New to FastAI. I am following the typical workflow described in the book and the tutorials, where I build a DataBlock, a dataloader, a model, and a learner (from the dls, the model, and some loss function). So far so good.
But what if I have multiple kinds of data collections? My training data is images. I have several collections of images, each with different sizes, labeled differently (labeled via file names, via CSV files, etc). I want to keep the original folder structure of all these different data collections.
Is it possible to have a single
dls that pulls images from all these different locations, processes them differently based on source path, and feeds them to the model during training / validation? The splitter would also have to split each source path individually, to make sure I train / validate with images from all source paths.
Are there any code examples that do this?