Progressive Sprinkles (cutout variation) - my new data augmentation & 98% on NIH Malaria dataset

You may want to take a look at this repo. There are a couple of notebooks and code that may be useful.


Hi, I was trying to use the blended augmentations repo with my pretrained ResNext101 model (from pytorchcv model zoo). I want to use cnn_learner(data, base_arch, custom_head).ricap() or .blend(**kwargs) to add augmentations as my image dataset (100k images, 42 classes) is quite varied and I want my model to be more robust. However, fastai keeps raising this error: AttributeError: ‘Learner’ object has no attribute ‘blend’

I read that there is a difference between using cnn_learner() and Learner(). Is this the reason? But Jeremy said cnn_learner() is superior, is it worth using Learner() if I want the power of custom augmentations?

Thanks and cheers.

I realise this was a long time ago but in case you are still interested. I have just updated with a version which is compatible with the xtra_tfms() call github here. I have tested it on my end but not extensively so there may be some issues I am not yet aware of.

Secondly it seems very slow at the moment when sprinkles are being added in each batch. I first wanted to get it working but will look to try and optimise it next if I can.


Thanks for sharing… Even though this was a while ago, thanks for letting me know… I will keep it in mind for future computer vision experiments!

@rwightman Do you use this data augmentation technique in your experiments? And from your experience what works good in your experiments (in terms of data augmentation)?