Tim Hartley, Director of Product and Marketing at Arm, presents the “Machine Learning for the Real World: What is Acceptable Accuracy, and How Can You Achieve It?” tutorial at the September 2020 Embedded Vision Summit.
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, Hartley explores 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, he focuses on how techniques like transfer learning can help fill the gaps when weaknesses in detection accuracy are found.
See here for a PDF of the slides.
