and building footprints from GIS resources

The dataset used in this paper for psoas muscle segmentation in low-dose X-ray computed tomography images.

A python package consisting of some well-known loss functions widely used for image segmentation.

The LoveDA dataset consists of 5,987 high-quality optical remote sensing images with a resolution of 0.3 meters per pixel.

The Underwater Change Detection dataset contains images of underwater scenes.

The Image Segmentation dataset is used to evaluate the performance of the ensemble average rule.

The dataset used for training and testing the proposed LSSC system, which consists of 1530 training images and 170 test images in RGB format.

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)...

Diffusion Probabilistic Models are employed for state-of-the-art image generation. In this work, we present a method for extending such models for performing image segmentation.

The dataset used for this project contains 4,000 seismic images with 4,000 labeled mask images. The seismic images are in grayscale, and the mask images are in black and white.

The Pascal VOC Challenge dataset is a benchmark for object detection and image segmentation.

The CATCH dataset is used to test the proposed Style-Extracting Diffusion Models (STEDM) for canine cancer histology image segmentation.

The HER2 dataset is used to test the proposed Style-Extracting Diffusion Models (STEDM) for histopathology image segmentation.

The MIT-Adobe FiveK dataset, a large-scale dataset for image segmentation and object detection.

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

The dataset used in this study consists of multiple aerial and satellite datasets, including UAV, Airborne, and Satellite data.

The dataset used in this study consists of multiple aerial and satellite datasets, including UAV, Airborne, and Satellite data.

A new Multiorgan Lesion Segmentation (MLS) dataset that contains images of various organs, including brain, liver, and lung, across different imaging modalities—MR and CT.

Remote-sensing image segmentation based on implicit 3-d scene representation

The dataset used in this paper is the BSDS500 dataset, which contains 200 natural images with over 1000 ground truth labellings.