N+1 Fish, N+2 Fish: A data science competition
CVision AI partnered with Kate Wing of the databranch, DrivenData, the Gulf of Maine Research Institute (GMRI), The Nature Conservancy, and researcher Joseph Paul Cohen, PhD of the Montreal Institute for Learning Algorithms to design a data science competition to help bring attention to and begin to reduce the burden of human video review for fisheries management.
GMRI provided hundreds of hours of video data, with loosely correlated annotations for species type and length. Using Tator, our team was able to create a data set that had frame accurate annotations with localization annotations for competitors to use. Working with Dr. Cohen, we then designed a novel competition score metric using multiple objectives: count, species identification, and length measurement. The results of the competition became the basis for an open source library for electronic monitoring, which we called OpenEM.
OpenEM: An open source toolkit for electronic monitoring algorithms
Today we continue to maintain OpenEM through grants from the National Oceanic and Atmospheric Agency and internal research funding. It currently supports detection, classification, counting and measurement of fish during landing or discard. This functionality is available via a deployment library with pretrained models available in our example data. The base library is written in C++, with bindings available for both Python and C#. Examples are included for all three languages.
