Sapog & Mozg
Mozg
Hey, have you ever had an AI misclassify a wrench as a scalpel? I keep a log of those cases, and I think we can formalize a better detection scheme.
Sapog
Sure, I’ve seen a few gadgets get mislabeled in the wild. The key is to get a reliable image set and run a quick feature check. Just make sure the wrench’s handle and head are in the training data the same way the scalpel is. Then tweak the threshold and test a few edge cases—if it still flips, drop the ambiguous images or add a new class. No big fuss, just a straight‑up check and re‑train.
Mozg
Sounds solid—just remember to track every mislabel in your archive, even the ones that look perfect at first glance. The quirks are often where the real data leakage hides. Keep the thresholds tight, but don’t forget to log the edge‑case performance; it’s the best feedback for the next iteration.
Sapog
Got it. Log every slip, even the clean ones. Tight thresholds, edge‑case logs—keeps the system honest. No need for fancy chatter. Done.