Thanks for your comments.
We would add analysis of the results – we simply didn’t have the time to complete it before the deadline.
As the hardware that such a solution will work on was not specified in the challenge and we didn’t have time to test it on different hardware, we decided not to include any performance figures at this stage. Our estimates for running the models on a Raspberry Pi are 2-3secs per image, for Nvidia Jetson TX1/2 about 30FPS and on a iOS mobile phone we achieved 60FPS @ 4K using Core ML.

Thanks for your comments. Foodnet in our paper refers to a food data set we have put together from parts of 2 public research data sets (Food101+ImageNet) and not to the paper and method you have mentioned. This was not clear, so thanks for mentioning it. We shall add some references to the article in due course. We have not tested the model performance on a Raspberry Pi but based on similar models we have tested in the past on such hardware we expect performance of 2-3sec per image.

Thanks for your feedback. We are not showing off but just trying to show the many experiments we conducted and the ones we started, trying to find the best solution we could achieve for this specific challenge.