I am new to fastai (getting started with the lib), fascinated to see the speed boost, tweaks and productivity it provides in creating models.
Am trying to create a model with custom head from a pre-trained model eg, resnet34 for an Image Regression problem - Price prediction based on Image, but doesn’t know how to achieve it using fastai library.
Thanks for the great diagrams @LotusGaurdian , it becomes much more clear now. Thanks to your post, I looked up image regression and learned a new concept!
Also, it’ beyond my current capabilities to suggest concrete fast.ai snippets etc , but just as a general direction, would it make sense to turn this into a multi-class prediction and each prediction represents a price range? So, instead of ‘goldfish’ we take ‘goldfish’ to mean “$120-130” ? To make it finer grained, make the number of categories larger and that’d squeeze the range accordingly?
There was an answer to a similar question previously, not sure if it fits your scenario exactly, but might help in moving things forward a bit?
I am framing the response since you are getting started with fastai library.
Create dataloaders using high level api - ImageDataLoaders.from_df ( Vision data | fastai ) . y_block should be chosen as RegressionBlock . If you want to tweak the data pipeline for more advanced use cases, definitely check out the Data Block tutorial in docs.fastai.com.