Fig 3 Comparison between original and optimized channel shuffle
mode.
Experiments and results: The network we proposed is trained and
tested using the PyTorch deep learning framework. The test dataset we
used is CIFAR100. The simplified ShuffleNetV2 network proposed in this
paper is compared with traditional networks, and the comparison results
are shown in the table Ⅰ. In the case of small input feature map size,
the network achieves a recognition accuracy improvement of 1.33% and
1.09% compared to the original network. At the same time, the number of
parameters is reduced from 1.4M to 1.22M, effectively reducing the
storage resources and data access on the hardware.