目录
[yolo 11 包含分割模型:](#yolo 11 包含分割模型:)
[yolov11 github地址,说是17ms](#yolov11 github地址,说是17ms)
[yolov11 项目地址:](#yolov11 项目地址:)
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
python
import sys
from rknn.api import RKNN
DATASET_PATH = '../../../datasets/COCO/coco_subset_20.txt'
DEFAULT_RKNN_PATH = '../model/yolo11.rknn'
DEFAULT_QUANT = True
def 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_QUANT
if 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 = True
else:
do_quant = False
if len(sys.argv) > 4:
output_path = sys.argv[4]
else:
output_path = DEFAULT_RKNN_PATH
return model_path, platform, do_quant, output_path
if __name__ == '__main__':
model_path, platform, do_quant, output_path = parse_arg()
# Create RKNN object
rknn = RKNN(verbose=False)
# Pre-process config
print('--> Config model')
rknn.config(mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]], target_platform=platform )
print('done')
# Load model
print('--> Loading model')
ret = rknn.load_onnx(model=model_path)
if ret != 0:
print('Load model failed!')
exit(ret)
print('done')
# Build model
print('--> 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 model
print('--> Export rknn model')
ret = rknn.export_rknn(output_path)
if ret != 0:
print('Export rknn model failed!')
exit(ret)
print('done')
# Release
rknn.release()