The benchmarks on this part are based mostly on a custom-trained YOLO mannequin containing over 200 object lessons, evaluated on a dataset of roughly 3,500 real-world pictures.
On this weblog, I discover and evaluate the inference efficiency of varied deep studying runtimes — PyTorch (CPU & GPU), ONNX Runtime (CPU & GPU), and TensorRT — utilizing a custom-trained object detection mannequin.
The mannequin was educated on 200+ lessons, and inference was carried out on a dataset of roughly 3,500 real-world pictures.
This analysis goals to reply important deployment questions:
How a lot quicker is GPU inference in comparison with CPU?
How does ONNX Runtime carry out relative to native PyTorch?
Is TensorRT value the additional engineering effort?
To make sure a good comparability, I used the identical pre-trained mannequin exported in several codecs and processed every picture uniformly with correct error dealing with. The dataset consists of a variety of object lessons, permitting us to look at detection consistency and runtime variation in sensible situations.
However first, let’s establish the {hardware} I used for this benchmark.
