Training Data for CV/DL

Training data is critical to create robust ML/DL algorithms. Some researchers and visionaries think that data is even more important than the algorithms themselves. In this talk you will learn some best known methods to deal with training data, understand problems with data collection, realize difficulties with data annotation, and find out how some Intel teams manage data in real projects. Also you will get a short overview of tools which are developed by Intel like CVAT (https://github.com/openvinotoolkit/cvat) and Datumaro (https://github.com/openvinotoolkit/datumaro) for annotating and managing training data.