These are chat archives for FreeCodeCamp/DataScience
discussion on how we can use statistical methods to measure and improve the efficacy of http://freeCodeCamp.com
This is a good basic description of conv-NN. The other name usually assigned to is "filter". Bear in mind that it is still an overloaded term anyway.
I am not sure but I believe to remember seen some references using the term "filter" to refer stacked layers for multidimensional analysis (eg. when running conv-NN by RGB ranges, which is usual).
Still, that doesn't answer why darknet requires two parameters to define what they called "filter".
The values you shared seemed to be more related to the default of the maxpool parameters in the darknet.cfg file.
With the available information I can't help tracking the relation, sorry.
The break down you supplied is good, thanks!
Here is where I definitively advise you to get some training:
fails to state how it has shrunk the layers...it goes from 416x416x3 -> 416x416x16 down to 208x208x16 with only a mention of using a leaky relu in between...so i can figure out how it shrinks the input down to 208...
I must say the material might be a bit confusing but it is giving some clues of what it is happening. I don't really think the material is failing on that regard.
(NOTE: we have talked about kernels, windows and filters; if you read well the break down reference you will at least suspect from where the rest of stacked matrices, from 3 to 16, might come from).
@deanhub2 bear in mind that I have studied convNN but I am not really an expert. It would be hard for me to provide you with more details.
I think the (mini-) yolo script is worth looking at! Success with the rest of the translation.
evaristoc sends brownie points to @deanhu2 and @deanhub2 :sparkles: :thumbsup: :sparkles:
It would be easier for you if you would understand convNN and then trying to replicate the procedure instead of translating the code.
I strongly recommend to study convNN a bit more.
deanhu2 sends brownie points to @evaristoc :sparkles: :thumbsup: :sparkles: