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repeat #4
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EXACTLY what i was thinking about. so i kinda do this manually now but in my specific case i also wanted to expand the dataser per image for different weather and lighting conditions. so using the img_aug library i do a post process operation where i dupe all the annotated images, make 4 different variarions for night, day, fog, rain. the original annotations follow making my dataset wider while keeping the accuracy. im still in a research stage of it, particularly night emulation cause shadows are tough, but i really want to see what we can do. i feel pseudolabelling is so friggin close to being nonsupervised and it would elevate one of the shittiest jobs in cv to a breeze. 1 perfect starting model is all it takes.. so cool. |
hey! cool stuff. im currently working on a similar system. what about repeating the process after the new weight is trained? to kinda keep getting better weights? any thoughts?
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