Abstract: To address the problems of high computational cost and large model parameters in unsupervised person re-identification models,a lightweight feature extraction network for unsupervised person re-identification is proposed.First,the Ghost Bottleneck is redesigned to compress the number of models parameters,and the ECA attention module is embedded into the lightweight backbone network to improve performance,enhance the network’s feature extraction capability,and solve the problem of feature loss caused by lightweight design.Second,a cluster-level dynamic memory dictionary and momentum update strategy are introduced to handle the embedding of unsupervised clustering features,which helps to alleviate the problem of feature inconsistency.Finally,pre-training is performed on the LUPerson dataset.A large number of experiments are carried out on common public datasets such as Market-1501,MSMT17,and PersonX.Compared with models such as PPLR,Cluster Contrast,and RTMem,the results show that LwFEN reduces the model parameters  by 24.3%,the computational amount（measured by floating point operations） by 28.12%,and improves the mAP of the model to 83.4%.

Key words: lightweight network, unsupervised pedestrian re-identification, dynamic memory dictionary, momentum update ,