tinyML EMEA 2022 - Louis Moreau: Performing object detection on constrained devices скачать в хорошем качестве

tinyML EMEA 2022 - Louis Moreau: Performing object detection on constrained devices

tinyML EMEA 2022 Algorithms, Software & Tools session Performing object detection on constrained devices Louis MOREAU, Senior DevRel Engineer, Edge Impulse In this session, we will go over the different existing image processing approaches used in tinyML, from simple and lightweight image classification techniques to more complex and compute-intensive ones such as object detection or image segmentation, which are typically not suitable for constrained devices. We will then introduce a new technique that can be used to perform object detection on constrained devices: FOMO. FOMO (Faster Objects, More Objects) is a novel open-source machine-learning algorithm that enables object detection on microcontrollers. With this new architecture, you will be able to find the location and count objects, as well as track multiple objects in an image in real-time using up to 30x less processing power and memory than MobileNet SSD or YOLOv5.