noob213:

I noticed, I can specify how many filters the conv net should use, but what kind of filters are being used? (edge detection, sharpen etct ?)


Each filter will be randomly initialized (using this metho 20 and trained. The filters will not be initialized as classical image processing filters, but the training procedure might of course yield filters which are similar to e.g. edge detectors etc.



 noob213:

What is the difference when I specify this argument as 10 or 20?


Each filter will process the input activation and create an output activation map (in the default setup). The more filters a conv layer has, the more output channels (each processed by a single filter) it will yield. The higher the number of filters, the more “capacity” this layer has (similar to the number of neurons of a linear layer).



 noob213:

As how I understood looks like there is a “filter pool”, and different number of filters will be randomly picked from the pool as specified by the out_channels?


No, all filters, which were specified during the initialization of the conv layer, will be used.
Have a look at CS231n - Convolutions 14 for a general overview how this layer works.