rknn yolo11 推理

目录

提供了工具下载地址：

yolo 11 包含分割模型：

yolov11 github地址，说是17ms

yolov11 项目地址：

yolov5转rknn

onnx转rknn

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提供了工具下载地址：

https://github.com/rokkieluo/yolo11_convert_rknn

yolo 11 包含分割模型：

https://github.com/yuking926/RKNN-YOLO11

yolov11 github地址，说是17ms

https://github.com/cqu20160901/yolov11_dfl_rknn_Cplusplus/tree/main

yolov11 项目地址：

https://gitcode.com/qq_42910179/lxmyzzs/tree/main/yolo11_rk3588

yolov5转rknn

https://gitcode.com/oYeZhou/yolov5-rknn?source_module=search_result_repo

onnx转rknn

import sys
from rknn.api import RKNNDATASET_PATH = '../../../datasets/COCO/coco_subset_20.txt'
DEFAULT_RKNN_PATH = '../model/yolo11.rknn'
DEFAULT_QUANT = Truedef parse_arg():if len(sys.argv) < 3:print("Usage: python3 {} onnx_model_path [platform] [dtype(optional)] [output_rknn_path(optional)]".format(sys.argv[0]))print("       platform choose from [rk3562, rk3566, rk3568, rk3576, rk3588, rv1126b, rv1109, rv1126, rk1808]")print("       dtype choose from [i8, fp] for [rk3562, rk3566, rk3568, rk3576, rk3588, rv1126b]")print("       dtype choose from [u8, fp] for [rv1109, rv1126, rk1808]")exit(1)model_path = sys.argv[1]platform = sys.argv[2]do_quant = DEFAULT_QUANTif len(sys.argv) > 3:model_type = sys.argv[3]if model_type not in ['i8', 'u8', 'fp']:print("ERROR: Invalid model type: {}".format(model_type))exit(1)elif model_type in ['i8', 'u8']:do_quant = Trueelse:do_quant = Falseif len(sys.argv) > 4:output_path = sys.argv[4]else:output_path = DEFAULT_RKNN_PATHreturn model_path, platform, do_quant, output_pathif __name__ == '__main__':model_path, platform, do_quant, output_path = parse_arg()# Create RKNN objectrknn = RKNN(verbose=False)# Pre-process configprint('--> Config model')rknn.config(mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]], target_platform=platform )print('done')# Load modelprint('--> Loading model')ret = rknn.load_onnx(model=model_path)if ret != 0:print('Load model failed!')exit(ret)print('done')# Build modelprint('--> Building model')ret = rknn.build(do_quantization=do_quant, dataset=DATASET_PATH)if ret != 0:print('Build model failed!')exit(ret)print('done')# Export rknn modelprint('--> Export rknn model')ret = rknn.export_rknn(output_path)if ret != 0:print('Export rknn model failed!')exit(ret)print('done')# Releaserknn.release()