# Model retraining

In order to improve the effectiveness of the trained model, we can subject it to a retraining process. To start, click the Re-train button on the selected model tile.

Model tile
Model tile

Retraining process consists of three stages:

  • The first to edit dataset;
  • The second to merge object classes;
  • The third relates to model configuration.

# Editing datasets

In the first stage, we choose the datasets that we want to use for the retraining process.

Pick Categories
Pick Categories

# Merging object classes

By clicking the Next button, we go to the category merge view. It is slightly different from the original merge view:

  • Categories from the previous training are automatically assigned to buckets and marked in gray
  • If you selected new categories, they will be assigned to the automatically generated "Unassigned" bucket
  • If the name of a new category matches the name of an existing bucket, it will be automatically assigned to it

Merge categories
Merge categories

Merging categories with all classes assigned
Merging categories with all classes assigned

# Model configuration

Clicking Next takes you to the datasets view. In the last stage, define the name of the retrained model and the number of planned epochs (in the case of classification) or iterations (in the case of object detection).

The model name must meet the same validation requirements as for regular training (See training).

Model training parameters
Model training parameters