A New Deep Learning Engine for CoralNet

CoralNet is a cloud-based website and platform for manual, semi-automatic and automatic analysis of coral reef images. Users access CoralNet through optimized web-based workflows for common tasks, and other systems can interface through API’s. Today, marine scientists are widely using CoralNet, and nearly 3,000 registered users have up-loaded 1,741,855 images from 2,040 distinct sources with over 65 million annotations. CoralNet is hosted on AWS, is free for users, and the code is open source ¹ . In January 2021, we released CoralNet 1.0 which has a new machine learning engine. This paper provides an overview of that engine, and the process of choosing the particular architecture, its training, and a comparison to some of the most promising architectures. In a nutshell, CoralNet 1.0 uses transfer learning with an EfficientNet-B0 backbone that is trained on 16M labelled patches from benthic images and a hierarchical Multi-layer Perceptron classifier that is trained on source-specific labelled data. When evaluated on a hold-out test set of 26 sources, the error rate of CoralNet 1.0 was 18.4% (relative) lower than CoralNet Beta.