A TinyML model for real-time object detection

A new machine learning technique developed by researchers at Edge Impulse, a platform for creating ML models for the edge, makes it possible to run real-time object detection on devices with very small computation and memory capacity. Called Faster Objects, More Objects (FOMO), the new deep learning architecture can unlock new computer vision applications.

Most object-detection deep learning models have memory and computation requirements that are beyond the capacity of small processors. FOMO, on the other hand, only requires several hundred kilobytes of memory, which makes it a great technique for TinyML, a subfield of machine learning focused on running ML models on microcontrollers and other memory-constrained devices that have limited or no internet connectivity.

Read more about FOMO on TechTalks.

For more on TinyML:

• TinyML is bringing neural networks to microcontrollers

• New deep learning model brings image segmentation to edge devices

• The cloud is becoming AI’s bottleneck