Too Long; Didn't Read
This is a high-level exploration of the most common ways of implementing image-based deep learning (often referred to as image AI or AI) Annotation approaches, types of annotation and levels of automation for this task. This article is intended to introduce topics that we will dive deeper into in follow up posts. For the sake of sanity, we have simplified some of the concepts below. The best annotators in the world have a 4.6% error rate while the average person has around 8–9%. This error rate makes a significant difference in the performance of the resulting AI.