Mastodawn

Training and Deploying YOLOv5 for Electric Bicycle Recognition on RK3568 Platform👇
✅ Environment Setup: Getting started with Anaconda, PyCharm, and LabelImg.
✅ Training Pipeline: Fine-tuning YOLOv5 for custom object classes.
✅ Model Optimization: Converting to .rknn using RKNN-Toolkit2.
✅ Edge Deployment: Running real-time inference on the OK3568 SBC with optimized post-processing.

Detailed Guide:
https://www.forlinx.net/article_view_775.html

Train YOLOv5 and Deploy to RK3568: A Step-by-Step RKNN Conversion Guide - Blog - Forlinx Embedded Technology Co., Ltd.

Learn how to train a custom YOLOv5 model, convert .pt to RKNN, and deploy on RK3568. Covers environment setup, LabelImg, ONNX export, and rknpu2 runtime deployment.