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Model VGG
The
Model VGG
node in AugeLab Studio represents the VGG model for deep learning tasks.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
The
Model VGG
node requires the following input:- Input Image: Connect an image data source to the node.
The
Model VGG
node outputs the created VGG model.- 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.
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.
- 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.
- 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.