[stared]

It is an interesting perspective, but my experience is exactly the opposite. In Theano debugging was awful. TF felt like a breeze until it didn't. When I jumped on PyTorch - it TF started feeling confusing by comparison. Errors exactly in the defective lines, possibility to print everywhere (or using any other kind of feedback / logging intermediate results).

For using models it may note matter that much (though, again read YOLO in TF and PyTorch and then decide which is cleaner :)).

For new models which go beyond a standard ConvNet/LSTM... well, PyTorch is heaven, Theano sounds like a torture.

[cttet]

I am not sure what is your programming style in Pytorch. As people recommended and in most tutorials I see the sequential approach, where a small mistake of data preprocessing would lead to clueless errors in a completely irrelevant line.

YOLO is a quite standard feed-forward model in my opinion. I mean the math part, which I am more concerned with.

I have never used Theano before, my idea from it is that Tensorflow followed its static graph approach.

[stared]

Did you read cost function of YOLO in both languages?