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}
}