MobileNetV1 & MobileNetV3 matlab code for detecting wafer map defect patterns

Give MobileNetV1 & MobileNetV3 matlab code for identifying defect wafer map patterns

Answer 1

[Garmit Pant]

Hello Sharith

MATLAB currently supports MobileNetV2 as a built-in model, but support for MobileNetV1 and MobileNetV3 is not built-in. However, you can still accomplish your goal by leveraging the capabilities of the “imagePretrainedNetwork” function, which allows you to train models on your own data both with and without pretrained weights.

Here are two resources that will be helpful:

1. Using “imagePretrainedNetwork” Function

The “imagePretrainedNetwork” function in MATLAB loads a pretrained neural network which can be used to directly or with transfer learning. The following code snippet can be used to load MobileNetV2, both with and without pretrained weights:

% Load network with pretrained weights and user-defined number of classes
net = imagePretrainedNetwork(“mobilenetv2”, NumClasses=numClasses) 
% Load network without pretrained weights and user-defined number of classes
net = imagePretrainedNetwork(“mobilenetv2”, NumClasses=numClasses, Weights = “none”); 

You can refer to the documentation included to implement the workflow of fine-tuning the network it with your own dataset to adapt the network to your task.

Learn more about using imagePretrainedNetwork

Please note that to use the pre-trained weights of the MobileNetV2 network, you will need to install the “Deep Learning Toolbox Model for MobileNet-v2 Network” support package either from the ‘Add-on Explorer’ or from the following File Exchange link:

Deep Learning Toolbox Model for MobileNet-v2 Network

2. Classifying Defect Wafer Map Patterns Using CNN

This resource demonstrates how to classify defect wafer map patterns using a Convolutional Neural Network (CNN). It provides a complete workflow, from data preparation to training and evaluating the model.

Learn more about classifying defect wafer map patterns

To achieve your goal, you can merge the workflows from these two resources. Start by understanding how to use the “imagePretrainedNetwork” function to load and fine-tune a pretrained model (such as MobileNetV2). Then, follow the steps outlined in the second resource to prepare your wafer map dataset and train the model to classify defect patterns.

By combining these two approaches, you should be able to effectively classify defect wafer map patterns using a MobileNetV2 in MATLAB.

I hope you find the above explanation and suggestions useful!