Fine-Tune in Stages
Plan staged fine-tuning for a pretrained model while controlling overfitting risk.
A pretrained image base reaches 84 percent validation accuracy with a newly trained head. Errors cluster around domain-specific packaging details. You have 3,200 labeled images. Freeze first, train the head, review errors, then selectively unfreeze upper layers with a smaller learning rate. Unfreezing the entire base at the first sign of weakness can overwrite reusable features and overfit small domain data. Freeze the base Set pretrained base layers non-trainable and attach a task-specific classifier head. This reuses general visual features while learning the new decision boundary. Train the head Train only the new top layers until validation stops improving. The…
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