Use transfer learning as an experiment, not a shortcut

There is more than one transfer strategy

The PyTorch computer vision tutorial demonstrates transfer learning through fine tuning and through using a network as a fixed feature extractor. These approaches change which parameters are trained. Neither approach removes the need to examine data quality and evaluate the model for the intended task. Reusing a network is an experiment design choice, not evidence that a model is already suitable. [1]

Define the comparison first

Choose a dataset version, split, baseline, and metric before adjusting the network. Keep image preprocessing documented and consistent between training and evaluation. Review whether labels and class coverage match the intended deployment setting. If measurements come from related specimens or acquisition sessions, account for that relationship when making the split.

Compare a small number of clearly described configurations first. Record which layers are trainable, how the output is adapted, and which training settings changed. Review representative errors alongside aggregate metrics. This makes it easier to learn from an experiment than a broad search in which many choices change at once.

Inspect where transfer does not help

Look for groups whose inputs differ from the data seen during training. Changes in background, scale, acquisition conditions, or labeling practice may expose important limitations. Plan what happens when the input is outside the expected workflow. Keep the experiment record and weights together. A practical model delivery includes preprocessing, inference, and evaluation information, not only a checkpoint file and an optimistic headline score.