154 datasets found

The B˚atvik seasonal dataset includes six drone flights that travel a distance of approximately 3.5 km over a coastal plot in southern Finland at an altitude approximately 100 m...

Dataset for table grape detection, segmentation and tracking

Dataset for table grape detection, segmentation and tracking

ShapeNet Parts is a dataset for part segmentation of 3D shapes.

A dataset of brain metastases (BM) segmentation, including T1 contrast enhanced (T1CE) MRI datasets.

A dataset of brain metastases (BM) segmentation, including T1 contrast enhanced (T1CE) MRI datasets.

A large-scale microscope image dataset of Trichomonas vaginalis for segmentation, consisting of 3,158 images with high-quality annotations.

The KiTS19 dataset is a kidney tumor image segmentation dataset.

MRI thigh muscle segmentation dataset

A dataset comprising 10 out-of-phase 3pt Dixon MRI volumes was used for algorithm development and testing.

3D X-ray microscope data for 3D object detection, segmentation, and metrology for buried structures in advanced IC packages

Medicalsemi-supervisedsegmentationisatechniquewhereamodelistrainedtosegmentobjectsofinterestinmedicalimageswithlimitedannotateddata.

The Spot-the-difference self-supervised pre-training for anomaly detection and segmentation dataset.

The MOTS20 dataset is used for multi-object tracking and segmentation.

The dataset used in this paper for segmentation of arterial walls in intravascular ultrasound cross-sectional images.

The dataset used for the experiments in the paper, which includes ImageNet, MS COCO, Cityscapes, and ADE20K datasets.

Automated medical image segmentation can assist doctors to diagnose faster and more accurate. Deep learning based models for medical image segmentation have made great progress...

The Multimodal Brain Tumor Segmentation Challenge 2020 dataset was used as our primary dataset for brain tumor classification and segmentation.

The Brain Tumor Segmentation (BraTS) Challenge Africa (BraTS-Africa) dataset, which provides a valuable resource for addressing challenges specific to resource-limited settings,...

We develop techniques for refining representations for fine-grained classification and segmentation tasks in a self-supervised manner.