You need to train an object detection model to identify bounding boxes around Post-it Notes® in an image. Post-it Notes can have a variety of background colors and shapes. You have a dataset with 1000 images with a maximum size of 1.4MB and a CSV file containing annotations stored in Cloud Storage. You want to select a training method that reliably detects Post-it Notes of any relative size in the image and that minimizes the time to train a model. What should you do?

AutoML is a codeless solution that minimizes time to train and develop the model, and it is capable of detecting bounding boxes up to one percent the length of a side of an image. The object detection capability of the Cloud Vision API confidently detects large objects within the image and is not the best option to reliably detect sticky notes of any relative size in the image. Creating a custom training job requires more development time than using AutoML does. The extra flexibility of custom training is not required because AutoML achieves state-of-the-art performance even on tiny objects (8-32 pixels). Additionally, training a model from scratch is not expected to be as performant as transfer learning.