246 datasets found

A private dataset of UAV-borne remote sensing images with a resolution between 10000×10000 and 20000×20000 is constructed. Each remote sensing image which corresponds to the red...

COCO validation dataset

The material segmentation dataset is a publicly available dataset containing 3,817 images extracted from the Virginia Department of Transportation bridge inspection reports with...

High-resolution remotely sensed images pose a challenge for commonly used semantic segmentation methods such as Convolutional Neural Network (CNN) and Vision Transformer (ViT).

The RIT-18 image set contains a single aerial training image with a spatial resolution of 0.047 m.

The DSTL image set comprises 25 satellite images covering a region of 1000 m × 1000 m.

The U-net is a deep convolutional neural network for biomedical image segmentation.

Few-shot segmentation aims to segment novel classes by using the model trained on base classes.

PASCAL-5i and COCO-20i are datasets used for evaluation of few-shot segmentation.

CelebAMask-HQ provides the parsing map of images in CelebA-HQ down-sampled to 512 × 512, where pixel-level annotation of 19 classes, including facial components and accessories,...

The dataset is used for indoor scene understanding and contains RGB and depth images.

The dataset used in this paper for medical image segmentation, including images from the ISBI-2012 dataset.

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

Indoor segmentation and support inference from RGB-D images.

The dataset used for the experiments with the proposed approach to augment image data for semantic segmentation networks by applying image-to-image translation with both, a...

Children’s wrist ultrasound scans dataset for bony region segmentation

High-resolution remote sensing images

The BSDS300 and BSDS500 datasets are benchmarks for image segmentation.

The Berkeley Segmentation Data Set is a benchmark for unsupervised image segmentation.

Scene segmentation in images is a fundamental yet challenging problem in visual content understanding, which is to learn a model to assign every image pixel to a categorical label.