Deep learning for mining data about your fish's wearabouts
If you're interested in checking out how it works see below
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Humans really are amazing creatures, our brain can tell the difference between millions of objects. Heck, we named them didn't we? We dared to see if a computer via a Deep Learning (FishNet) could learn to recognize one object. The Betta fish Watson was our adorable test subject for this years hackathon.
How it works
That's a long story! Basically, we trained our Neural Network (NN) to recognize a fish using a tiny Python script. We click the position of the fish and data is generated. We then upload this data to Digits, a machine learning application on one of our desktops, which uses GPU horse power to "learn" what a Betta fish looks like.
We then loaded the image and coordinate data into the Application Digits, to start parsing and learning. This is a process that took us hours of fine tuning! We finally determined a model that would predict if a Betta fish in the frame, all using machine learning!
After training our NN we used a model to determine if the image contained a Betta fish or not and where Watson was. Then using bash and python scripts we updated his image containing a caption with what the Neural Network thought he was doing.