Super Model Introduction
Mech-Mind provides self-developed super models for carton and sack palletizing and depalletizing. The super models can be used directly for most projects to correctly segment objects without acquiring additional image data or training.
Usage Scenario
The super models can reliably recognize the following objects:
| Super Model for Cartons | |
|---|---|
Plain carton |
|
Patterned carton |
|
Carton with transparent tapes |
|
Carton with opaque tapes |
|
Strapped carton |
|
| Super Model for Sacks | |
|---|---|
Full sack |
|
Partially filled sack with wrinkles |
|
Download the Super Models
You can download the super models from Download Center. The super models include model packages (.dlkpack) that are used for deployment and models (.dlkmp) that are used for fine-tuning.
Deploy the Super Model Packages in Mech-Vision
You can use the Deep Learning Model Package Inference Step to import the super model packages and perform inference on images of cartons and sacks.
After the inference is completed, you can view the recognition results in Mech-Vision.
Fine-tune the Super Models in Mech-DLK
If the recognition results are not satisfactory, you can fine-tune the super models in Mech-DLK.
You can use the following method to fine-tune a model:
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Acquire images that report 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 acquired 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 (.dlkmp). -
In the Training parameters tab, lower the Learning rate properly. The Epochs can be reduced to 50–80.
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Complete model training and export the model.
After you fine-tune a super model, you can export it as a super model package (".dlkpack" or ".dlkpackC" file), then import it again into Mech-Vision for model package inference.