Yolov8 not working in example ultralytics/examples/YOLOv8-CPP-Inference about ultralytics

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Install

Pip install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

Environments

YOLOv8 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Status

If this badge is green, all Ultralytics CI tests are currently passing. CI tests verify correct operation of all YOLOv8 Modes and Tasks on macOS, Windows, and Ubuntu every 24 hours and on every commit.

[glenn-jocher]

@mohkan1 hey there! Thanks for providing detailed information about the issue you're encountering with YOLOv8 in the YOLOv8-CPP-Inference example.

From your description, it looks like when switching models from YOLOv5 to YOLOv8, you encounter distinct results or possibly an error. A common cause of such problems can be related to differences in model inputs and outputs structure, or ONNX model conversion specificities.

Let's start troubleshooting with the following:

1. Model Input/Output Check: Ensure the model inputs and outputs are correctly configured for YOLOv8. Differences in input dimensions or preprocessing could cause issues.

2. ONNX Model Verification: Double-check that the YOLOv8 ONNX model was correctly converted and isn't corrupted. Re-export it if necessary.

3. Code Adjustments: Make sure all model-specific parameters (e.g., input size, class names, anchors etc.) align with YOLOv8's specifications.

4. Dependencies: Confirm that all dependencies particularly OpenCV and ONNX are up to date, as outdated versions might lead to unexpected behaviors.

If these steps don't resolve the issue, please provide any error messages or odd behaviors you notice when you switch to YOLOv8. This will help further narrow down the problem! 🛠️