Convert to RQNet model and then IR Model
1. RQNet wconv -c <path/to/darknet/network/config> -i <path/to/darknet/weights> [-o <dir/for/output>]
2. RQNet openvino -n <path/to/rqnet/network/defintion> -w <path/to/rqnet/weights> [-o <dir/for/output>] [-d FP16|FP32] [-t model_name]
I tried. It works perfectly.
Let me know if you have questions or concerns.
Thank you Shubha.
I used Tiny YoloV3, but as far as I see, the model converted and run on NSC2 does not reproduce the precision as on my GPU.
Here I posted my codes, could you please help me confirm this? I doubt there may be some kind of layer which only used by Yolo has defects(since the MobileNet +SSD model works), but not sure.
I am now developing a CV based drone, and NCS2 is very useful in the project(in my verification codes) I get around 10fps on Raspberry Pi+NCS.
Hope I can do something to contribute to the eco-system of OpenVINO.
Shubha R. (Intel) wrote:
Dear Tsin, Ross,
Thank you for sharing such gold nuggets with the community !
We appreciate it,