Need help with slower inference using YOLOv8 on NVIDIA Orin Nano 4GB

Issue Overview

Users are experiencing significantly slower inference speeds when running the YOLOv8 object detection model on the NVIDIA Orin Nano 4GB compared to the previous Jetson Nano. Specifically, inference times have increased from approximately 170 ms on the Jetson Nano to about 300 ms on the Orin Nano, despite the latter being marketed as a more powerful device. The issue arises after installing JetPack version 5.1.1 and setting up YOLOv8 via the Ultralytics documentation. Users have reported that while the model works correctly, the performance degradation is puzzling and impacts their project involving dynamic object counting.

Context

• Hardware: NVIDIA Orin Nano 4GB
• Object Detection Model: YOLOv8
• JetPack Version: 5.1.1
• Reported Inference Times:
  • Jetson Nano: ~170 ms
  • Orin Nano: ~300 ms

The problem seems consistent across different setups, indicating a potential systemic issue rather than an isolated case.

Possible Causes

1. Framework Compatibility:

   • Users have not utilized TensorRT for optimization, which may lead to suboptimal performance on the Orin Nano.

2. Configuration Errors:

   • The installation process may not have properly configured CUDA or other necessary dependencies, leading to reliance on CPU rather than GPU for inference tasks.

3. Driver Issues:

   • The JetPack version might have bugs or compatibility issues affecting performance.

4. Environmental Factors:

   • Power supply inconsistencies or thermal throttling could impact performance.

5. User Errors:

   • Potential misconfigurations during setup or inadequate environment setup (e.g., missing virtual environments or incorrect Python versions).

Troubleshooting Steps, Solutions & Fixes

Step-by-Step Diagnosis

1. Verify Framework and Dependencies:

   • Ensure that you are using a GPU-enabled version of PyTorch or ONNX Runtime.
   • Consider installing TensorRT for optimized inference.

2. Check CUDA Installation:

   • Run the following command to verify CUDA installation:

     nvcc --version
     

   • Confirm that your environment is set up to utilize CUDA.

3. Gather System Information:

   • Use nvidia-smi to check GPU utilization during inference.
   • Monitor system resources with htop or top to ensure no bottlenecks.

4. Test Different Configurations:

   • Try running YOLOv8 in a clean virtual environment with Python 3.8.
   • Clone the YOLOv8 repository again and ensure all dependencies are installed correctly.

5. Run Performance Benchmarks:

   • Compare performance with other models (like YOLOv5) to see if the issue is specific to YOLOv8.

Potential Fixes

• Install TensorRT:
  Follow these steps to install TensorRT:

  sudo apt-get install nvidia-tensorrt
  

• Use ONNX Runtime with GPU:
  Install ONNX Runtime optimized for GPU by following instructions from the official ONNX Runtime documentation.

• Optimize Model with TensorRT:
  Convert your YOLOv8 model to TensorRT format using the following command:

  import torch
  from models.common import DetectMultiBackend
  
  model = DetectMultiBackend('yolov8.pt', device='cuda')
  model.export(format='engine')
  

Best Practices for Future Prevention

• Always use the latest stable version of JetPack and related libraries.
• Regularly check for updates regarding optimizations and bug fixes in NVIDIA forums.
• Maintain a clean development environment to avoid dependency conflicts.

Unresolved Aspects

• Users have not confirmed whether switching to TensorRT resolves their performance issues.
• Further investigation may be needed into specific configurations that could be impacting inference speed on the Orin Nano compared to its predecessor.