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Description
Right now we randomly resize each image independently and then pad the batch of images to have the same dimensions as the largest image. This means we waste compute and also that with probability ~1 the model doesn't see images of the target resolution without padding.
This PR moves the random resize to be directly in front of the forward pass. This gives slightly higher accuracy and slightly higher throughput.
It also fixes a bug where the expanded scales argument was ignored for Roboflow datasets. This should give slightly better results but slightly lower throughput.
Type of change
Please delete options that are not relevant.
How has this change been tested, please provide a testcase or example of how you tested the change?
Tested locally via a training script.
Any specific deployment considerations
For example, documentation changes, usability, usage/costs, secrets, etc.
Docs