Filter pruning based on evolutionary algorithms for person re-identification

Filter pruning has drawn much attention for releasing the limitations of person re-identification (ReID) in resource-constrained platforms due to the large parameters and high computational consumption. Filter pruning is depended on the given combination of pruning ratios to trim off unimportant filters, therefore, we define filter pruning as optimization problem. Existing strategies generally retrain all the pruning results and compare their performance iteratively, representing a major expenditure of time and effort. In this paper, we formally put forward a scheme of filter pruning based on evolutionary algorithms (EAFPruner) for ReID task for the first time, which automatically finds the optimal pruning structure in the pruning space with the better trade-off between computational cost and identification accuracy. Specifically, multiple evolutionary algorithms are adopted to solve the optimization problem by using mean average precision (mAP) as the fitness for filter-level sparsity. In addition, the adaptive batch normalization strategy is adopted to evaluate the pruned candidates generated by evolutionary algorithm without fine-tuning them to speed up training process. The experimental results are carried out on four person ReID datasets, and the number of parameters required by ResNet50 architecture is reduced by half while maintaining a comparable Rank-1 accuracy, which demonstrates the effectiveness of our proposed method.