[arallen]

Hello,

Thanks for your interest,

As far as the architecture goes, we probably went through hundreds during the course of the competition season. The main problems we were trying to minimize while changing the network was the time it took to process one iteration of the network and over-fitting (finding only balls that look exactly like the training data). As our data set grew we often had to made small tweaks to the network to better optimize it. By the end of the competition season we had started to automate the process of changing the network parameters, training the network, testing it on a separate set of data, then reporting back.

We did find discrepancies between the performance in our lab and the performance at a competition. Primarily this came from changes in lighting (we had theorized that the networks were heavily dependent on the lighting of the ball and were working to fix that later in the season) and the fact that in a competition, the background of the image is much more dynamic than at a lab tracking a ball against a wall or floor.

We will certainly be looking to improve the efficiency of our process as the next season approaches and we may get to see this working live on the field!