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AugeLab Studio Manual
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Model ResNet

Model ResNet Node Documentation

The Model ResNet node in AugeLab Studio represents the ResNet model for deep learning tasks.

Node Overview

The Model ResNet node allows you to create a ResNet model for image classification. It has the following properties:
  • Node Title: Model ResNet
  • Node ID: OP_NODE_AI_RESNET50

Inputs

The Model ResNet node requires the following input:
  • Input Image: Connect an image data source to the node.

Outputs

The Model ResNet node outputs the created ResNet model.

Node Interaction

  1. 1.
    Drag and drop the Model ResNet node from the node library onto the canvas in AugeLab Studio.
  2. 2.
    Connect an image data source to the node.
  3. 3.
    Configure the node properties:
    • Model Type: Choose the ResNet 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. 4.
    The ResNet model will be created and outputted by the node.

Implementation Details

The Model ResNet node is implemented as a subclass of the NodeCNN base class. It overrides the evalAi method to create the ResNet model.
  • The Model ResNet 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 ResNet 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 ResNet model, is returned as the output.

Usage

  1. 1.
    Drag and drop the Model ResNet node from the node library onto the canvas in AugeLab Studio.
  2. 2.
    Connect an image data source to the node.
  3. 3.
    Configure the node properties:
    • Model Type: Choose the ResNet model type (ResNet50, ResNet101, ResNet152, ResNet50V2, ResNet101V2, ResNet152V2).
    • Input Size: Specify the input size of the images.
    • Pooling: Choose the pooling mode (Maximum, Average, or None).
  4. 4.
    The ResNet model will be created based on the specified configuration.
  5. 5.
    Use the output ResNet model for image classification tasks or connect it to other nodes for further processing.

Notes

  • The Model ResNet node allows you to create ResNet 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 ResNet models have different variations and depths. 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 ResNet model can be used for image classification tasks.
  • Connect the output of the Model ResNet node to other nodes for further processing, such as training, evaluation, or prediction.
  • Experiment with different ResNet model types, input sizes, and pooling modes to achieve optimal results for your image classification tasks.
  • The Model ResNet node is particularly useful for computer vision tasks, such as object recognition or image classification.

    Model ResNet Node Documentation

    The Model ResNet node in AugeLab Studio represents the ResNet model for deep learning tasks.

    Node Overview

    The Model ResNet node allows you to create a ResNet model for image classification. It has the following properties:
    • Node Title: Model ResNet
    • Node ID: OP_NODE_AI_RESNET50

    Inputs

    The Model ResNet node requires the following input:
    • Input Image: Connect an image data source to the node.

    Outputs

    The Model ResNet node outputs the created ResNet model.

    Node Interaction

    1. 1.
      Drag and drop the Model ResNet node from the node library onto the canvas in AugeLab Studio.
    2. 2.
      Connect an image data source to the node.
    3. 3.
      Configure the node properties:
      • Model Type: Choose the ResNet 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. 4.
      The ResNet model will be created and outputted by the node.

    Implementation Details

    The Model ResNet node is implemented as a subclass of the NodeCNN base class. It overrides the evalAi method to create the ResNet model.
    • The Model ResNet 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 ResNet 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 ResNet model, is returned as the output.

    Usage

    1. 1.
      Drag and drop the Model ResNet node from the node library onto the canvas in AugeLab Studio.
    2. 2.
      Connect an image data source to the node.
    3. 3.
      Configure the node properties:
      • Model Type: Choose the ResNet model type (ResNet50, ResNet101, ResNet152, ResNet50V2, ResNet101V2, ResNet152V2).
      • Input Size: Specify the input size of the images.
      • Pooling: Choose the pooling mode (Maximum, Average, or None).
    4. 4.
      The ResNet model will be created based on the specified configuration.
    5. 5.
      Use the output ResNet model for image classification tasks or connect it to other nodes for further processing.

    Notes

    • The Model ResNet node allows you to create ResNet 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 ResNet models have different variations and depths. 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 ResNet model can be used for image classification tasks.
    • Connect the output of the Model ResNet node to other nodes for further processing, such as training, evaluation, or prediction.
    • Experiment with different ResNet model types, input sizes, and pooling modes to achieve optimal results for your image classification tasks.
    • The Model ResNet node is particularly useful for computer vision tasks, such as object recognition or image classification.
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