In an image classification project, I have tried various CNN architectures with fastai v1. I’d like to share my note here, as it may take some time to figure out what cut and split parameters to use and where to get the pretained models.
This notebook shows the structures of some archs and how to use them with fastai v1. It includes ResNet, ResNeXt, SENet, Densenet, Inception V4, WRN, Xception, VGG, etc. Some pretrained models are from torchvision, some are from Cadene. The notebook also shows what the model is like after cutting and splitting into layer groups.
I hope it helps!
Although the fastai v1’s flexible API allows people to customize their own models, it may be more convenient to use if the library has default cut and split parameters for various models. Actually v0.7 has more models included and I saw people asking about it in some other posts.
I’d be happy to submit a PR if we want to include some of the models into the library.