Engine not working in detectnet but in deepstream-app

Issue Overview

Users are experiencing difficulties when attempting to run the detectnet.py script with a YOLOv8 model converted to ONNX format. While the model operates correctly with the deepstream-app, it fails to execute properly in detectnet.py, leading to unexpected outputs and errors.

Specific Symptoms

• Successful execution of deepstream-app -c deepstream_app_config.
• Failure when running detectnet.py, with output indicating issues with class descriptions and colors.
• The output logs show:
  • Initialization of CUDA engine context.
  • Binding information for inputs and outputs.
  • Warnings about not loading the expected number of class descriptions and colors.

Context

• The problem arises specifically when using a YOLOv8 model trained with Ultralytics, which has been converted to ONNX format.
• The user followed instructions from the DeepStream documentation for conversion.

Hardware/Software Specifications

• Device: Nvidia Jetson Orin Nano
• Software: Jetson Inference library, DeepStream SDK

Frequency and Impact

• The issue appears consistently when running detectnet.py with the converted model.
• This impacts the ability to utilize the model effectively for detection tasks, limiting functionality compared to using DeepStream.

Possible Causes

• Hardware Incompatibilities or Defects: The Jetson Orin Nano may have specific compatibility issues with certain YOLO versions or configurations.

• Software Bugs or Conflicts: There may be bugs within the Jetson Inference library that prevent proper handling of YOLOv8 models.

• Configuration Errors: Incorrect configurations in the detectnet.py script or in the model’s parameters could lead to failures.

• Driver Issues: Outdated or incompatible drivers for CUDA or TensorRT might result in unexpected behavior.

• Environmental Factors: Power supply issues or overheating could potentially affect performance, although this is less likely given the nature of the problem.

• User Errors or Misconfigurations: The user may not have set up the model correctly, especially regarding input/output bindings or class definitions.

Troubleshooting Steps, Solutions & Fixes

1. Verify Model Compatibility:

   • Confirm that the version of Jetson Inference supports YOLOv8. If not, consider using an earlier version of YOLO that is known to work.

2. Update Parser:

   • Modify the parser in detectnet.py to accommodate the specific output format of YOLOv8. This may involve adjusting how class descriptions and colors are loaded.

3. Check Configuration Files:

   • Ensure that all configuration files used by detectnet.py match those expected by the YOLOv8 model. Pay special attention to class labels and colors.

4. Run Diagnostic Commands:

   • Use terminal commands to gather system information and logs:

     cat /proc/cpuinfo
     nvidia-smi
     

   • Check for any errors in logs that might indicate driver issues or hardware problems.

5. Test with Different Models:

   • Try running detectnet.py with a different, simpler model (e.g., YOLOv5) to isolate whether the issue is specific to YOLOv8.

6. Consult Documentation:

   • Review relevant documentation for both Jetson Inference and DeepStream, particularly any notes on supported models and configurations.

7. Update Software and Drivers:

   • Ensure that all software packages, including TensorRT and CUDA drivers, are up-to-date. Use:

     sudo apt update
     sudo apt upgrade
     

8. Seek Community Support:

   • If issues persist, consider posting detailed logs and configurations on forums dedicated to Jetson development for further assistance.

9. Recommended Approach:

   • As noted by users in the forum, if compatibility issues continue, it may be more effective to utilize DeepStream for running YOLO models rather than attempting to adapt detectnet.py.

10. Preventive Measures:

    • Regularly check for updates on libraries and frameworks used.
    • Maintain backups of working configurations for quick recovery from similar issues in the future.

Unresolved Aspects

Further investigation may be needed regarding support for newer YOLO variants within Jetson Inference, as well as community-driven solutions for adapting existing scripts to accommodate these models.