Original post author: Ryan
Picking up from where I left off on my previous machine learning post, which you can find here...
I am embarking on a personal project involving machine learning and computer vision. The project has four main goals:
Develop an artificial neural network that can identify vehicles within an image.
Optimize that neural network so it can scan between five and ten images every second (in other words, allow it to operate on a video stream).
Further optimize that neural network so it can run on a $35 mini-computer known as a Raspberry Pi (rather than my large desktop computer).
Develop some computer routines that utilize the above and collect data.
Learn as much as possible in the process. Tonight, the project reached its first milestone: I programmed and trained my first artificial neural network. I trained it by showing it 6,592 images - half of which contained a car, and half did not contain a car. After viewing each image, the neural network would say whether or not it thought the image contained a car. If it guessed wrong, it would re-calibrate itself, and then we'd move to the next image. The above sounds like a really boring a tedious process - but it's not. The training is all automated, and it completed in under one hour. After training, I showed the neural network about 750 images that it never seen before, and it was able to predict whether or not they contained a car with over 95% accuracy. These accomplishments are rather mundane in the world of computer science - but its a humble start to the project. I can't make the neural network too flashy, or else Steps 2 and 3 will get very difficult.
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