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目录
1. 前言
2.环境配置
(1) RK3588开发板Python环境
(2) PC转onnx和rknn的环境
3.PT模型转onnx
4. ONNX模型转RKNN
6.测试结果
1. 前言
yolov8就不介绍了，详细的请见YOLOV8详细对比，本文章注重实际的使用，从拿到yolov8的pt检测模型，怎样转onnx和rknn的使用，配置好相应的环境，就可以开心的做yolov8的检测啦！！！
2.环境配置
(1) RK3588开发板Python环境
numpy ==1.26.0
onnxruntime == 1.16.0
opencv-python== 4.8.0.76
rknn-toolkit-lite2 ==1.5.2
(2) PC转onnx和rknn的环境
Python == 3.10
ultralytics ==8.0.147
rknn_toolkit2 == 1.5.2
torch ==2.0.1
torchvision==0.15.2
相关的库缺少什么就安装，很简单的
3.PT模型转onnx
根据自己模型路径设置model_path，输入的shape也自己设置，我这里设置的是640*640，这样就得到了onnx模型，按道理是可以做推理了
from ultralytics import YOLO# pip install ultralyticsinput_width = 640
input_height = 640
model_path = "./models/yolov8x.pt"model = YOLO(model_path)
model.export(format="onnx", imgsz=[input_height, input_width], opset=12)4. ONNX模型转RKNN
rknn用FP16，用INT8设置了，但是量化效果精度有误差，用混合量化或者增加量化数据集，都可以试试(我偷懒就不尝试了)
from rknn.api import RKNNONNX_MODEL = 'yolov8n-sim.onnx'
RKNN_MODEL = 'yolov8n-sim-fp16.rknn'
IMG_PATH = './bus.jpg'
DATASET = './dataset.txt'if __name__ == '__main__':# Create RKNN objectrknn = RKNN()if not os.path.exists(ONNX_MODEL):print('model not exist')exit(-1)# pre-process configprint('--> Config model')rknn.config(mean_values=[[0, 0, 0]], std_values=[[1, 1, 1]], target_platform='rk3588')print('done')# Load ONNX modelprint('--> Loading model')ret = rknn.load_onnx(model=ONNX_MODEL)if ret != 0:print('Load yolov8 failed!')exit(ret)print('done')# Build modelprint('--> Building model')ret = rknn.build(do_quantization=False, dataset=DATASET)if ret != 0:print('Build yolov8 failed!')exit(ret)print('done')# Export RKNN modelprint('--> Export RKNN model')ret = rknn.export_rknn(RKNN_MODEL)if ret != 0:print('Export yolov8rknn failed!')exit(ret)print('done')# Ret = rknn.accuracy_analysis(inputs=['./bus.jpg'])# if ret != 0:# print('Accuracy analysis failed!')# exit(ret)# print('done')5. RK3588推理
代码使用的是https://github.com/ibaiGorordo/ONNX-YOLOv8-Object-DetectionibaiGorord博主的基础代码，在YOLOv8中集成了rknn模型的推理
import cv2
from yolov8 import YOLOv8# Initialize yolov8 object detector
# model_path = "./models/yolov8n-sim.onnx"
model_path = "./models/yolov8n-sim-fp16.rknn"
yolov8_detector = YOLOv8(model_path, conf_thres=0.3, iou_thres=0.5)
# Read image
img = cv2.imread('./bus.jpg')
# Detect Objects
boxes, scores, class_ids = yolov8_detector(img)
# Draw detections
combined_img = yolov8_detector.draw_detections(img)
cv2.imwrite("./detected_objects.png", combined_img)
6.测试结果
|
onnx推理结果 |
rknn推理结果 |
 |  |
使用的是yolov8n模型，检测的速度加速了8倍，量化成int8的话应该还要更加快！！！
完整的代码在资源中下载