Finetune a model

Parameters

Model name, no spaces: Name of the finetuned model as it will appear in the other empanada modules after finetuning.

Train directory: Training directory for finetuning. Must conform to the standard directory structure specified for empanada (as for example is created by the Save finetune/training patches module).

Validation directory (optional): Validation directory. Must conform to the standard directory structure specified for empanada. Can be the same as Train directory.

Model directory: Directory in which to save the finetuned model definition and config file. The directory will be created if it doesn’t exist already.

Model to finetune: Empanada model to finetune.

Finetunable layers: Layers to unfreeze in the model encoder during finetuning. See insert either best practice or FAQ link with photo description

Iterations: Number of iterations to finetune the model.

Patch size in pixels: Patch size in pixels to use for random cropping of the image during finetuning. Should be divisible by 16 for PanopticDeepLab model or 128 for PanopticBiFPN models. Use Get model info to check.

Custom config (optional): Use a custom config file to set other training hyperparameters. See here for a finetuning template to modify.

Output

Saves and registers a .pth torchscript model that has been finetuned on the provided data. Also saves a .yaml config with parameters necessary for additional finetuning.

Demo

Check out the step-by-step tutorial here!