Skip to content

Latest commit

 

History

History
51 lines (42 loc) · 2.22 KB

README.md

File metadata and controls

51 lines (42 loc) · 2.22 KB
Raw

Mutiple Granularity Network(MGN)-ReId

Reproduction of paper：Learning Discriminative Features with Multiple Granularitiesfor Person Re-Identification

The architecture of MGN

The architecture of MGN

Composition

MGN based on Tensorflow:

  • export_pb.py ： export tensorflow pb file；
  • export_pb_with_pre _post.py ： export tensorflow pb file containing preprocess and postprocess；
  • export_tf_serving_model.py ： export the tensorflow serving model from tensorflow pb；
  • export_tf_serving_with_pre_post.py ： export the tensorflow serving model containing preprocess and postprocess from tensorflow pb；

Train

  • Download dataset Market1501；
  • Parameter initialization using the pytorch model which is trained by seathiefwang(Optional)；
  • Set training parameters and training paths in train.py and start training;

Inference

The architecture of MGN export_pb_with_pre _post.py and export_tf_serving_with_pre_post.py pack these operations into the model.

result

Results without re-ranking on Market-1501

map rank@1 rank@3 rank@5 rank@10
0.874288 0.947150 0.975653 0.984857 0.990499

Reference

https://github.com/seathiefwang/MGN-pytorch

https://github.com/lwplw/reid-mgn

@ARTICLE{2018arXiv180401438W,
    author = {{Wang}, G. and {Yuan}, Y. and {Chen}, X. and {Li}, J. and {Zhou}, X.},
    title = "{Learning Discriminative Features with Multiple Granularities for Person Re-Identification}",
    journal = {ArXiv e-prints},
    archivePrefix = "arXiv",
    eprint = {1804.01438},
    primaryClass = "cs.CV",
    keywords = {Computer Science - Computer Vision and Pattern Recognition},
    year = 2018,
    month = apr,
    adsurl = {http://adsabs.harvard.edu/abs/2018arXiv180401438W},
    adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}