Mutiple Granularity Network(MGN)-ReId

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

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

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}
}