The invention discloses a
convolution kernel
pruning model compression method and device, which are used in the field of convolutional neural networks, and the
processing flow of the
convolution kernel
pruning model compression method comprises the following steps: firstly, for a use scene, training a
convolutional neural network by using a common model training method until convergence; calculating two-dimensional image entropies corresponding to all feature maps in the
convolution layer to form a convolution kernel importance array; then pre-
cutting the convolution kernel, further observing the performance change of the model, and finally determining whether the convolution kernel is determined to be
cut; after all convolutional
layers in the model are traversed, ending the process; according to the improved convolution kernel
pruning method, the convolution layer of a
convolutional neural network is pruned, and a two-dimensional image entropy is put forward to be used as a criterion for importance evaluation of a convolution kernel; according to the method, on the basis of ensuring the performance of the
convolutional neural network model, compared with traditional convolution kernel pruning, the convolutional layer is
cut to a greater extent, and a greater degree of
model compression effect is achieved.