Start Time: 1:30 pm
End Time: 2:00 pm
The benefits of running machine learning at the edge are widely accepted, and today’s low-power edge devices are already showing great potential to run ML. But what constitutes acceptable accuracy when applied to real-world, real-time use cases? In this talk we explore what constitutes acceptable detection accuracy for specific use cases, and how this can be measured. Looking at which ML models are meeting the challenges and which fall short, we focus on how techniques like transfer learning can help fill the gaps when weaknesses in detection accuracy are found.