I keep seeing this getting asked like people are expecting a magic bullet solution.
​
In general you can only get out something within the realm of what you put in.
There are intelligent ways to structure training and models, but you can't fill in expected gaps without training with a reference or a close approximation of what those gaps are.
My best suggestion is to limit your input data or muxed model to specific high resolution subsets.
ex. You can train a LoRa on a small focused subset of data.
Joel_Duncan t1_jczkbc0 wrote
Reply to [D] Determining quality of training images with some metrics by i_sanitize_my_hands
I keep seeing this getting asked like people are expecting a magic bullet solution.
​
In general you can only get out something within the realm of what you put in.
There are intelligent ways to structure training and models, but you can't fill in expected gaps without training with a reference or a close approximation of what those gaps are.
My best suggestion is to limit your input data or muxed model to specific high resolution subsets.
ex. You can train a LoRa on a small focused subset of data.