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

[mohkan1]

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• [X] I have searched the YOLOv8 issues and found no similar bug report.

YOLOv8 Component

Other

Bug

The project structure of ultralytics/examples/YOLOv8-CPP-Inference

The code in the example ultralytics/examples/YOLOv8-CPP-Inference/main.cpp

#include <iostream>
#include <vector>
#include <getopt.h>

#include <opencv2/opencv.hpp>

#include "inference.h"

using namespace std;
using namespace cv;

int main(int argc, char ** argv)
{
    std::string projectBasePath =
      "/home/bober/Desktop/douchebag/ultralytics/examples/YOLOv8-CPP-Inference/";                             // Set your ultralytics base path

    bool runOnGPU = true;

    //
    // Pass in either:
    //
    // "yolov8s.onnx" or "yolov5s.onnx"
    //
    // To run Inference with yolov8/yolov5 (ONNX)
    //

    // Note that in this example the classes are hard-coded and 'classes.txt' is a place holder.
    Inference inf(projectBasePath + "yolov5s.onnx", cv::Size(640, 480),
      projectBasePath + "classes.txt",
      runOnGPU);

    std::vector<std::string> imageNames;
    imageNames.push_back(projectBasePath + "bus.jpg");
    imageNames.push_back(projectBasePath + "zidane.jpg");

    for (int i = 0; i < imageNames.size(); ++i) {
        cv::Mat frame = cv::imread(imageNames[i]);

        // Inference starts here...
        std::vector<Detection> output = inf.runInference(frame);

        int detections = output.size();
        std::cout << "Number of detections:" << detections << std::endl;

        for (int i = 0; i < detections; ++i) {
            Detection detection = output[i];

            cv::Rect box = detection.box;
            cv::Scalar color = detection.color;

            // Detection box
            cv::rectangle(frame, box, color, 2);

            // Detection box text
            std::string classString = detection.className + ' ' + std::to_string(
                detection.confidence).substr(0, 4);
            cv::Size textSize = cv::getTextSize(classString, cv::FONT_HERSHEY_DUPLEX, 1, 2, 0);
            cv::Rect textBox(box.x, box.y - 40, textSize.width + 10, textSize.height + 20);

            cv::rectangle(frame, textBox, color, cv::FILLED);
            cv::putText(
                frame, classString, cv::Point(
                    box.x + 5,
                    box.y - 10), cv::FONT_HERSHEY_DUPLEX, 1, cv::Scalar(
                    0, 0, 0), 2, 0);
        }
        // Inference ends here...

        // This is only for preview purposes
        float scale = 0.8;
        cv::resize(frame, frame, cv::Size(frame.cols * scale, frame.rows * scale));
        cv::imshow("Inference", frame);

        cv::waitKey(-1);
    }
}

When using yolov5, it yeilds the following results:

But when using the yolov8, it yields the following:

Any idea why the model yolov8 not working here ?

Environment

No response

Minimal Reproducible Example

No response

Additional

No response

Are you willing to submit a PR?

• [ ] Yes I'd like to help by submitting a PR!

[github-actions[bot]]

👋 Hello @mohkan1, thank you for your interest in Ultralytics YOLOv8 🚀! We recommend a visit to the Docs for new users where you can find many Python and CLI usage examples and where many of the most common questions may already be answered.

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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! 🛠️

[mohkan1]

@glenn-jocher Thanks for your suggestions, I have solved the issue. It was wrong model input output.

But now I got a new issue and wondering if you have any idea about why this is happening and how one can solve it ?

I have sat the variable runOnGPU to true but it will always switches to CPU.