The paper discusses various approaches used to optimize the convolutional neuralnetwork (CNN) model with You Only Look Once (YOLO) version 3 architecture. At the same time, an implementation using graphic processing ...
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The paper discusses various approaches used to optimize the convolutional neuralnetwork (CNN) model with You Only Look Once (YOLO) version 3 architecture. At the same time, an implementation using graphic processing units (GPU) computing is used as a baseline performance estimate. A separate study was conducted on the intel Core i5-8500 central processing unit (CPU). Both the classical methods of the neuralnetwork approach, such as model pruning, and effective methods for processing video data, in particular, the optical flow and speed prediction, are considered. The proposed algorithm made it possible to overclock the GPU computing of the NVIDIA RTX 2080 Super video card by 4 times, up to almost 30 frames per second. However, processing frames using intel open visual inference and neural network optimization (openVINO) toolkit allows us to achieve similar performance on the CPU without optical flow and speed extrapolation. In this case, the acceleration reaches almost 30 times.
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