Use transfer learning for a small classifier
Apply transfer learning steps to build a small image classifier without training from scratch.
Build a four-class product packaging classifier from 1,200 labeled photos without overfitting a model trained from scratch. Reuse a pre-trained visual base, train a small task-specific head, evaluate honestly, then fine-tune only if validation evidence supports it. The common shortcut is to unfreeze everything immediately. With a small dataset, that can overfit fast and destroy useful pre-trained features. Split by product batch Hold out product batches and photo sessions so near-duplicate packaging images do not leak across train and validation. Transfer learning still needs an honest read on new images. Leakage can make a borrowed-feature baseline look production-ready. Freeze the…
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