AugeLab Studio Manual
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  • 👋Welcome to AugeLab Studio User Manual
  • 📘Introduction
    • AugeLab Studio
    • Key Features
    • Use Cases
    • System Requirements
  • 🚀Getting Started
    • Signing up
    • Installation
    • First Look
    • Simple Tour
    • Your Very First Project
      • Basics
      • Detection
      • Wrapping Up
    • More Local Examples
    • Further Reading
  • đŸ–Ĩī¸AugeLab Studio Interface
    • Detailed Look
    • Scenario Area
    • Menu and Toolbar
    • Managing Projects
    • Installing AI and much more
      • Leverage AI with Module Downloader
  • 🧱Function Blocks
    • Block Structures
    • Sockets
    • Blocks Column
    • Connections
    • All Function Blocks
      • AI Blocks
        • Face Detection
        • Mask Detection
        • Object Detection - Custom
        • Object Detection
        • Pose Estimation
        • Safety Equipment Detection
        • Social Distance Detector
        • Super Resolution
        • Text Detection
        • OCR
      • CNN Blocks
        • Average Pooling 2D
        • Batch Normalization
        • Choose Folder 2D
        • Compile Model
        • Conv. Sep. Layer 2D
        • Conv. Trans. Layer 2D
        • Convolutional Layer 2D
        • Dropout Layer
        • Flatten Layer 2D
        • Fully Connected
        • Global Average Pooling 2D
        • Global Max Pooling 2D
        • Input Layer 2D
        • Loss CCE
        • Max Pooling 2D
        • Metrics Accuracy
        • Model EfficientNet
        • Model MobileNet
        • Model ResNet
        • Model VGG
        • Optimizer Adadelta
        • Optimizer Adagrad
        • Optimizer Adam
        • Optimizer Adamax
        • Optimizer FTRL
        • Optimizer Nadam
        • Optimizer RMSProp
        • Optimizer SGD
        • ReLU Layer
        • Softmax Layer
        • Training Parameters
      • Data/Logic
        • Flow Control
          • Batch Concatenation
          • Batch Processing
          • Debatch
          • Get Batch Size
          • HMI Background
          • Subsystem Enabled
          • Subsystem In
          • Subsystem Loop
          • Subsystem Out
          • Subsystem
        • logic
          • All True
          • And
          • Demux
          • Equals
          • Greater
          • Logic Operations
          • Mux
          • Not
          • Or
          • Set - Reset
          • Smaller
        • Mathmetical Operations
          • Add
          • Counter
          • Divide
          • Math Operations
          • Maximum
          • Minimum
          • Multiply
          • Not Equals
          • Round
          • Square Root
          • Subtract
          • Trigonometry
        • Data Operations
          • Data Memory
          • Data to JSON
          • Data Type Converter
          • Datetime Compare
          • Dictionary Operations
          • Exclude Nones
          • Find Substring
          • Get Element
          • Is None
          • List Operations
          • Parse Data Dictionary
          • Replace None
          • String Merge
          • String Operations
        • Referencing
          • Data Read Global
          • Data Read Local
          • Data Write Global
          • Data Write Local
          • Debug Input
          • Tag From
          • Tag To
        • Signal Operators
          • Delay Step
          • Edge Falling
          • Edge Rising
          • Multi Port Switch
          • OFF Delay
          • ON Delay
      • Image/Transformations
        • Analysis
          • Color Density Percentage
          • Get Dimension
          • Histogram On Curve
          • Histogram On Line
          • Image Color Match
          • Image Memory
          • Image Resolution and Channel Value
          • Maximum Images
          • Mean Value of Image
          • Measure Position Distance
          • Minimum Images
          • Non-zero of Image
          • Std. of Image
          • Structural Similarity
        • Transformation Filters
          • Auto Alignment
          • Auto Contrast
          • Color Quantizer and Clustering
          • Color Space
          • Contrast-Brightness-Gamma
          • Contrast Optimization
          • Deconvolution
          • Denoising
          • Distance Transformation
          • FloodFill
          • Grab Cut Algorithm
        • Color Filters
          • 2D Filter
          • Apply Mask
          • Bilateral Filter
          • Blur
          • Edge Filter
          • HSV Filter
          • Image Adaptive Threshold
          • Image Threshold
          • Invert Image
          • Morphological Transformations
          • Normalize Image
          • RGB Mask
          • RGB Set
          • Sobel Filter
        • Operations
          • Add Images Weighted
          • Add Images
          • Collage Images
          • Divide Images
          • Flip Image
          • Image AutoRotator
          • Image Concatenate
          • Image Resize
          • Image Resizer
          • Merge Channels
          • Multiply Images
          • Polar Transform
          • Rotate Image Angle
          • Slice Image
          • Split Image
          • Subtract Images
      • Detections/Shapes
        • Detectors
          • Barcode Reader
          • Blob Detector
          • Blur Detector
          • Circle Detector
          • Corner Detector
          • Custom CNN Model
          • Data Matrix Reader
          • Detect Reference
          • Feature Detector
          • Find Object - Multiple Image
          • Find Object
          • Find Reference
          • Harris Corner Filter
          • Line Detector
          • Match Shapes
          • Measure Object Distance
          • Shape Detector
        • Draw
          • Draw Detections
          • Draw Line
          • Draw Point
          • Draw Rectangle
          • Draw Result On Image
          • Write Date On Image
          • Write Text On Image
        • Roi Processing
          • Check Area (Polygon)
          • Check Area
          • Get Pixel Mouse
          • Get Pixel
          • Get ROI
          • Image ROI Center
          • Image ROI Polygon
          • Image ROI Select Multi
          • Image ROI Select
          • Image ROI
          • Perspective Transform
          • Rectangles in Rectangle
        • Shape Analysis
          • Approximate Contour
          • Choose Line
          • Contour to Image
          • Fill Contour
          • Find Contour
          • Hull Convex
          • Minimum Circle
          • Minimum Ellipse
          • Minimum Rectangle
          • Minimum Rotated Rectangle
          • Most Similar Shape
          • Point Polygon Test
      • Input/Output
        • Communication
          • Modbus Connect
          • Modbus Read
          • Modbus Write
          • MQTT Publish
          • MQTT Subscribe
          • OPC UA Client
          • OPC UA Read
          • OPC UA Write
          • REST API - Get
          • REST API - Post
          • Send Mail
          • Siemens S7 Connect
          • Siemens S7 Read
          • Siemens S7 Write
        • Data Inputs
          • Date-Time List
          • Date-Time
          • Headless Check
          • Keyboard/Barcode Reader
          • Logic Input
          • Number Input
          • Number Range
          • PWM (Pulse Width Modulation)
          • Rising Edge
          • String Input
          • Text
        • Image Inputs
          • Camera IP (ONVIF)
          • Camera IP
          • Camera USB External
          • Camera USB Vidgear
          • Camera USB
          • Load Image From Path
          • Load Image
          • Make Image
          • Pixel
          • Video
        • Outputs/Exports
          • CSV Export
          • Cycle Timer
          • File/Folder Operations
          • GPU Statistics
          • Image Logger
          • Image Write
          • Led Output
          • Multi Image Write
          • Output
          • Scope
          • Show Image
          • Stop
  • 📡Devices and Communication
    • Camera Usage
    • Communication Protocols
    • Further Reading
  • 🧩Example Projects
    • Demo Projects
    • Circumference Measurement
    • Object Counting
    • Tile Width Measurement
    • Human Detection
    • Object Detection
  • 🔑Key Features
    • Deploy Custom HMI Applications
    • Annotate Data for Object Detection
    • Train Custom AI Models
      • Choosing the Right Database
      • When to Stop Training
    • Create Plugins
      • Components
      • Coding Reference
    • Share Your Solutions with Community
    • Instal Python Packages
  • 📑FAQ
    • Contact Us
    • FAQ
    • Setting up a full project
  • Additional Resources
    • Training Schedule
    • Training Materials
    • AugeLab Experts
  • Appendix
    • Dictionary
    • References
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  • đŸ“Ĩ Inputs
  • 📤 Outputs
  • đŸ•šī¸ Controls
  • 🎨 Features
  • 📝 Usage Instructions
  • 📊 Evaluation
  • 💡 Tips and Tricks
  • đŸ› ī¸ Troubleshooting

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  1. Function Blocks
  2. All Function Blocks
  3. CNN Blocks

Conv. Sep. Layer 2D

This function block utilizes a separable convolution layer, which is a vital component in neural networks for processing image data. It focuses on optimizing the convolution operation by separating it into depthwise and pointwise convolutions, improving efficiency without sacrificing performance.

đŸ“Ĩ Inputs

The specific inputs will depend on the configurations set within the parent NodeCNN. Generally, this block receives features from the previous block, which might include image data or feature maps.

📤 Outputs

The block outputs processed feature maps after applying the separable convolution, which can be utilized in subsequent layers for further processing or classification.

đŸ•šī¸ Controls

Kernel Size This control allows you to set the size of the convolution kernel, which determines the size of the region that will be focused on during the convolution operation.

Activation Function Choose the activation function to apply after the convolution operation, such as ReLU, Sigmoid, etc.

Filters Specify the number of filters to be used in the layer, impacting how many feature maps will be generated.

Padding Decide whether to use padding, which can affect the dimensionality of the output.

🎨 Features

Layer Optimization By implementing separable convolutions, this layer optimizes computational efficiency while maintaining the effectiveness of feature extraction.

Dynamic Configurations Users can adjust kernel sizes, activation functions, and filters to customize the convolution processing based on specific image data or use cases.

📝 Usage Instructions

  1. Connect to Previous Layer: Ensure the input is connected to a preceding layer that provides suitable feature data for convolution.

  2. Configure Layer Parameters: Set the desired number of filters, kernel size, and activation functions as per your model requirements.

  3. Evaluate: Run the block to process the input data through the separable convolution layer.

📊 Evaluation

When executed, the function block applies the separable convolution process on the input data, creating refined feature maps that can be utilized in downstream layers of your neural network.

💡 Tips and Tricks

Kernel Size Impacts

Experiment with different kernel sizes. Smaller kernels can effectively extract more features from the fine details of images, while larger kernels can capture more context.

Adjusting Filters

Increasing the number of filters allows the model to capture more complex features. Start with a small number and increase as needed, based on validation performance.

Monitor Performance

Use Debug Input or Image Logger to check the outputs at this layer, ensuring that the features are being extracted correctly and effectively.

đŸ› ī¸ Troubleshooting

No Output Produced

Ensure that there is a valid connection from the preceding layer and that the inputs are in the expected shape for processing.

Invalid Layer Configuration

Double-check your configurations such as kernel size, filters, and activation functions to ensure they are set appropriately for your model structure.

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Last updated 8 months ago

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