We have a project that detects faces and recognizes them. Project’s programming language is CPP. We use Caffe for detecting faces and MxNet Insightface for recognizing them. When we commented all recognition code, CUDA functions in detecting algorithm works on GPU. However, if we include recognition code and link MxNet library, MxNet functions works on GPU but CUDA functions in detecting algorithm does not work on GPU.
We understand this result by looking GPU, CPU usage.
Without Recognition → All cores of CPU is about 40%, GPU is 17%-27% (That means CUDA functions doesn’t work on CPU, works on GPU)
With Recognition → All cores of CPU is about 100%, GPU is 0%-15% (That means MxNet functions works on GPU and CUDA functions works on CPU)
And also we considered process time of detection and recognition algorithm.
Without Recognition → Detection algorithm takes about 60ms (which is expected)
With Recognition → Detection algorithm takes about 1000ms (which is unusual) and Recognition algorithm takes about 15ms (which is expected)
We couldn’t figure out why this happens. Even if you don’t have any solution, some theories and knowledge may save our time. Please share with us!
Jetson Xavier AGX → Jetpack 4.5.1