Model Iteration
When a model is put into use for some time, it might not cover certain scenarios. At this point, the model should be iterated. Usually, using more data to re-train the model can reach the iteration purpose, but it could reduce the overall recognition accuracy and might take a long time. Hence, Model Finetuning can be used to iterate the model, which can maintain the accuracy and save time.
General Model Iteration
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Collect images that lead to poor recognition results.
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Use Mech-DLK to open the project the model belongs to.
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Enable the Developer Mode by clicking .
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Add the collected images into the training and validation sets.
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Label the newly added images.
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Click and then enable Finetune.
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In the Training Parameters tab, lower the Learning rate properly. The Epochs can be reduced to 50–80.
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Complete the model training and export the model.
Super Model Iteration
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Collect images that lead to poor recognition results.
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Open Mech-DLK, create a New Project, and add the Instance Segmentation module.
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Enable the Developer Mode by clicking .
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Add the collected images into the training and validation sets.
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Label the newly added images.
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Click and then enable Finetune.
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Select Super model finetuning and click
to select the super model. -
In the Training Parameters tab, lower the Learning rate properly. The Epochs can be reduced to 50–80.
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Complete the model training and export the model.