Performance Evaluation of HPC algorithms in different architectures

In the last decades, image processing has moved from academic research to innovative consumer applications. One of the most valuable of these practical uses is in object detection: from an image, identify and locate elements. One family of object detectors based on deep learning is known as YOLO. The goal of this work is to benchmark an object detection model based on YOLO version 5 regarding detection metrics as well as timing and energy consumption. We evaluate the performance of a vehicle license plate detector as well as examine what software frameworks and hardware resources are most suitable for the task. We present the literature about object detection and discuss performance metrics. We describe the dataset of license plates curated for the experiments and the training procedure. Then, we present performance results for different software stacks, with PyTorch as the baseline, and hardware equipment, CPUs and GPUs part of the DaVinci-1 cluster at Leonardo SpA and Intel Developer Cloud Beta. Besides that, energy consumption results are also discussed. Finally, we evaluated the effect of larger image sizes on the inference time as well as of grouping images in the same batch for processing.