Accelerating Face Mask Detection Training Model Based on Multi-GPUs and Multi-core CPU

Modern machine-learning applications require GPUs, and modern platforms can leverage numerous GPUs on one or more machines to increase performance. Contemporary deep-learning models are too huge for CPU or GPU training. Training these models with many GPUs without performance degradation is necessary to train them rapidly and maximize GPU consumption. Thus, training deep convolutional neural networks (DCNN) with multiple GPUs has become necessary for improving training. Therefore, we presented a parallel design and development of an efficient model for enhancing face mask CNN performance and improving resource efficiency. This DCNN model is a parallel training system over multiple GPUs, a multi-core CPU, and a multi-process GPU platform with large batch size and learning rate involvement to optimize resource use across storage, configuration and scaling using large datasets. the proposed model contains two parts, the first one is used for specifying and extracting the faces using the Haar Cascade classifier, and the second one considers the core part that extracts features from facial images for classification. As a result, the average speed of a multi-GPU is about 2.7 times faster than the GPU and about 3.2 times faster than the CPU. Also, according to our evaluation results, the training time obtained using multiple GPUs and multiple processes is much smaller than that obtained using a single GPU single process. Parallel training on multiple GPUs improves GPU resource utilization and training throughput. This model reflects significant accuracy compared to the other commonly used methods from relevant articles by achieving an Accuracy score of 99.5%.