Awesome
A Simple Codebase for Image-based Person Re-identification
Requirements: Python 3.6, Pytorch 1.6.0, yacs
Supported losses
Classification Losses
Pairwise Losses
Supported models
Get Started
- Replace
_C.DATA.ROOT
and _C.OUTPUT
in configs/default.py
with your own data path
and output path
, respectively.
- Run
train.sh
Some Results
Market-1501
classification loss | pairwise loss | backbone | top-1 | mAP |
---|
CrossEntropy | Triplet | ResNet-50 | 94.5 | 86.6 |
CrossEntropy | Contrastive | ResNet-50 | 94.3 | 86.4 |
CrossEntropy | Cosface | ResNet-50 | 94.3 | 86.2 |
CELabelSmooth | Triplet | ResNet-50 | 95.0 | 87.4 |
CELabelSmooth | Contrastive | ResNet-50 | 94.5 | 87.1 |
CELabelSmooth | Cosface | ResNet-50 | 94.1 | 86.4 |
Cosface | Triplet | ResNet-50 | 95.1 | 86.7 |
Cosface | Cosface | ResNet-50 | 94.5 | 87.1 |
Arcface | Triplet | ResNet-50 | 94.2 | 86.3 |
Circle | Circle | ResNet-50 | 94.7 | 87.3 |
MSMT
classification loss | pairwise loss | backbone | top-1 | mAP |
---|
CrossEntropy | Triplet | ResNet-50 | 78.9 | 57.0 |
CrossEntropy | Contrastive | ResNet-50 | 79.3 | 56.7 |
CrossEntropy | Cosface | ResNet-50 | 78.2 | 55.2 |
CELabelSmooth | Triplet | ResNet-50 | 79.9 | 58.0 |
CELabelSmooth | Contrastive | ResNet-50 | 80.3 | 58.7 |
CELabelSmooth | Cosface | ResNet-50 | 79.2 | 56.6 |
Cosface | Triplet | ResNet-50 | 78.1 | 54.1 |
Cosface | Cosface | ResNet-50 | 78.8 | 55.9 |
Arcface | Triplet | ResNet-50 | 78.2 | 54.2 |
Circle | Circle | ResNet-50 | 79.7 | 57.0 |
Citation
If you use our code in your research or wish to refer to the baseline results, please use the following BibTeX entry.
@InProceedings{CVPR2019IANet
author = {Hou, Ruibing and Ma, Bingpeng and Chang, Hong and Gu, Xinqian and Shan, Shiguang and Chen, Xilin},
title = {Interaction-And-Aggregation Network for Person Re-Identification},
booktitle = {CVPR},
year = {2019}
}