Lightweight Multi-Branch Network for Person Re-Identification

Pytorch implementation for the paper "Lightweight Multi-Branch Network for Person Re-Identification"

This repo supports easy dataset preparation, including Market-1501, DukeMTMC-ReID, CUHK03, MOT17, sota deep neural networks and various options(tricks) for reid, easy combination of different kinds of loss function, end-to-end training and evaluation and less package requirements.

List of functions

• Warm up cosine annealing learning rate
• Random erasing augmentation
• Cutout augmentation
• Drop Block and Batch Erasing
• Label smoothing(Cross Entropy loss)
• Triplet loss
• Multi-Simulatity loss
• Focal loss
• Center loss
• Ranked list loss
• Group Loss
• Different optimizers
• Attention modules
• BNNeck

Inplemented networks:

• Lightweight Multi-Branch Network(LightMBN), which we proposed
• PCB [link]
• MGN [link]
• Bag of tricks [link]
• OSNet [link]
• Batch Drop Block(BDB) for Person ReID [link]

Getting Started

The designed code architecture is concise and easy explicable, where the file engine.py defines the train/ test process and main.py controls the overall epochs, and the folders model, loss, optimizer including respective parts of neural network.

The user-friendly command-line module argparse helps us indicate different datasets, networks, loss functions, and tricks as we need, the detailed options/configurations are described in the bottom of this page.

If you don't have any dataset yet, run

git clone https://github.com/jixunbo/ReIDataset.git

to download Market-1501, DukeMTMC, CUHK03 and MOT17.

Create a virtual environment and install torch and torchvision. Then, install the remaining requirements:

pip install -r requirements.txt

To implement our Lightweight Multi-Branch Network with Multi-Similarity loss, run

python [path to repo]/main.py --datadir [path to datasets] --data_train market1501 --data_test market1501 --model LMBN_n --batchid 6 --batchimage 8 --batchtest 32 --test_every 20 --epochs 130 --loss 0.5*CrossEntropy+0.5*MSLoss --margin 0.7 --nGPU 1 --lr 6e-4 --optimizer ADAM --random_erasing --feats 512 --save '' --if_labelsmooth --w_cosine_annealing

Also, using pre-defined config file

python [path to repo]/main.py --config [path to repo]/lmbn_config.yaml --save ''

All logs, results and parameters will be saved in folder 'experiment'.

Note that, the option '--datadir' is the dataset root, which contains folder Market-1501, DukeMTMC-ReID etw..

'--data_train' and '--data_test' specify the name of train/test dataset, which we can train on one dataset but test on another dataset.

'--batchid 8' and '--batchimage 6' indicate that each batch contrains 8 persons, each person has 6 different images, totally 48 images.

'--epochs' is the epochs we'd like to train, while '--test_every 10' means evaluation will be excuted in every 10 epochs, the parameters of network and optimizer are updated after every every evaluation.

Actually, for the LightMBN model we have two kinds of backbone, LMBN_r we use ResNet50 as backbone, while LMBN_n is OSNet, OSNet contrains much less parameters but could achieve a little bit better performance than ResNet50.

Results

Model	Market1501	DukeMTMC-reID	CUHK03-D	CUHK03-L
LightMBN	96.3 (91.5)	92.1 (83.7)	85.4 (82.6)	87.2 (85.1)
+ re-rank	96.8 (95.3)	93.5 (90.2)	90.2 (90.6)	91.3 (92.2)
LightMBN(ResNet)	96.1 (90.5)	90.5 (82.0)	81.0 (79.2)	83.7 (82.5)
BoT	94.2 (85.4)	86.7 (75.8)
PCB	95.1 (86.3)	87.6 (76.6)
MGN	94.7 (87.5)	88.7 (79.4)

Note, Rank-1(mAP), the results are produced by our repo without re-ranking, models and configurations may differ from original paper.

Additionally, the evaluation metric method is the same as bag of tricks repo.

For training on cuhk03 dataset with MS Loss, the batch size of 8 x 8 would be recommended.

Pre-trained models

and correpondent config files can be found here .

If you have pretrained model and config file, run

python [path to repo]/main.py --test_only --config [path to repo]/lmbn_config.yaml --pre_train [path to pretrained model]

to see the performance of the model.

If you would like to re-inplement Bag of Tricks, run

python [path to repo]/main.py --datadir [path to datasets] --data_train market1501 --data_test market1501 --model ResNet50 --batchid 16 --batchimage 4 --batchtest 32 --test_every 10 --epochs 120 --save '' --decay_type step_40_70 --loss 0.5*CrossEntropy+0.5*Triplet --margin 0.3 --nGPU 1 --lr 3.5e-4 --optimizer ADAM --random_erasing --warmup 'linear' --if_labelsmooth

or

python [path to repo]/main.py --config [path to repo]/bag_of_tricks_config.yaml --save ''

If you would like to re-inplement PCB with powerful training tricks, run

python [path to repo]/main.py --datadir [path to datasets] --data_train Market1501 --data_test Market1501 --model PCB --batchid 8 --batchimage 8 --batchtest 32 --test_every 10 --epochs 120 --save '' --decay_type step_50_80_110 --loss 0.5*CrossEntropy+0.5*MSLoss --margin 0.7 --nGPU 1 --lr 5e-3 --optimizer SGD --random_erasing --warmup 'linear' --if_labelsmooth --bnneck --parts 3

Note that, the option '--parts' is used to set the number of stripes to be devided, original paper set 6.

And also, for MGN model run

python [path to repo]/main.py --datadir [path to datasets] --data_train Market1501 --data_test Market1501 --model MGN --batchid 16 --batchimage 4 --batchtest 32 --test_every 10 --epochs 120 --save '' --decay_type step_50_80_110 --loss 0.5*CrossEntropy+0.5*Triplet --margin 1.2 --nGPU 1 --lr 2e-4 --optimizer ADAM --random_erasing --warmup 'linear' --if_labelsmooth

Resume Training

If you want to resume training process, we assume you have the checkpoint file 'model-latest.pth', run

python [path to repo]/main.py --config [path to repo]/lmbn_config.yaml --load [path to checkpoint]

Of course, you can also set options individually using argparse command-line without config file.

Easy Implementation

Our code can be implemented easily online without installing any package or requirement even GPU in your own computer thanks to Google Colab, all the packages we need are Colab standard pre-installed packages.

Open this notebook, following the steps there and you can see the training process and results.

Please be sure that your are using Google's powerful GPU(Tesla P100 or T4).

The whole training process(120 epochs) takes ~9 hours. If you are hard-core player ^ ^ and you'd like to try different models or options, see Get Started as above.

Option Description

'--nThread': type=int, default=4, number of threads for data loading.

'--cpu', action='store_true', if raise, use cpu only.

'--nGPU', type=int, default=1, number of GPUs.

--config', type=str, default="", config path,if you have config file,use to set options, you don't need to input any option again.

'--datadir', type=str, is the dataset root, which contains folder Market-1501, DukeMTMC-ReID etw..

'--data_train' and '--data_test', type=str, specify the name of train/test dataset, which we can train on single or multiple datasets but test on another datasets, supported options: market1501, dukemtmc, MOT17, cuhk03_spilited(767/700 protocol) or e.g. market1501+dukemtmc.

'--batchid 6' and '--batchimage 8': type=int, indicate that each batch contrains 6 persons, each person has 8 different images, totally 48 images.

'--sampler', type=str,default='True', if 'True', sample batchid persons and batchimage in a batch, else, ramdom selected totally batchid*batchimage in a batch.

''--batchtest', type=int, default=32, total batch size for evaluation.

'--test_only', action='store_true', if raise, only run the evaluation.

'--save', type=str, default='test', name of the folder to save output, if '', then it will create the name using current time.

'--load', type=str, default='', name of the output folder, if there is a checkpoint file in the folder, it will resume trainning.

'--pre_train', type=str, default='', path of pre-trained model file.

'--epochs', type=int, is the epochs we'd like to train, while '--test_every 10' means evaluation will be excuted in every 10 epochs, the parameters of network and optimizer are updated after every every evaluation.

'--model', default='LMBN_n', name of model, options: LMBN_n, LMBN_r, ResNet50, PCB, MGN, etw..

'--loss', type=str, default='0.5*CrossEntropy+0.5*Triplet', you can combine different loss functions and corresponding weights, you can use only one loss function or 2 and more functions, e.g. '1*CrossEntropy', '0.5*CrossEntropy+0.5*MSLoss+0.0005*CenterLoss', options: CrossEntropy, Triplet, MSLoss, CenterLoss, Focal, GroupLoss.

'--margin', type=float, margin for Triplet and MSLoss.

'--if_labelsmooth', action='store_true', if raise, label smoothing on.

'--bnneck', action='store_true', if raise, use BNNeck, only for ResNet and PCB.

'--drop_block', action='store_true', if raise, use Batch Drop Block, and '--h_ratio 0.3 and --w_ratio 1.0' indicate the erased region on the feature maps.

'--pool', type=str, default='avg', choose pooling method, options: avg, max.

'--feats', type=int, default=256, dimension of feature maps for evaluation.

'--height', type=int, default=384, height of the input image.

''--width', type=int, default=128, width of the input image.

'--num_classes', type=int, default=751, number of classes of train dataset, but normally you don't need to set it, it'll be automatically setted depend on the dataset.

'--lr', type=float, default=6e-4, initial learning rate.

'--gamma', type=float, default=0.1,learning rate decay factor for step decay.

'--warmup', type=str, default='constant', learning rate warmup method, options: linear, constant.

'--w_cosine_annealing', action='store_true', if raise, use warm up cosine annealing learning rate scheduler.

'--pcb_different_lr', type=str, default='True', if 'True', use different lr only for PCB, if lr is 5e-3, then lr for classifier is 5e-3, lr for other part is 5e-4.

'--optimizer, default='ADAM', options: 'SGD','ADAM','NADAM','RMSprop'.

'--momentum', type=float, default=0.9, SGD momentum.

'--nesterov', action='store_true', if raise, SGD nesterov.

'--parts', type=int, default=6, is used to set the number of stripes to be devided, original paper set 6.

'--re_rank', action='store_true', 'if raise, use re-ranking.

'--cutout', action='store_true', if raise, use cutout augmentation.

'--random_erasing', action='store_true', if raise, use random erasing augmentation.

'--probability', type=float, default=0.5, probability of random erasing.

'--T', type=int, default=3, number of iterations of computing group loss.

'--num_anchors', type=int, default=1, number of iterations of computing group loss.

Acknowledgments

The codes was built on the top of deep-person-reid and reid-strong-baseline and MGN-pytorch , we thank the authors for sharing their code publicly.

Reference

If you want to cite LightMBN, please use the following BibTex:

@INPROCEEDINGS{9506733,
  author={Herzog, Fabian and Ji, Xunbo and Teepe, Torben and Hörmann, Stefan and Gilg, Johannes and Rigoll, Gerhard},
  booktitle={2021 IEEE International Conference on Image Processing (ICIP)}, 
  title={Lightweight Multi-Branch Network For Person Re-Identification}, 
  year={2021},
  volume={},
  number={},
  pages={1129-1133},
  doi={10.1109/ICIP42928.2021.9506733}}