The deployment process is divided intoFour key steps::
- environmental preparation: Ensure that the RKNPU2 driver is installed, and it is recommended to add a 4GB swap partition to avoid insufficient memory.
- model transformation: Convert Paddle model to RKNN format (requires settings)
ENABLE_RKNPU2_BACKEND=ONcap (a poem)RKNN2_TARGET_SOC=RK3588) - Deployment implementation: Enter
demos/vision/detection/paddledetection/rknpu2/pythondirectory, runpython infer.py --model_file picodet_s_416_coco_lcnet_rk3588.rknn --config_file picodet_s_416_coco_lcnet/infer_cfg.yml --image input.jpg - Performance Tuning: Monitor time consumption through VisualDL, adjust batch_size and other parameters
Note: The RK3588 requires specific compilation options, and dynamic dimensional inputs need to be configured during model conversion.
This answer comes from the articleFastDeploy: an open source tool for rapid deployment of AI modelsThe
May not be reproduced without permission:AI productivity tools " What is the exact process for deploying a target detection model on the RK3588 chip?
