Combining Models in PrismRCL

Introduction #

PrismRCL introduces a powerful feature that allows users to combine multiple models into a single, more robust model. This capability is particularly useful when you have trained several models on different chunks of the same dataset using identical training parameters. This article provides a step-by-step guide on how to effectively merge multiple models using PrismRCL.

Requirements #

• Consistency in Training: Ensure all models to be combined were trained on parts of the same dataset and used the same training parameters.
• Version Compatibility: Models to be combined should be created with the same version of PrismRCL. Note that models created with version 2.4.0 and onwards are not compatible with earlier versions.

Command Overview #

To combine models, utilize the following command structure:

C:\PrismRCL\PrismRCL.exe loadmodel=c:\PrismRCL\models\basemodel.classify addmodel=c:\PrismRCL\models\model998.classify;c:\PrismRCL\models\model_999.classify savemodel=c:\PrismRCL\models\bestmodel.classify stopwhendone

• loadmodel: Specifies the path to the pre-trained base model that you wish to enhance with additional models.
• addmodel: Indicates the paths to the additional models to be merged with the base model. Separate multiple model paths with semicolons.
• savemodel: Designates the path where the final combined model will be saved.
• stopwhendone: Commands PrismRCL to automatically shut down after the combination process is completed.

Steps for Combining Models #

1. Prepare Models: Verify that all models you plan to combine were trained on the same dataset and with the same parameters.
2. Select Base Model: Choose one of your models as the base model. This will be the starting point for the combination process.
3. Specify Additional Models: Identify the models you wish to add to your base model. Ensure you have their paths ready.
4. Execute the Command: Adjust the command above with your specific model paths. Run this command in the command line, making sure you’re in the PrismRCL directory or have PrismRCL executable in your system’s PATH.
5. Monitor the Process: Although no specific parameters are required for combining models, it’s good practice to monitor the process through log files, if applicable, to ensure the combination process completes successfully.

Conclusion #

Combining models in PrismRCL is a straightforward process that can significantly enhance the performance and robustness of your AI models. By following the guidelines provided in this article, you can effectively merge multiple models into a single comprehensive model, maximizing the utility of your training sessions and datasets.