Palmprint-recognitition and Face-recognitition Lightly Neural Network Baseline

Paper: "Bag of Tricks and A Strong Baseline for Deep Person Re-identification"[pdf]

We plan to release those lightly backbones for Palmprint-recognition on this baseline[link]. You can use this project on your edge GPU device. Otherwise, we provide new backbone like transformer, we actually select small model.

model	method	ERR	mAP
resnet34
repVGG-A0
GhostNetv2
ShuffleNetv2
MobileNetv3
PyramidTNT-Ti
G-Ghost RegNet
SqueezeNet
Swin-T

@inproceedings{luo2019bag,
  title={Bag of Tricks and A Strong Baseline for Deep Person Re-identification},
  author={Luo, Hao and Gu, Youzhi and Liao, Xingyu and Lai, Shenqi and Jiang, Wei},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops},
  year={2019}
}

Directory layout

.
├── config                  # hyperparameters settings
│   └── ...                 
├── datasets                # data loader
│   └── ...           
├── log                     # log and model weights             
├── loss                    # loss function code
│   └── ...   
├── model                   # model
│   └── ...  
├── processor               # training and testing procedures
│   └── ...    
├── solver                  # optimization code
│   └── ...   
├── tools                   # tools
│   └── ...
├── utils                   # metrics code
│   └── ...
├── train.py                # train code 
├── test.py                 # test code 
├── get_vis_result.py       # get visualized results 
├── docs                    # docs for readme              
└── README.md

Pipeline

Pretrained Model

Get Started

1. cd to folder where you want to download this repo

2. Run git clone https://github.com/zzk2021/Palmprint-recognition-Lightly-Neural-Network-Baseline.git

3. Install dependencies:

   • pytorch>=0.4
   • torchvision
   • cv2 (for preprocess)

Train

python train.py

Test

python test.py

To get visualized reID results, first create results folder in log dir, then:

python ./tools/get_vis_result.py

You will get the FAR-GAR and ROC curve, like:

Results