The Accurate Nevus Shapes/Segmentation dataset was made by the MetaVerse team using their own data. To create precise masks from the original video streams, multiple segmentation techniques were applied. These techniques were carefully chosen to maintain the natural variations and original shapes of the objects.

Accurate Nevus Shapes/Segmentation is a dataset for instance segmentation, semantic segmentation, and object detection tasks. Possible applications of the dataset could be in the medical industry.

The dataset consists of 3208 images with 6667 labeled objects belonging to 1 single class (nevus).

Images in the Accurate Nevus Shapes dataset have pixel-level instance segmentation annotations. Due to the nature of the instance segmentation task, it can be automatically transformed into a semantic segmentation (only one mask for every class) or object detection (bounding boxes for every object) tasks. All images are labeled (i.e. with annotations). There are no pre-defined train/val/test splits in the dataset. The dataset was released in 2022 by the MetaVision, Ukraine.

Accurate Nevus Shapes/Segmentation is under CC BY 4.0 license.

Source

If you make use of the Accurate Nevus Shapes data, please cite the following reference:

@dataset{Accurate Nevus Shapes,
	author={metavision},
	title={Accurate Nevus Shapes/Segmentation},
	year={2022},
	url={https://www.kaggle.com/datasets/metavision/accurate-nevus-shapessegmentation}
}

Source

Dataset Accurate Nevus Shapes can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='Accurate Nevus Shapes', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here.