With less than 500 North Atlantic right whales left in the world’s oceans, knowing the health and status of each whale is integral to the efforts of researchers working to protect the species from extinction.
The current process is quite time-consuming and laborious. It starts with photographing right whales during aerial surveys, selecting and importing the photos into a catalog, and finally comparing the photos against known whales in the catalog by trained researchers.
As part of an ongoing preservation effort, NOAA Fisheries launched a Kaggle data science competition to create the best automated process for identifying individual right whales.
Second place finisher Felix Lau describes how he used cuDNN, GeForce GPUs for initial development and an Amazon Web Services GPU instance to train his deep convolutional neural network.
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AI-Generated Summary
- NOAA Fisheries launched a Kaggle data science competition to develop automated identification of individual North Atlantic right whales, of which fewer than 500 remain.
- Second-place finisher Felix Lau used cuDNN and GeForce GPUs for initial development, then an Amazon Web Services GPU instance to train a deep convolutional neural network.
- Lau replaced the localizer with an aligner that rotates images so the whale bonnet is always right to the blowhead, enabling the classifier to focus on the callosity pattern.
Next Step
- Read Felix Lau's blog post for a detailed description of the approaches he took for the challenge.
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