To improve the effectiveness of the trained model, you can subject it to a re-training process. To start, go to the Models
section, hover over the gear icon and click the Re-train
button on the selected model tile.
![Model tile](https://docs.onestepai.com/static/model_creation_and_training/model_retraining/model_retraining_1.jpg)
Retraining process consists of three steps:
- Editing the dataset;
- Merging object classes;
- Configuring the model.
#Editing datasets
In the first step, select the datasets you want to use for the re-training process.
![Pick Categories](https://docs.onestepai.com/static/model_creation_and_training/model_retraining/model_retraining_2.jpg)
#Merging object classes
Clicking the Next
button takes you to the category merge view. It is slightly different from the original merge view:
- Categories from the previous training are automatically assigned to buckets
- If you have 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 that bucket
![Merge categories](https://docs.onestepai.com/static/model_creation_and_training/model_retraining/model_retraining_3.jpg)
Unassigned
bucket into the appropriate buckets. Only then will the Unassigned
bucket be automatically deleted and the Re-train
button be enabled. Each bucket must always contain at least one dataset category.![Merging categories with all classes assigned](https://docs.onestepai.com/static/model_creation_and_training/model_retraining/model_retraining_4.jpg)
#Model configuration
Click Next
to proceed to the configuring the model step. Define the name of the re-trained model and the number of epochs (for classification) or iterations (for object detection).
The model name
must meet the same validation requirements as for regular training.
![Model training parameters](https://docs.onestepai.com/static/model_creation_and_training/model_retraining/model_retraining_5.jpg)
Click Re-train
to start re-training the model.