DEEPCRAFT™ Studio 5.6 has arrived. Read more →
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  • Welcome Page
  • DEEPCRAFT™ Studio
    • Release notes
    • Download and Install DEEPCRAFT™ Studio
      • Machine learning Algorithms in DEEPCRAFT™ Studio
      • DEEPCRAFT™ Studio Accelerators and Project Templates
        • DEEPCRAFT™ Studio Accelerators
        • Project Templates
      • Graph UX
        • Graph UX Overview
      • Infineon Boards
        • PSOC™ Edge E84 Artificial Intelligence Kit
        • PSOC™ Edge E84 Evaluation Kit
        • PSOC™ 6 Artificial Intelligence Evaluation Kit
        • PSOC™ 6 Wi-Fi BT Pioneer Kit and IoT sense expansion kit
        • Other Infineon Boards
      • Streaming Protocol for Real-Time Data Collection
        • Registering sensors and boards for data streaming using Protocol version 2
        • Registering sensors and boards for data streaming using Protocol version 1
        • Flashing your custom firmware using ModusToolbox™
        • Studio Project File
          • Data Tab
    • Data Preparation
      • Real-time Data Collection
        • Real-time Data Collection using Infineon Boards
          • PSOC™ Edge E84 Artificial Intelligence Kit
            • Collect and label data using microphone sensor
            • Collect and label data using IMU sensor
            • Collect and label data using Magnetometer sensor
            • Collect and label data using Pressure and Temperature Sensor
            • Collect and label data using Humidity and Temperature Sensor
            • Collect and label data using using Radar sensor
            • Collect and label data using multiple sensors
          • PSOC™ Edge E84 Evaluation Kit
            • Collecting and labeling data using microphone sensor
            • Collecting and labeling data using IMU sensor
            • Collecting and labeling data using Magnetometer sensor
            • Collecting and Labeling data using multiple sensors
          • PSOC™ 6 Artificial Intelligence Evaluation Kit
            • Collecting and labeling data using microphone sensor
            • Collecting and labeling data using IMU sensor
            • Collecting and labeling data using Magnetometer sensor
            • Collecting and labeling data using Pressure Sensor
            • Collecting and labeling data using using Radar sensor
            • Collecting and Labeling data using multiple sensors
          • Data Collection with PSOC™ 6 AI Evaluation Kit using old streaming firmware/ Protocol version 1
          • Data Collection with PSOC™ 6 Wi-Fi BT Pioneer Kit using old streaming firmware/ Protocol version 1
        • Real-time Data Collection without using a kit
          • Image data collection and labeling using camera
      • Bring your own data
        • Bring your own data for classification and regression projects
        • Bring your own data for object detection projects
      • Adding data to classification and regression projects
      • Adding data to object detection projects
      • Data Labeling
        • Manual Labeling
        • ML Assisted Labeling
        • Sequential Labeling
        • Labeling Images
      • Data Distribution
        • Data Augmentation
        • Managing labels
        • Creating multiple label tracks
    • Preprocessing
    • Model Training
      • Generating model
      • Starting and Tracking Training Job
      • Downloading model files and Importing model predictions
      • Training Object Detection Model
    • Model Evaluation
        • Model evaluation using Confusion Matrix
        • Model explainability using Window Visualization and Grad-CAM
        • Evaluating model using Graph UX
      • Evaluate regression model
      • Evaluating Object Detection Model
    • Code Generation
      • Code Generation for Infineon PSoC™ 6 and PSOC™ Edge boards
      • Code Generation for Vision Models
      • Code Generation for Infineon AURIX, Traveo, XMC7200 and Other Development Boards
      • Code Generation using Graph UX
    • Model Deployment
      • Edge API
      • Model Deployment on Supported Infineon Boards
        • Deploy siren detection model on AURIX™ TC375 Lite Kit
        • Deploy model on PSOC™ 6 and PSOC™ Edge boards
        • Deploy vision model on PSOC™ Edge boards
        • Deploy siren detection model on TRAVEO™ T2G (KIT_T2G-B-E_LITE)
        • Deploy siren detection model on TRAVEO™ T2G (KIT_T2G-B-H_LITE)
        • Deploy siren detection model on XMC7200 Evaluation Kit
      • Model Deployment on Other Development Boards
      • Supported layers and functions
      • Creating custom layers or functions
      • IAR Embedded Workbench
      • Building Siren Detection machine learning model
        • Data Preparation
        • Preprocessing
        • Model Training
        • Code Generation and Model Deployment
      • Predicting Power Generation from Wind Turbine
    • Troubleshooting
      • DEEPCRAFT™ Studio Installer Issues
    • Frequently Asked Questions
  • DEEPCRAFT™ Model Converter
    • Install and Launch
    • Graphical User Interface
    • Command Line Interface
    • Deploy Code on PSOC™ Edge using the Code Example
    • Additional Resources: Setup, Compatibility, Formats
      • Setup and Run the PyTorch to TFLite Converter API with Docker or Podman
      • Supported Data Formats
      • Supported Layers and Operators
    • Troubleshooting
      • Degraded LSTM Model Performance
      • Insufficient model arena memory during deployment
  • DEEPCRAFT™ Ready Models
    • Testing using pre-compiled binary/hex files
    • Testing using static library in custom applications
  • Legal
    • Imagimob AI Model Evaluation License Agreement
    • DEEPCRAFT™ Studio Terms and Conditions
    • DEEPCRAFT™ Studio Licensing Metrics and Fees
    • DEEPCRAFT™ Model Converter Agreement
    • Privacy Policy for DEEPCRAFT™ Studio and connected services
    • Free Open Source Software (FOSS)
    • Ultralytics YOLO License Information

On This Page

  • Troubleshooting
Questions? Ask the experts in the DEEPCRAFT™ forum! (opens in a new tab)

Troubleshooting

This section provides information about how to troubleshoot a problem related to DEEPCRAFT™ Model Converter. The following section cover some of known issues with suggested solutions.

  • Degraded LSTM Model Performance
  • Insufficient model arena memory during deployment

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