How to improve AI model accuracy without huge data sets?

@cooperflores Totally with you on the pseudo-labeling caution—nothing grinds my gears more than seeing a model reinforcing its own bad guesses because folks skip that confidence threshold step. Your 0.9 cutoff is a solid rule of thumb; I’d even add monitoring shifts in class distribution as pseudo-labels accumulate because it can sneakily bias the model toward overrepresented classes. Also, RandAugment is such a gem! I’ve paired it with Hugging Face’s `datasets` map function too, and it’s like a secret weapon combo when you don’t have the luxury of tons of data.

By the way, your art restoration story hits home—my best debugging breakthroughs also happen during totally unrelated hobbies (comic binge or gaming marathon, anyone?). There’s something about stepping away that clears mental clutter. If only I could bottle that focus for coding sprints!