/dev/null || { echo "NVIDIA driver not found, falling back to CPU-only mode..."; } # Common package installation command: pip install opencv-python numpy scikit-image tensorflow -i https://pypi.tuna.tsinghua.edu.cn/simple --no-cache-dir echo "Installation complete!" # Additional note from author: # If you're using a Mac M-series chip , you might need to use Rosetta emulation or install via Homebrew with --catalyst flag. Once you have se tools installed, let's dive into downloading actual YOLOv3 model files! Remember my advice from years of coding experience? Start simple and build up gradually! There are two main ways to get started: - Use pre-trained weights - Train from scratch I strongly recommend beginners start with pre-trained models because it lets you see immediate results and understand how everything works before going deeper. For this tutorial we'll focus on using pre-trained weights because I believe re's nothing more frustrating than spending hours debugging when you could be enjoying successful detections! This also connects perfectly to our previous section about hardware compatibility — since CPU implementations work best with optimized frameworks rar than raw custom layers... Anyway enough rambling — let's move forward! Now let me share a small secret that helped me debug many times during development... always check your file paths! No matter how experienced you are, path issues will trip you up at least once every few projects. So before we write any code let's prepare our directory structure properly. Example structure I personally use: project-root/ ├── yolov3_detector/ # Main application directory │ ├── configs/ # Configuration files │ ├── trained_models/ # Where all our downloaded models live! │ │ └── yolov3-tiny.weights │ │ ... or versions ... │ │ │ ├── images/ # Sample images for testing detection speed comparison between different configurations │ │ ... sample image files ... │ └── notebooks/ # Jupyter notebook examples if needed later but not covered here today! Let's now proceed carefully through each step — taking screenshots of my terminal session while executing commands helps visualize what each step does practically. When working on embedded systems like Raspberry Pi Zero W though remember different memory constraints apply — typically you'd need to use tiny version of YOLO which has fewer convolutional layers hence faster inference but slightly lower accuracy. In fact during my tests on RPi platform I often switch between using:

Here’s where we’d normally show detailed code snippets and step-by-step explanations...