Episode Details

Daniel wants to catalog his small-parts drawer — screws, bolts, standoffs — by training a model on about a hundred phone photos. That raises two questions: what annotation tooling actually works at this scale, and should he fine-tune a large vision-language model or train something small and task-specific? We dig into why capture consistency and a scale reference matter more than image count, how to structure the schema so the model isn't doing two jobs at once, and where the open-vocabulary tradeoff flips the answer. Episode #090862 — open it directly at myweirdprompts.com/090862