jpainam/SLS_ReID - Githubissues

SLS ReID

Datasets

Dataset preperation

Change the download_path to point to your dataset folder. Comment out multi-query except for Market1501 dataset.

python prepare.py
python re_index.py

Training

Train the baseline
```
python train.py 
```
Train SLS_ReID
```
python sls_train.py 
```
Add --use_dense argument to train using DenseNet121 architecture

Pre-trained models

Dataset	Dense Baseline	ResNet Baseline	Dense SLS_ReID	ResNet SLS_ReID
Market-1501	market/dense.pth	market/resnet.pth	cuhk03/dense_slsreid.pth	market/resnet_slsreid.pth
CUHK03	cuhk03/dense.pth	cuhk03/resnet.pth	cuhk03/dense_slsreid.pth	cuhk03/resnet_slsreid.pth
VIPeR	viper/dense.pth	viper/resnet.pth	viper/dense_slsreid.pth	viper/resnet_slsreid.pth
DukeMTMCReID	duke/dense.pth	duke/resnet.pth	duke/dense_slsreid.pth	duke/resnet_slsreid.pth

To generate the GAN label (gan%.list ie gan0.list, gan1.list and gan2.list for three cluster ), run generate_labels_for_gan:

 python prepare_gan_data.py

Testing

python test_cuhk03.py --model_path ./cuhk03/model.pth --use_dense
python eval_cuhk03.py
----------
python test_viper.py --model_path ./viper/model.pth --use_dense
%Add --re_rank to get re-ranking with k-reciprocal encoding
----------
python test.py --model_path ./market/model.pth --use_dense
python evaluate.py
python evaluate_rerank.py 
%Add --multi for multi-query evaluation

Currents results

Dataset	Rank 1	Rank 5	Rank 10	Rank 20	mAP
`CUHK03-Dense Baseline`	67.92%	90.94%	95.35%	97.78%	78.10%
`CUHK03-Dense SLS_ReID`	84.32%	97.13%	98.92%	99.63%	89.92%
`CUHK03-ResNet Baseline`	75.02%	95.08%	97.92%	99.11%	83.87%
`CUHK03-ResNet SLS_ReID`	90.99%	98.24%	99.25%	99.74%	94.18%
`VIPeR-Dense Baseline`	63.45%	72.78%	79.11%	86.23%	-
`VIPeR-Dense SLS_ReID`	67.41%	81.01%	88.61%	93.51%	-
`Market-1501-Dense Baseline`	90.05%	96.82%	98.10%	98.81%	74.16%
`Market-1501-Dense SLS_ReID`	92.43%	97.27%	98.40%	-	79.08%
`Market-1501-ResNet Baseline`	87.29%	95.57%	96.94%	-	69.70%
`Market-1501-ResNet SLS_ReID`	89.16%	95.78%	97.33%	-	75.15%
`DukeMTMC-ReID-Dense Baseline`	79.67%	89.85%	92.86%	95.11%	63.19%
`DukeMTMC-ReID-Dense SLS_ReID`	82.94%	91.69%	94.43%	95.96%	67.78%
`DukeMTMC-ReID-ResNet Baseline`	76.66%	87.83%	91.47%	93.76%	58.35%
`DukeMTMC-ReID-ResNet SLS_ReID`	76.53%	88.15%	91.02%	93.54%	60.79%

Multi-query results for Market-1501 dataset

Dataset	Rank 1	Rank 5	Rank 10	mAP
ResNet baseline	91.27%	96.85%	98.19%	76.94%
DenseNet baseline	92.90%	97.89%	98.69%	81.22%
Resnet SLS_ReID	92.25%	97.51%	98.34%	81.92%
Dense SLS_ReID	94.06%	98.16%	98.84%	85.20%

References

@ARTICLE{AinamSLSR2018,
author={J. {Ainam} and K. {Qin} and G. {Liu} and G. {Luo}}, 
journal={IEEE Access}, 
title={Sparse Label Smoothing Regularization for Person Re-Identification}, 
year={2019}, 
volume={7},  
pages={27899-27910},  
doi={10.1109/ACCESS.2019.2901599}, 
ISSN={2169-3536}
}

jpainam / SLS_ReID

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