Multiple Granularity Network
Reproduction of paper:Learning Discriminative Features with Multiple Granularities for Person Re-Identification
Dependencies
- Python >= 3.5
- PyTorch >= 0.4.0
- TorchVision
- Matplotlib
- Argparse
- Sklearn
- Pillow
- Numpy
- Scipy
- Tqdm
Train
Prepare training data
Download Market1501 training data.here
Begin to train
In the demo.sh file, add the Market1501 directory to --datadir
run sh demo.sh
Result
|
mAP |
rank1 |
rank3 |
rank5 |
rank10 |
2018-7-22 |
92.17 |
94.60 |
96.53 |
97.06 |
98.01 |
2018-7-24 |
93.53 |
95.34 |
97.06 |
97.68 |
98.49 |
last |
93.83 |
95.78 |
97.21 |
97.83 |
98.43 |
Download model file in here
The architecture of Multiple Granularity Network (MGN)
Figure . Multiple Granularity Network architecture.
@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}
}