Name
Abstract | ||
|---|---|---|
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 uploaded 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 holdout test set of 26 sources, the error rate of CoralNet 1.0 was 18.4% (relative) lower than CoralNet Beta. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICCVW54120.2021.00412 | 2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2021) |
DocType | Volume | Issue |
Conference | 2021 | 1 |
ISSN | Citations | PageRank |
2473-9936 | 0 | 0.34 |
References | Authors | |
6 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qimin Chen | 1 | 0 | 0.34 |
| Oscar Beijbom | 2 | 224 | 9.94 |
| Stephen Chan | 3 | 0 | 0.68 |
| Jessica Bouwmeester | 4 | 0 | 0.34 |
| David Kriegman | 5 | 7693 | 451.96 |