Hi I have followed the tutorial on using Yolo object detector for my application.
I want to process the frames of a full HD image.
I can process each image in about 0.1 s (large image), but when I try to access the class_IDs or score outputs from the net (
class_IDs, scores, bounding_boxs = net(x)) the time required is around 0.5 s! I am just looping over the first 10 entries of class_IDs to check for a particular target.
I assume it is because these are gpu arrays. I have tried with cpu only, and the time is even slower.
However, when I do something similar with a numpy array, the time taken is about 0.01 s.
I have also tried converting class_IDs to a numpy array first using .asnumpy but the overall time is about the same.
I am using Windows. Is this something peculiar to Windows? Is there any way to rapidly check the entries of class_IDs so that I can process and identify objects in multiple images per second?