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Model VGG

Model VGG Node Documentation
The Model VGG node in AugeLab Studio represents the VGG model for deep learning tasks.
Node Overview
The Model VGG node allows you to create a VGG model for image classification. It has the following properties:
Node Title: Model VGG
Node ID: OP_NODE_AI_VGG16
Inputs
The Model VGG node requires the following input:
Input Image: Connect an image data source to the node.
Outputs
The Model VGG node outputs the created VGG model.
Node Interaction
1.Drag and drop the Model VGG node from the node library onto the canvas in AugeLab Studio.
2.Connect an image data source to the node.
3.Configure the node properties:
Model Type: Choose the VGG model type from the dropdown list.
Input Size: Specify the input size of the images.
Pooling: Choose the pooling mode (Maximum, Average, or None).
4.The VGG model will be created and outputted by the node.
Implementation Details
The Model VGG node is implemented as a subclass of the NodeCNN base class. It overrides the evalAi method to create the VGG model.
The Model VGG node requires an image data source as input.
The node validates the input size and ensures it is greater than or equal to 32.
The node checks if the input images are in RGB format and raises an error if they are not.
The VGG model is created using the specified model type, input size, and pooling mode.
The created model is added to the PNNModel object.
The PNNModel object, containing the VGG model, is returned as the output.
Usage
1.Drag and drop the Model VGG node from the node library onto the canvas in AugeLab Studio.
2.Connect an image data source to the node.
3.Configure the node properties:
Model Type: Choose the VGG model type (VGG16, VGG19).
Input Size: Specify the input size of the images.
Pooling: Choose the pooling mode (Maximum, Average, or None).
4.The VGG model will be created based on the specified configuration.
5.Use the output VGG model for image classification tasks or connect it to other nodes for further processing.
Notes
The Model VGG node allows you to create VGG models for image classification.
It expects the Keras library to be installed.
The node requires an image data source as input.
The input images should be in RGB format. Grayscale images are not supported.
The VGG models have different variations, such as VGG16 and VGG19. Choose the appropriate model type based on your requirements.
The input size specifies the height and width dimensions of the input images. It should be greater than or equal to 32.
The pooling mode determines how the spatial dimensions of the output are reduced. Choose between Maximum, Average, or No pooling.
The output VGG model can be used for image classification tasks.
Connect the output of the Model VGG node to other nodes for further processing, such as training, evaluation, or prediction.
Experiment with different VGG model types, input sizes, and pooling modes to achieve optimal results for your image classification tasks.
The Model VGG node is particularly useful for computer vision tasks, such as object recognition or image classification.

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