Design a Small Image Classifier
Plan a small image classifier using data checks, transfer learning, and staged fine-tuning.
Small vision project The team has 2,400 shelf images, five stores, and one week. A model debate starts before anyone checks whether the validation split leaks near-duplicate shelves. In small-data vision, split quality and staged transfer learning often matter more than architectural novelty. Staged transfer Audit -> frozen base -> augment -> fine-tune selectively Use transfer learning to buy a fast baseline, then increase flexibility only when validation evidence asks for it. Scratch CNN Maximum flexibility, maximum small-data risk. Each stage answers a different uncertainty. Do not spend small labels learning generic edges and textures from scratch unless transfer evidence…
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