260 datasets found

The Berkeley Image Segmentation Dataset (BSDS) is a standard benchmarking data set for many computer vision image segmentation algorithms.

The Graphic Narrative Corpus (GNC) is a dataset of annotated comic book pages, representing English-language graphic novels with a variety of styles.

The dataset comprises various rice types photographed under natural conditions without professional lighting or equipment, presenting a challenging yet practical classification...

The MIT scene parsing dataset used for training the FCN network.

The proposed method for automatically generating image masks to train Mask R-CNN for object detection.

A large-scale computationally annotated dataset of image-mask pairs and a large dataset of unannotated images extracted from video frames.

The V-Net is a deep learning model for medical image segmentation that uses a U-Net architecture.

The MS COCO 2014 Dataset contains images of 91 object categories, which contains 82783 training images, 40504 validation images and 40775 testing images.

Real-time semantic segmentation system for high-resolution images

Panoptic segmentation aims to unify instance and semantic segmentation in the same framework. Existing works propose to merge instance and semantic segmentation using...

Panoptic segmentation aims to unify instance and semantic segmentation in the same framework. Existing works propose to merge instance and semantic segmentation using...

CamVid dataset is a benchmark dataset for semantic segmentation. It consists of 700 images with 11 object classes.

BDD100K Dataset is a large-scale dataset for autonomous driving, containing 100,000 images, with 20,000 images for training and 80,000 images for testing.

Bloodstain pattern analysis dataset for bloodstain image segmentation

This paper proposes a framework to address the issue of requiring precise masks for existing FSS tasks, which address weakly-supervised few-shot segmentation tasks with only...

Crack detection on UAS-based Imagery using Transfer Learning

The NYU-Depth V2 dataset contains pairs of RGB and depth images collected from Microsoft Kinect in 464 indoor scenes.

A dataset for unsupervised performance evaluation of image segmentation.

The HRSOD dataset is used for evaluation, which consists of 287 samples.

The COIFT dataset is used for evaluation, which consists of 280 samples.