Deep Learning for Object Detection

Course description

Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. Deep learning techniques have emerged as a powerful strategy for learning feature representations directly from data and have led to remarkable breakthroughs in the field of Object Detection. During this course, we will review how evolved Deep Learning approaches for Object Detection, cover major building block of modern Object Detection models, review key training technics and learn how to build state-of-the-art Object Detection and Tracking system. The course will be useful for participants who do not familiar or have little experience with Object Detection.

Course tools

  •  Python
  • PyTorch

Prerequisites

  • Understanding how Deep Learning Classification works

Level of complexity of course

Intermediate

Lecturer

Mr. Alexander Zarichkovyi

Alexander is a Computer Vision Researcher with 3 years of expertise in creating robust high-performance Computer Vision algorithms for Object Detection and Tracking.  Also, he is the participant and winner of various competitions in Object Detection. Kaggle Competitions Master (Top-500 on Kaggle).

Fields of interests: Object Detection, Object Tracking, Semantic Segmentation, Instance Segmentation

Contacts[email protected]