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Candidate Improvements for an Underperforming Image Classifier

Possible improvements for an underperforming image classifier fall into two broad groups. Data-side options include collecting more labeled examples and making the training set more varied. Model-side options include training for more iterations, changing network capacity, adjusting regularization, or redesigning the architecture. Use evidence from the model's errors to decide which option to test rather than applying every change indiscriminately.

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Updated 2026-09-19

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