Interesting. I did some digging to find out why fastai was setting .crit to nll_loss by default \longrightarrow turns out if the ModelData object’s .is_multi attribute is False (and .is_reg too) then this is the default.
Maybe that makes sense because the model’s ultimately choosing a single class foreach anchor box? – not sure if that’s by design or WIP.
Anyway, I made sure to set crit=F.cross_entropy, and here’s what I got:
The shape looks like what you’d expect, and so does the loss for a blank-slate model.
I’m going to take a look at using Cyclic Learning Rates for training this on ImageNet. I’m not sure if it’s worth it to train a new model from scratch on ImageNet – but I figure if I want a ‘new’ pretrained model: that’s the way to go; and I guess it should only really take a day or two.
Thanks for the pointer, btw!