A lightweight DNN network based on cuDNN
Still under development, Anyone who is interested can contact me via email r@uamgno.cn or wechat umango_ross
Windows 10 + Visual Studio 2019 + cuda11.1 + GTX 1060
RQNet train|eval|detect|demo|wconv|openvino [options]
To train a network:
RQNet train -d <path/to/data/defintions> -n <path/to/network/defintion> [-w <path/to/weights>]
weights file is .pb file. If weights file is not given, then a random set of weighs are initialized.
To eval a network:
RQNet eval -d <path/to/data/defintions> -n <path/to/network/defintion> -w <path/to/weights>
To detect objects in image:
RQNet detect -n <path/to/network/defintion> -w <path/to/weights> -i <path/to/image>
To detect objects in video:
RQNet demo -n <path/to/network/defintion> -w <path/to/weights> [-i <path/to/vedio>]
If input file is not given, then use a camera.
To convert .weights file to .pb files:
RQNet wconv -c <path/to/darknet/network/config> -i <path/to/darknet/weights> [-o <path/to/output>]
To convert RQNet model to openvino model(irv7), if "-p" option is not given, FP16 is used.
RQNet openvino -n <path/to/network/defintion> -w <path/to/weights> [-o <dir/to/output>] [-p FP16|FP32] [-name model_name]
This program is running only with CUDA support!
Any questions, email to r@umango.cn