Deep Learning Model Package Inference

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From Mech-Vision 1.7.2, the Deep Learning Model Package CPU Inference and Deep Learning Model Package Inference (Mech-DLK 2.2.0+) Steps are merged into the Deep Learning Model Package Inference Step, which supports two deep learning model files exported from Mech-DLK with the suffixes being .dlkpackC and .dlkpack.

Open the previous project with Mech-Vision 1.7.2, you can find the Deep Learning Model Package CPU Inference and Deep Learning Model Package Inference (Mech-DLK 2.2.0+) Steps are automatically replaced with the Deep Learning Model Package Inference Step.

Function

This Step performs inference with single model packages and cascaded model packages exported from Mech-DLK and outputs the inference result.

Currently, this Step performs model package inference in seven scenarios: classification, object detection, defect segmentation, instance segmentation, text detection, text recognition, and unsupervised segmentation.

This Step only supports model packages exported from Mech-DLK 2.2.0 or above.

From Mech-DLK 2.4.1, model packages can be divided into single model packages and cascaded model packages.

Single model package: There is only one deep learning model in the model package, such as an “Instance Segmentation” model.
Cascaded model package: Multiple models are cascaded in the model package, and the output result of the previous model is input to the next model. For example, there are two models, i.e., Object Detection and Instance Segmentation, in the model package, and the inference sequence is Object Detection Instance Segmentation. The output of the Object Detection model is input to the Instance Segmentation model.

When this Step performs inference with cascaded model packages, the Deep Learning Result Parser Step can parse the exported result.

Usage Scenario

This Step is usually used in classification, object detection, and defect segmentation scenarios. For a description of the compatibility of the Step, please refer to Compatibilities of Deep Learning Steps.

Input and Output

Single Model Package

When a single model package is imported, the input and output of this Step are shown below in the case of the Object Detection module.

deep learning model package inference input and output single

Cascaded Model Package

When a cascaded model package is imported, the input and output of this Step are shown below in the case of the cascaded modules of Object Detection + Defect Segmentation + Classification.

deep learning model package inference input and output multi

System Requirements

The following system requirements need to be met when using this Step.

CPU: needs to support the AVX2 instruction set and meets any of the following conditions:
- IPC or PC without any discrete graphics card: Intel i5-12400 or higher.
- IPC or PC with a discrete graphics card: Intel i7-6700 or higher, with the graphics card not lower than GeForce GTX 1660.
This Step has been thoroughly tested on Intel CPUs but has not been tested on AMD CPUs yet. Therefore, Intel CPUs are recommended.
GPU: GeForce GTX 1660 or above (if with a discrete graphics card).

Parameter Description

You can click the following links to check the corresponding parameter descriptions of each function.

When using this Step for inference with the cascaded model package, you can adjust the parameters in the next Step Deep Learning Result Parser.

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