Table 2.

Computational comparison between weight pruning-UNet and other models

Model	Parameters	Flops
UNet	5,680,353	62.4e
UNet (depth-wise + BN)	2,601,921	7.8e
WP-UNet (network pruning + depth-wise + BN)	1,297,441	7.2e

BN – Batch normalization; WP – Weight pruning