My machine learning project was on the back burner for the last few months. I recently resurrected the project for no reason other than to just push it to a decent stopping point. I already spent a lot of time developing a tool to categorize thousands of self-captured images of vehicles to train the neural network. It would have been silly to leave the project off before actually implementing all of that data.
So, I re-trained the neural network last month - and just this weekend I finally got around to putting the re-trained tool to work. I set it loose on some pre-recorded traffic footage from cameras owned by the Maryland Dept. of Transportation - and I was very happy with its performance!
If you recall, the earlier version of this tool was trained on third-party car images and - let's be honest - it didn't do such a good job. The retrained network is much much better. I recorded some video clips of its performance and saved it onto youtube. You can check it out here.
My primary goal of this project was to learn as much as possible, and from that perspective, it was a success. Some of my other goals, such as optimizing the neural network to run on the Raspberry Pi (a $35 mini computer), were not completely accomplished. I mean it can technically run on the pi, but it only does about 5 frames per second. With time, I am confident I can improve that - probably a lot - but there will be diminishing returns in terms of learning if I spend more time on this. Also, it's easy to imagine developing a set of tools that would sit on top of this technology to do useful things like count cars, detect traffic conditions, etc. All of that stuff would be fun to do, but time-consuming.
So, with this last post, I am putting this project on hold indefinitely.
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