Multi-Model Package

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Function

Use multiple model packages for inference of input images. The Multi-Model Package integrates multiple sub-models (such as Image Classification, Object Detection, and Defect Segmentation) and executes them collaboratively based on predefined combination logic: series, parallel, or series-parallel. It outputs the results of each sub-model along with the overall validation status.

This model is suitable for complex quality inspection scenarios, effectively reducing the number of projects, avoiding model duplication, and improving model usage and on-site maintenance efficiency.

Input and Output

After importing the model package in the Deep Learning Model Package Inference Step, the following input and output ports will be displayed.

Input

Input port Data type Description

Image

Image/Color

Image input to this port will be used for deep learning model package inference.

Output

Output port Data type Description

Comprehensive Validation Result

Bool

The comprehensive validation result of multiple model inferences. Outputs True if all inferences pass validation; otherwise, outputs False.

Classification

DLResult/Classification

Model package inference result.

Fast Positioning

DLResult/FastLocating

Model package inference result.

Text Detection

DLResult/TextDetection

Model package inference result.

Unsupervised Segmentation

DLResult/UnsupervisedSegmentation

Model package inference result.

Defect Segmentation

DLResult/DefectSegmentation

Model package inference result.

Object Detection

DLResult/ObjectDetection

Model package inference result.

Instance segmentation

DLResult/InstanceSegmentation

Model package inference result.

Text Recognition

DLResult/TextRecognition

Model package inference result.

Parameter Description

The following parameters need to be adjusted when the multiple model package is imported into this Step.

Model Package Settings

Parameter Description

Model Manager Tool

Parameter description: This parameter is used to open the deep learning model package management tool and import the deep learning model package. The model package file is a “.dlkpack” file exported by Mech-DLK.
Tuning instruction: Please refer to Deep Learning Model Package Management Tool for the usage.

Model Name

Parameter description: This parameter is used to select the model package that has been imported for this Step.
Tuning instruction: Once you have imported the deep learning model by using the deep learning model package management tool, you can select the corresponding model name in the drop-down list.

Release Original Model Package After Switching

Parameter description: This parameter determines whether to release the resources occupied by the original model package immediately when the model package is switched.
Default setting: Selected.
Instruction: Once this option is selected, when the Step switches to another model package, the system will immediately release the original model package resources, even if the model package is still in use by other Steps. When this option is not selected, the system will automatically release the original model package only when it is no longer used by any Steps.

Model Package Type

Parameter description: Once a Model Name is selected, the DI Algo Type Translated String will be filled automatically.

GPU ID

Parameter description: This parameter is used to select the device ID of the GPU that will be used for the inference.
Tuning instruction: Once you have selected the model name, you can select the GPU ID in the drop-down list of this parameter.

Preprocessing

Parameter Description

ROI File

Parameter description: This parameter is used to set or modify the ROI of the input image.

Tuning instruction: Once the deep learning model is imported, a default ROI will be applied. If you need to edit the ROI, click Open the editor. Edit the ROI in the pop-up Set ROI window, and fill in the ROI name.

Before the inference, please check whether the ROI set here is consistent with the one set in Mech-DLK. If not, the recognition result may be affected.

During the inference, the ROI set during model training, i.e. the default ROI, is usually used. If the position of the object changes in the camera’s field of view, please adjust the ROI.

If you would like to use the default ROI again, please delete the ROI file name below the Open the editor button.

Postprocessing

Parameter Description

Inference configuration

Parameter description: This parameter is used to configure parameters related to multi-model package inference. You can click Open the editor to open the inference configuration window.
Instruction: Please refer to Inference Configuration Tool for detailed parameter description.

Class Display Mode

Parameter description: Select to display classes in the output results by name or by index.

Visualization Settings

Parameter Description

Draw Result on Image

Description: Once enabled, the detection result will be displayed on the image.
Default value: Disabled.
Instruction: Set the parameter according to the actual requirement.

Visualization Method for Results

Parameter description: This parameter is used to specify the instance color scheme in the visualized output result.
Default setting: Display each instance
Value list: Display each instance, Display instances by class, Display instance center points
Instruction: Set the parameter according to the actual requirement. Please refer to the tuning examples for the corresponding result.

Customize font size

Parameter description: This parameter determines whether to customize the font size in the visualized outputs. Once this option is selected, you should set the Font Size (0–10).
Default value: Disabled.
Instruction: Set the parameter according to the actual requirement.

Font Size (0–10)

Parameter description: This parameter is used to set the font size of texts in the visualized outputs.
Default value: 3.0
Instruction: Set the parameter according to the actual requirement.

Tuning Examples

Visualization Method for Results

Visualization Method for Results Description Image

Display each instance

Visualize each instance in a unique color.

instances sample

Display instances by class

Visualize the displayed instances by class, and the objects of the same class have the same color.

classes sample

Display instance center point

Visualize the center point of an instance. The instance color is related to the “Confidence Threshold”.

central point sample

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