This is the second instalment to our 2-part blog based on our approach of the Nvidia GPU Hackathon and NOAA Fisheries. Part 1 of the blog described and focused on general introduction and the data used for the hackathon, this part focuses on the algorithms used and the results obtained.

Example from object detector

Object detection

Our partner in the hackathon, AI.Fish, were in charge of creating the object detector. AI.Fish decided to use a Faster-RCNN model pretrained on the COCO dataset.

15,000 bounding box annotations over 1,733 images were used by Ai.Fish to finetune the model and create a fish detection system. The object detector…

A great start to the Christmas season for the Lynker Analytics team in collaboration with Ai.Fish was being selected for the NOAA fisheries & Nvidia GPU Hackathon. The Hackathon was a multi-day event designed to help teams of three to six developers accelerate their own code on high-end Nvidia GPUs using a programming model, or machine learning framework of their choice.

Sample of imagery used in the hackathon. (from left to right) Lingcod, Eelpout, Pacific Ocean Perch, Urchin, Starfish, Arrowtooth Flounder, Spotted Ratfish, Longnose Skate

The hackathon saw as many as 9 teams each with a different focus ranging from mathematical modelling to machine learning and AI approaches. Our team’s goal was to build a fish detection and classification model using active learning. …

Michael Stanley

I am a Data Science Researcher at Lynker Analytics. Based in Wellington, New Zealand

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