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Sub-byte quantization of Mobile Face Recognition Convolutional Neural Networks
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Datum
2022
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Verlag
Gesellschaft für Informatik e.V.
Zusammenfassung
Converting convolutional neural networks such as MobileNets to a full integer representation
is already quite a popular method to reduce the size and computational footprint of classification
networks but its effect on face recognition networks is relatively unexplored. This work presents a
method to reduce the size of MobileFaceNet using sub-byte quantization of the weights and activations.
It was found that 8-bit and 4-bit versions of MobileFaceNet can be obtained with 98.68% and
98.63% accuracy on the LFW dataset which reduces the footprint to 25% and 12.5% of the original
weights respectively. Using mixed-precision, an accuracy of 98.17% can be achieved whilst requiring
only 10% of the original weight footprint. It is expected that with a larger training dataset, higher
accuracies can be achieved.