Overview

This section describes the architecture configuration design of models. For details on NetsPresso Trainer's model configuration excluding the architecture, please refer to the model components page.

NetsPresso Trainer prioritize model compression and device deployment, thus models fulfill the following criteria:

• Compatible with torch.fx converting.
• Can be compressed by pruning method provided in NetsPresso.
• Can be easily deployed at many edge devices.

To provide a wide range of models that meet these conditions in diverse forms, we define and use four fields for model definition: full, backbone, neck, and head. This approach allows users to utilize backbones, necks, and heads in desired configurations. For models that cannot be segmented into these three modules, we provide them in a full models.

model:
  architecture:
    full: ~ # For full model which can't be separated to backbone, neck and head.
    backbone: ~ # Model backbone configuration.
    neck: ~ # Model neck configuration.
    head: ~ # Model head configuration.

Field list

Field	Description
full	(dict) If the model does not distinctly separated to backbone, neck, and head, the model's details are defined under this field. If this field is not None, the backbone, neck, and head fields are ignored.
backbone	(dict) This field defines the model's backbone, applicable only when the full field is None.
neck	(dict) This field defines the model's neck, applicable only when the full field is None. This can be None anytime because the necessity of the neck module may vary depending on the task.
head	(dict) This field defines the model's head, applicable only when the full field is None.