Iterate a Model

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If the model performs poorly after deployment, it can be improved through iteration. Usually, using more data to re-train the model can do the job, but it could reduce the overall recognition accuracy and might take a long time. Hence, model finetuning can be used to iterate the model while maintaining its accuracy and saving time.

This feature only works under the Developer Mode.

General Model Iteration

Models you trained can be fine-tuned using the following steps:

  1. Acquire images that report poor recognition results.

  2. Use Mech-DLK to open the project the model belongs to.

  3. Enable the Developer mode by clicking Settings  Settings.

  4. Import the collected image data and complete labeling.

  5. Click training parameter icon under the Training tab. On the Model finetuning tab of the Training Parameter Settings window, enable Finetune and select Self-finetuning.

  6. Navigate to the Training parameters tab, lower the Learning rate, and choose 50 to 80 for the Epochs.

  7. Click OK to save the parameter settings.

  8. Complete model training and export the model.

Deep Learning Model Iteration

The deep learning models are universal models developed by Mech-Mind. For more information about model introduction and usage instructions, see Deep Learning Model Introduction.

You can use the following method to fine-tune the deep learning models:

  1. Acquire images that report poor recognition results.

  2. Open Mech-DLK, create a new project, and add the corresponding module.

  3. Enable the Developer mode by clicking Settings  Settings.

  4. Import the collected image data and complete labeling.

  5. Click training parameter icon under the Training tab. In the Training Parameters window, go to the Model finetuning tab, enable Finetune, check the Deep learning model finetuning option, and then click model iteration folder to select a deep learning model (a .dlkmp file).

  6. On the Training parameters tab, lower the Learning rate properly. The Epochs can be reduced to 50–80.

  7. Complete model training and export the model.

Cascade Model Iteration

When you train cascade models, you can select a single module in the Modules area and perform the above operations to iterate the module.

If the previous module is updated, the following module needs to be re-trained. If escapes and overkills exist in a module, the module should be iterated. Then, in the following module, use the newly added images from the previous module as iteration data to iterate the following module.

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