[pitchups]

This is great news! Here has been my experience using Fast.ai : I had been training a deep learning network using Keras with Tensorflow, for diagnosing medical images - and it took me several months of hard work - tweaking parameters, training and testing to get acceptable levels of accuracy for our models. And then last month, I switched to Fast.ai (their pre-release version) and I was blown away - my models trained faster, and I matched and finally exceeded accuracy levels acheived with my earlier models. And I accomplished what had taken several months in Keras, in just a few days! And the biggest reasons for it were in my view, fast.ai's learning rate finder, the differential learning rates, and Test Time augmentation - all which are advanced features built into fast.ai. And the other great thing is that fast.ai uses the best defaults automatically, and it trains much, much faster than Keras / TF for some reason.

So I can't wait to try the new release out. I think Fast.ai has set a new bar for deep learning frameworks in terms of speed and ease of use. Thank you for all your great work!

[hcrisp]

There is a learning rate finder for Keras:

https://github.com/surmenok/keras_lr_finder

[infinitone]

Do you know if you can do object detection on images right out of the box? Or is it just classification?

[pitchups]

I am using it for classification right now - but pretty sure you should be able to do object detection quite easily as well.

[jph00]

It supports segmentation right now, but object detection won't be supported "out of the box" until 1.1 (in a month or two).

You can certainly train a YOLO or SSD pytorch model with fastai, however.

[lelima]

Hello Jeremy,

One question, there is any best practice to transform video into [n] frames to then use model.predict(n) to make a "live classification / object detection"?

Kind regards from Dublin!

[fredguth]

differential learning rates are now called discriminative learning rates.