Hello every one,
I’m using a Jetson Nano 4GB with Yolov3 to apply the image detector in live video (I have a webcam - USB camera - Logitech camera).
When I run the command:
./darknet detector demo cfg / coco.data cfg / yolov3.cfg yolov3.weights
The camera works well, but it’s very slow, I only get 1.5 fps.
I tried to change the dimensions of the camera in Yolov3.cfg to (width=416, height=416)
but the live video is always (1280*960) very slow, but this change was already applied to the test photos when I run the command:
./darknet detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights data/dog.jpg.
how can I adjust the dimensions of the camera in live video, or should I do something different to get more fps for live video?
I use Jetson nano 4GB
JetPack: 4.5
Ubuntu: 18.04
Python: 3.6.9
Torch: 1.8.0
torchvision: v.0.9.0
OpenCV: ‘3.2.0’
That frame rate is about right for a nano running a full model yolov3 with that image size. From memory, in the past with the original PJ Reddie code is was around 600ms for inference with your settings.
Note, on the newer nano, the Orin, the frame rate of yolov7, a much better model, is just over 7 fps. But the memory required for yolov7 (pytorch based) needs the 8GB model.
The quality of recognition drops significantly with the tiny versions, for people detection I consider them unusable because of a very high false positive rate.