Importing a MATLAB Semantic Segmentation Model into a TensorFlow Environment and Running It

Steps for Importing and Running a MATLAB Semantic Segmentation Model in a TensorFlow Environment

Building a DeepLabV3+ Model in MATLAB

To build the DeepLabV3+ model in MATLAB using ResNet-18 as the backbone, you can follow the steps below:

1. Set the image size and number of classes: Define the size of the input images and the number of classes for your semantic segmentation task. In this case, the image size is 720x960 with 3 color channels, and the number of classes is 32.  Build the network: Use the deeplabv3plus function to build the model with the specified image size, number of classes, and ResNet-18 backbone.

Here is the MATLAB code for building the DeepLabV3+ model:

matlab
% Set image size and number of classes
imageSize = [720 960 3];
numClasses = 32;

% Build the DeepLabV3+ network with ResNet-18 backbone
network = deeplabv3plus(imageSize, numClasses, 'resnet18');
network = deeplabv3plus(imageSize, numClasses, 'mobilenetv2');
network = deeplabv3plus(imageSize, numClasses, "xception");
network = deeplabv3plus(imageSize, numClasses, 'InceptionResNetV2');
network = deeplabv3plus(imageSize, numClasses, 'resnet50');

This code will create a DeepLabV3+ model for semantic segmentation with the specified input image size and output class count, using ResNet-18 as the backbone architecture.

2. Export the Network to TensorFlow: Use the exportNetworkToTensorFlow function to export the model to TensorFlow format. The exported model will be saved in the Python package myModel.

matlab
exportNetworkToTensorFlow(net,"myModel");

To export from the tempdir: if the model in tempdir named "net_1" then export to tensorflow by 
exportNetwrorkToTensorFlow(net_1, "ModelName")

3. Load the Exported TensorFlow Model in Python: In Python, use the following code to load the exported TensorFlow model from the myModel package. The saved model should be copied and pasted to the folder where the ipynb file is located. 

python
import myModel
model = myModel.load_model()

4. Save the Exported Model in TensorFlow SavedModel Format: Saving the model in the SavedModel format is optional. You can directly perform deep learning workflows with the model. However, if you'd like to save it in the SavedModel format, use the following code:

python
model.save("myModelTF")