The journey from a local training environment to real-time inference on embedded hardware can be a complex puzzle. I’ve just documented a complete pipeline for training a custom YOLOv5 model and successfully deploying it on the Rockchip RK3568 NPU.
The Workflow Highlights:
🛠️ Environment Setup: Navigating the specific Python 3.8/3.9 requirements for YOLOv5 and LabelImg on Windows 11.
🏷️ Dataset Engineering: Implementing a clean directory structure and YOLO-format annotation for high-quality training.
🔄 Model Transformation: Converting the PyTorch .pt weights into .onnx and finally into .rknn using the RKNN-Toolkit2 on Ubuntu 22.04.
🚀 Hardware Deployment: Compiling the C++ RKNPU2 demo and optimizing the post-processing headers for high-performance edge inference.
The result? A seamless transition from a standard PC environment to efficient, hardware-accelerated detection on an embedded development board.
Check out the full breakdown below! 👇
https://www.forlinx.net/industrial-news/yolov5-training-rk3568-rknn-deployment-guide-775.html
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