| A GoogleNet or VGGNet has tons of parameters. Kinda, but they are defined for you. For example the GoogLeNet design is described in[1]. Page 5 lists the parameters, the diagram on page 6 shows how the layers are linked. Yes, I agree that the design of a new neural network architecture is a skilled process, and there is a lot of hard work there. I couldn't agree with that more, but that isn't what we are talking about here. It is quite possible to take a CNN like GoogLeNet designed for a specific purpose and reuse it in similar situations. GoogLeNet will always do pretty well for image classification. I think of it as analogous to a piece of software like a database. Designing a new database system is hard, but taking something like SQLite and using it is easy. Yes, you can tune it and get better performance out of it, and yes, it will break if you use it in the wrong circumstances, but it is generally pretty reliable if used as designed. Now this analogy breaks down because industrial use of CNNs is pretty new compared to Database systems. It's more like trying to get msql running on your Slackware 0.9 system in 1993 it is getting Postgres on Ubuntu 15.10. Nevertheless, there isn't really a black art to using an existing CNN. Lots of schlepping to get CUDA running on your machine, though. [1] http://www.cv-foundation.org/openaccess/content_cvpr_2015/pa... [2] Not MySQL, msql: https://en.wikipedia.org/wiki/MSQL |