[D]Why network runs much faster after loading the trained models(parameters)?
I’ve found that before loading trained models, the network runs at relatively low speed. After loading the .pth file, the speed of inference boosts about 10 times faster. Does this circumstance normally come in deep learning?
I’ve tried on SSD(single shot multibox detector) on object detection task of COCO dataset, the code is written in python with pytorch 1.0.
Before loading, I got 2-3 fps on GTX1080, and after that, it reached 20 fps on the same device under same environment.