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Ubuntu+opencv+cuda+cudnn Environment Installation Guide

Darknet Framework Introduction

Common Commands

Single Image Detection

Cd darknet ./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg

Yolo v3 COCO - Video Detection

Darknet detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights -ext_output test.mp4

Web Camera Usage (Tiny Version)

./darknet detector demo cfg/coco.data cfg/yolov3.cfg yolov3-tiny.weights http://192.168.1.16:8080/video?dummy=param.mjpg

Training Weight Download Link

Please refer to the provided link for pre-trained models.

Note on Third-Party Software Authorization

The use of third-party software and models is subject to respective licenses and permissions.

Prerequisites

Ensure CUDA and CuDNN compatibility before proceeding with framework integration.

Framework Compatibility

The framework is optimized for efficient processing of real-time data streams and video monitoring tasks.

Installation Guide

Follow the installation steps carefully:

Console Commands

All commands should be executed in the terminal within the darknet directory.

Usage注意事项

For the best performance, ensure your GPU drivers are properly installed and compatible with CUDA versions.

Troubleshooting Tips

If you encounter issues, refer to the official documentation or community forums for solutions and updates.

Software Authorization

Verify the legitimacy and compliance with all applicable regulations for software usage.

Final Notes

This guide is intended for reference and demonstration purposes only.

The official repository and documentation should be consulted for the latest updates and features.