Fine-Tuning Pre-Trained Models for Computer Vision

1. Select a suitable pre-trained model

Start with a network whose input size, compute profile and architecture fit the target task. ResNet, MobileNet and EfficientNet represent different accuracy–resource trade-offs. Edge deployment constraints should influence the choice before training begins.

2. Prepare the dataset and evaluation split

• Define labels and remove duplicates or leakage across splits.
• Create separate training, validation and test sets.
• Apply the preprocessing expected by the pre-trained backbone.
• Use augmentation that reflects plausible variation in the target environment.

3. Adapt and fine-tune

Replace the task head, freeze the backbone initially and train the new layers. Then unfreeze a controlled portion of the network with a lower learning rate. The amount of fine-tuning depends on dataset size and how far the target domain differs from the original training data.

4. Evaluate for the real task

Choose a loss and metrics that match the operational cost of errors. Inspect performance by class and under relevant lighting, camera angle or background conditions. Before edge deployment, measure memory, latency and energy on the actual target hardware.