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So many amazing projects shared here :slight_smile: I think we are seeing a fastai explosion - so hard / impossible to keep up with what is happening these days :slight_smile:

Just wanted to share my whale repository now that it is completed :slight_smile:

It contains a bunch of stuff including:
:white_check_mark: training a classifier
:white_check_mark: training on bounding boxes (localization)
:white_check_mark: landmark detection
:white_check_mark: bounding box extraction
… and finally training a model that combines classification and metric learning (places in top 7% of a recent Kaggle competition)

From the perspective of being able to leverage fastai functionality, some of the notebooks do a better job, some worse. You can’t win it all :slight_smile: And in fact, I don’t mind venturing off the beaten track all that much. Sometimes doing things my way allows me to move faster (mostly because I am not that good with figuring out how some things are done in the library and can code up simple things rather quickly) but mostly because this approach is very good for learning.

What I really appreciated about this competition is the sense of 'hacking ’ on something that it reconnected me with. This is the sort of state of mind where you know how everything that you use works, you use simple building blocks and can change things up rather quickly.

Well, maybe knowing how everything works is not the right expression - I surely have no idea how augmentation is applied for example, nor do I have a particular willingness to know that. Its more about knowing what each building block does than how it does it. No surprises, simple behavior.

Going forward I would like to stick more closely to what the library provides but this feeling of ‘hacking’ on something is definitely something I will continue to look for in any personal project I work on. I think I would even be willing to trade performance for more of that feeling. My current thinking is that in the long run staying in this hacking state is actually a better predictor of success than initial results. But hey - not sure if I’ll have the same perspective on this 6 months from now.

Look forward to part 2 awesomeness that will ensue soon :slight_smile: and already have a couple of ideas for future projects, this time with even more fastai :smile:

EDIT: just wanted to clarify - I don’t use the library in only two places, in the Siamese notebook and the final one, and that is only for reading in data. As a matter of fact, as far as I am aware, fastai offers the best way of augmenting images currently available, and just yesterday realized you can apply the transformations to arbitrary data with ease… Hoping to share an NB on that in near future… For everything else I am using the library and it is only through is functionality that I was able to complete so much in record time (the training loop for instance has so many cool aspects you will not find elsewhere that I am only now learning about)

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