Reproduction of paper:Learning Discriminative Features with Multiple Granularitiesfor Person Re-Identification
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;
- 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;
export_pb_with_pre _post.py
and export_tf_serving_with_pre_post.py
pack these operations into the model.
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 |
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}
}