LabelMe

One of the most terrible threats of coronavirus infection is the difficulty of timely diagnosis. Due to the possibility of a symptomless course of the disease, it is only possible to detect the disease in time by using the computer tomography.

LabelMe contributes to healthcare and helps non-profit organizations to develop technologies that save millions of people's lives.

Here are the examples of the markup we made for one open source project aimed at automating AI-assisted examinations. You can see examples of data labeling in which we selected different lung image projections and engaged qualified doctors.

Dataset with segmentation of affected cells in COVID-19 Набор данных включает 31 КТ-снимок легких в разных проекциях, в которых отсегментированы пораженные клетки. С помощью подобных датасетов можно создать алгоритмы де...

See examples of our Data Labeling

LabelMe wants to be open to new partners and demonstrate our expertise. That's why our team regularly develops examples of datasets for different tasks. So you can see the result of our work.

For example, a dataset with clothing segmentation. We collected and annotated 1,000 photos. There are 30 classes in total. You can download the dataset for review on our website.

Semantic Segmentation of fashion clothing (dataset) We have collected semantic segmentation of 1000 full-length photos of top models and celebrities. 50% male and 50% female. Segmented all clothing. In the archive you will find: the original image (png), masks to them (png) and a color chart of all classes