Pytorch ReID

Strong, Small, Friendly

A tiny, friendly, strong baseline code for Object-reID (based on pytorch) since 2017.

Strong. It is consistent with the new baseline result in several top-conference works, e.g., Joint Discriminative and Generative Learning for Person Re-identification(CVPR19), Beyond Part Models: Person Retrieval with Refined Part Pooling(ECCV18), Camera Style Adaptation for Person Re-identification(CVPR18). We arrived Rank@1=88.24%, mAP=70.68% only with softmax loss.
Small. With fp16 (supported by Nvidia apex), our baseline could be trained with only 2GB GPU memory.
Friendly. You may use the off-the-shelf options to apply many state-of-the-art tricks in one line. Besides, if you are new to object re-ID, you may check out our Tutorial first (8 min read) 👍 .

Features

Now we have supported:

Training

Running the code on Google Colab with Free GPU. Check Here (Thanks to @ronghao233)
DG-Market (10x Large Synethic Dataset from Market CVPR 2019 Oral)
Swin Transformer / EfficientNet / HRNet
ResNet/ResNet-ibn/DenseNet
Circle Loss, Triplet Loss, Contrastive Loss, Sphere Loss, Lifted Loss, Arcface, Cosface and Instance Loss
Float16 to save GPU memory based on apex
Part-based Convolutional Baseline(PCB)
Random Erasing
Linear Warm-up

Testing

TensorRT
Pytorch JIT
Fuse Conv and BN layer into one Conv layer
Multiple Query Evaluation
Re-Ranking (CPU & GPU Version)
Visualize Training Curves
Visualize Ranking Result
Visualize Heatmap

Here we provide hyperparameters and architectures, that were used to generate the result. Some of them (i.e. learning rate) are far from optimal. Do not hesitate to change them and see the effect.

P.S. With similar structure, we arrived Rank@1=87.74% mAP=69.46% with Matconvnet. (batchsize=8, dropout=0.75) You may refer to Here. Different framework need to be tuned in a different way.

Some News

2022 News

24 Jul 2022 Market-HQ is released with super-resolution quality from 128*64 to 512*256. Please check at https://github.com/layumi/HQ-Market

14 Jul 2022 Add adversarial training by python train.py --name ftnet_adv --adv 0.1 --aiter 40.

1 Feb 2022 Speed up the inference process about 10 seconds by removing the cat function in test.py.

1 Feb 2022 Add the demo with TensorRT (The fast inference speed may depend on the GPU with the latest RT Core).

2021 News

30 Dec 2021 We add supports for new losses, including arcface loss, cosface loss and instance loss. The hyper-parameters are still tunning.

3 Dec 2021 We add supports for four losses, including triplet loss, contrastive loss, sphere loss and lifted loss. The hyper-parameters are still tunning.

1 Dec 2021 We support EfficientNet/HRNet.

15 Sep 2021 We support ResNet-ibn from ECCV2018 (https://github.com/XingangPan/IBN-Net).

17 Aug 2021 We support running code on Google Colab with free GPU. Please check it out at https://github.com/layumi/Person_reID_baseline_pytorch/tree/master/colab .

14 Aug 2021 We have supported the training with DG-Market for regularization via Self-supervised Memory Learning. You only neeed to download/unzip the dataset and add --DG to train model.

12 Aug 2021 We have supported the transformer-based model Swin by --use_swin. The basic performance is 92.73% Rank@1 and 79.71%mAP.

23 Jun 2021 Attack your re-ID model via Query! They are not robust as you expected! Check the code at Here.

5 Feb 2021 We have supported Circle loss(CVPR20 Oral). You can try it by simply adding --circle.

11 January 2021 On the Market-1501 dataset, we accelerate the re-ranking processing from 89.2s to 9.4ms with one K40m GPU, facilitating the real-time post-processing. The pytorch implementation can be found in GPU-Re-Ranking.

2020 News

11 June 2020 People live in the 3D world. We release one new person re-id code Person Re-identification in the 3D Space, which conduct representation learning in the 3D space. You are welcomed to check out it.

30 April 2020 We have applied this code to the AICity Challenge 2020, yielding the 1st Place Submission to the re-id track 🚗. Check out here.

01 March 2020 We release one new image retrieval dataset, called University-1652, for drone-view target localization and drone navigation 🚁. It has a similar setting with the person re-ID. You are welcomed to check out it.

2019 News

07 July 2019: I added some new functions, such as --resume, auto-augmentation policy, acos loss, into developing thread and rewrite the save and load functions. I haven't tested the functions throughly. Some new functions are worthy of having a try. If you are first to this repo, I suggest you stay with the master thread.

01 July 2019: My CVPR19 Paper is online. It is based on this baseline repo as teacher model to provide pseudo label for the generated images to train a better student model. You are welcomed to check out the opensource code at here.

03 Jun 2019: Testing with multiple-scale inputs is added. You can use --ms 1,0.9 when extracting the feature. It could slightly improve the final result.

20 May 2019: Linear Warm Up is added. You also can set warm-up the first K epoch by --warm_epoch K. If K <=0, there will be no warm-up.

2018 & 2017 News

What's new: FP16 has been added. It can be used by simply added --fp16. You need to install apex and update your pytorch to 1.0.

Float16 could save about 50% GPU memory usage without accuracy drop. Our baseline could be trained with only 2GB GPU memory.

python train.py --fp16

What's new: Visualizing ranking result is added.

python prepare.py
python train.py
python test.py
python demo.py --query_index 777

What's new: Multiple-query Evaluation is added. The multiple-query result is about Rank@1=91.95% mAP=78.06%.

python prepare.py
python train.py
python test.py --multi
python evaluate_gpu.py

What's new: PCB is added. You may use '--PCB' to use this model. It can achieve around Rank@1=92.73% mAP=78.16%. I used a GPU (P40) with 24GB Memory. You may try apply smaller batchsize and choose the smaller learning rate (for stability) to run. (For example, --batchsize 32 --lr 0.01 --PCB)

python train.py --PCB --batchsize 64 --name PCB-64
python test.py --PCB --name PCB-64

What's new: You may try evaluate_gpu.py to conduct a faster evaluation with GPU.

What's new: You may apply '--use_dense' to use DenseNet-121. It can arrive around Rank@1=89.91% mAP=73.58%.

What's new: Re-ranking is added to evaluation. The re-ranked result is about Rank@1=90.20% mAP=84.76%.

What's new: Random Erasing is added to train.

What's new: I add some code to generate training curves. The figure will be saved into the model folder when training.

Trained Model

I re-trained several models, and the results may be different with the original one. Just for a quick reference, you may directly use these models. The download link is Here.

Methods	Rank@1	mAP	Reference
[EfficientNet-b4]	85.78%	66.80%	`python train.py --use_efficient --name eff; python test.py --name eff`
[ResNet-50 + adv defense]	87.77%	69.83%	`python train.py --name adv0.1_40_w10_all --adv 0.1 --aiter 40 --warm 10 --train_all; python test.py --name adv0.1_40_w10_all`
[ConvNeXt]	88.98%	71.35%	`python train.py --use_convnext --name convnext; python test.py --name convnext`
[ResNet-50 (fp16)]	88.03%	71.40%	`python train.py --name fp16 --fp16 --train_all`
[ResNet-50]	88.84%	71.59%	`python train.py --train_all`
[ResNet-50-ibn]	89.13%	73.40%	`python train.py --train_all --name res-ibn --ibn`
[DenseNet-121]	90.17%	74.02%	`python train.py --name ft_net_dense --use_dense --train_all`
[DenseNet-121 (Circle)]	91.00%	76.54%	`python train.py --name ft_net_dense_circle_w5 --circle --use_dense --train_all --warm_epoch 5`
[HRNet-18]	90.83%	76.65%	`python train.py --use_hr --name hr18; python test.py --name hr18`
[PCB]	92.64%	77.47%	`python train.py --name PCB --PCB --train_all --lr 0.02`
[PCB + DG]	92.70%	78.31%	`python train.py --name PCB_DG --PCB --train_all --lr 0.02 --DG; python test.py --name PCB_DG`
[ResNet-50 (all tricks)]	91.83%	78.32%	`python train.py --warm_epoch 5 --stride 1 --erasing_p 0.5 --batchsize 8 --lr 0.02 --name warm5_s1_b8_lr2_p0.5`
[ResNet-50 (all tricks+Circle)]	92.13%	79.84%	`python train.py --warm_epoch 5 --stride 1 --erasing_p 0.5 --batchsize 8 --lr 0.02 --name warm5_s1_b8_lr2_p0.5_circle --circle`
[ResNet-50 (all tricks+Circle+DG)]	92.13%	80.13%	`python train.py --warm_epoch 5 --stride 1 --erasing_p 0.5 --batchsize 8 --lr 0.02 --name warm5_s1_b8_lr2_p0.5_circle_DG --circle --DG; python test.py --name warm5_s1_b8_lr2_p0.5_circle_DG`
[DenseNet-121 (all tricks+Circle)]	92.61%	80.24%	`python train.py --warm_epoch 5 --stride 1 --erasing_p 0.5 --batchsize 8 --lr 0.02 --name dense_warm5_s1_b8_lr2_p0.5_circle --circle --use_dense; python test.py --name dense_warm5_s1_b8_lr2_p0.5_circle`
[HRNet-18 (all tricks+Circle+DG)]	92.19%	81.00%	`python train.py --use_hr --name hr18_p0.5_circle_w5_b16_lr0.01_DG --lr 0.01 --batch 16 --DG --erasing_p 0.5 --circle --warm_epoch 5; python test.py --name hr18_p0.5_circle_w5_b16_lr0.01_DG`
[Swin]	92.75%	79.70%	`python train.py --use_swin --name swin; python test.py --name swin`
[Swin (all tricks+Circle)]	94.12%	84.39%	`python train.py --use_swin --name swin_p0.5_circle_w5 --erasing_p 0.5 --circle --warm_epoch 5; python test.py --name swin_p0.5_circle_w5`
[Swin (all tricks+Circle+b16)]	94.00%	85.21%	`python train.py --use_swin --name swin_p0.5_circle_w5_b16_lr0.01 --lr 0.01 --batch 16 --erasing_p 0.5 --circle --warm_epoch 5; python test.py --name swin_p0.5_circle_w5_b16_lr0.01`
[Swin (all tricks+Circle+b16+DG)]	94.00%	85.36%	`python train.py --use_swin --name swin_p0.5_circle_w5_b16_lr0.01_DG --lr 0.01 --batch 16 --DG --erasing_p 0.5 --circle --warm_epoch 5; python test.py --name swin_p0.5_circle_w5_b16_lr0.01_DG`

More training iterations may lead to better results.
Swin costs more GPU memory (11G GPU is needed) to run.
The hyper-parameter of DG-Market --DG is not tuned. Better hyper-parameter may lead to better results.

Different Losses

I do not optimize the hyper-parameters. You are free to tune them for better performance.

Methods	Rank@1	mAP	Reference
CE	92.01%	79.31%	`python train.py --warm_epoch 5 --stride 1 --erasing_p 0.5 --batchsize 32 --lr 0.08 --name warm5_s1_b32_lr8_p0.5_100 --total 100 ; python test.py --name warm5_s1_b32_lr8_p0.5_100`
CE + Sphere [Paper]	92.01%	79.39%	`python train.py --warm_epoch 5 --stride 1 --erasing_p 0.5 --batchsize 32 --lr 0.08 --name warm5_s1_b32_lr8_p0.5_sphere100 --sphere --total 100; python test.py --name warm5_s1_b32_lr8_p0.5_sphere100`
CE + Triplet [Paper]	92.40%	79.71%	`python train.py --warm_epoch 5 --stride 1 --erasing_p 0.5 --batchsize 32 --lr 0.08 --name warm5_s1_b32_lr8_p0.5_triplet100 --triplet --total 100; python test.py --name warm5_s1_b32_lr8_p0.5_triplet100`
CE + Lifted [Paper]	91.78%	79.77%	`python train.py --warm_epoch 5 --stride 1 --erasing_p 0.5 --batchsize 32 --lr 0.08 --name warm5_s1_b32_lr8_p0.5_lifted100 --lifted --total 100; python test.py --name warm5_s1_b32_lr8_p0.5_lifted100`
CE + Instance [Paper]	92.73%	81.11%	`python train.py --warm_epoch 5 --stride 1 --erasing_p 0.5 --batchsize 32 --lr 0.08 --name warm5_s1_b32_lr8_p0.5_instance100_gamma64 --instance --ins_gamma 64 --total 100 ; python test.py --name warm5_s1_b32_lr8_p0.5_instance100_gamma64`
CE + Contrast [Paper]	92.28%	81.42%	`python train.py --warm_epoch 5 --stride 1 --erasing_p 0.5 --batchsize 32 --lr 0.08 --name warm5_s1_b32_lr8_p0.5_contrast100 --contrast --total 100; python test.py --name warm5_s1_b32_lr8_p0.5_contrast100`
CE + Circle [Paper]	92.46%	81.70%	`python train.py --warm_epoch 5 --stride 1 --erasing_p 0.5 --batchsize 32 --lr 0.08 --name warm5_s1_b32_lr8_p0.5_circle100 --circle --total 100 ; python test.py --name warm5_s1_b32_lr8_p0.5_circle100`
CE + Contrast + Sphere	92.79%	82.02%	`python train.py --warm_epoch 5 --stride 1 --erasing_p 0.5 --batchsize 32 --lr 0.08 --name warm5_s1_b32_lr8_p0.5_cs100 --contrast --sphere --total 100; python test.py --name warm5_s1_b32_lr8_p0.5_cs100`
CE + Contrast + Triplet (Long)	92.61%	82.01%	`python train.py --warm_epoch 5 --stride 1 --erasing_p 0.5 --batchsize 24 --lr 0.062 --name warm5_s1_b24_lr6.2_p0.5_contrast_triplet_133 --contrast --triplet --total 133 ; python test.py --name warm5_s1_b24_lr6.2_p0.5_contrast_triplet_133`
CE + Contrast + Circle (Long)	92.19%	82.07%	`python train.py --warm_epoch 5 --stride 1 --erasing_p 0.5 --batchsize 24 --lr 0.08 --name warm5_s1_b24_lr8_p0.5_contrast_circle133 --contrast --circle --total 133 ; python test.py --name warm5_s1_b24_lr8_p0.5_contrast_circle133`
CE + Contrast + Sphere (Long)	92.84%	82.37%	`python train.py --warm_epoch 5 --stride 1 --erasing_p 0.5 --batchsize 24 --lr 0.06 --name warm5_s1_b24_lr6_p0.5_contrast_sphere133 --contrast --sphere --total 133 ; python test.py --name warm5_s1_b24_lr6_p0.5_contrast_sphere133`

Model Structure

You may learn more from model.py. We add one linear layer(bottleneck), one batchnorm layer and relu.

Prerequisites

Python 3.6
GPU Memory >= 6G
Numpy
Pytorch 0.3+
timm pip install timm for Swin-Transformer with Pytorch >1.7.0
pretrainedmodels via pip install pretrainedmodels
[Optional] apex (for float16)
[Optional] pretrainedmodels

(Some reports found that updating numpy can arrive the right accuracy. If you only get 50~80 Top1 Accuracy, just try it.) We have successfully run the code based on numpy 1.12.1 and 1.13.1 .

Getting started

Installation

Install Pytorch from http://pytorch.org/
Install Torchvision from the source

git clone https://github.com/pytorch/vision
cd vision
python setup.py install

[Optinal] You may skip it. Install apex from the source

git clone https://github.com/NVIDIA/apex.git
cd apex
python setup.py install --cuda_ext --cpp_ext

Because pytorch and torchvision are ongoing projects.

Here we noted that our code is tested based on Pytorch 0.3.0/0.4.0/0.5.0/1.0.0 and Torchvision 0.2.0/0.2.1 .

Dataset & Preparation

Download Market1501 Dataset [Google] [Baidu]

Preparation: Put the images with the same id in one folder. You may use

python prepare.py

Remember to change the dataset path to your own path.

Futhermore, you also can test our code on [DukeMTMC-reID Dataset]( GoogleDriver or (BaiduYun password: bhbh)). Our baseline code is not such high on DukeMTMC-reID Rank@1=64.23%, mAP=43.92%. Hyperparameters are need to be tuned.

[Optional] DG-Market is a generated pedestrian dataset of 128,307 images for training a robust model.

Train

Train a model by

python train.py --gpu_ids 0 --name ft_ResNet50 --train_all --batchsize 32  --data_dir your_data_path

--gpu_ids which gpu to run.

--name the name of model.

--data_dir the path of the training data.

--train_all using all images to train.

--batchsize batch size.

--erasing_p random erasing probability.

Train a model with random erasing by

python train.py --gpu_ids 0 --name ft_ResNet50 --train_all --batchsize 32  --data_dir your_data_path --erasing_p 0.5

Test

Use trained model to extract feature by

python test.py --gpu_ids 0 --name ft_ResNet50 --test_dir your_data_path  --batchsize 32 --which_epoch 59

--gpu_ids which gpu to run.

--batchsize batch size.

--name the dir name of trained model.

--which_epoch select the i-th model.

--data_dir the path of the testing data.

Evaluation

python evaluate.py

It will output Rank@1, Rank@5, Rank@10 and mAP results. You may also try evaluate_gpu.py to conduct a faster evaluation with GPU.

For mAP calculation, you also can refer to the C++ code for Oxford Building. We use the triangle mAP calculation (consistent with the Market1501 original code).

re-ranking

python evaluate_rerank.py

It may take more than 10G Memory to run. So run it on a powerful machine if possible.

It will output Rank@1, Rank@5, Rank@10 and mAP results.

Tips

Notes the format of the camera id and the number of cameras.

For some dataset, e.g., MSMT17, there are more than 10 cameras. You need to modify the prepare.py and test.py to read the double-digit camera ID.

For some vehicle re-ID datasets. e.g. VeRi, you also need to modify the prepare.py and test.py. It has different naming rules. layumi#107 (Sorry. It is in Chinese)

Citation

The following paper uses and reports the result of the baseline model. You may cite it in your paper.

@article{zheng2019joint,
  title={Joint discriminative and generative learning for person re-identification},
  author={Zheng, Zhedong and Yang, Xiaodong and Yu, Zhiding and Zheng, Liang and Yang, Yi and Kautz, Jan},
  journal={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2019}
}

The following papers may be the first two to use the bottleneck baseline. You may cite them in your paper.

@article{DBLP:journals/corr/SunZDW17,
  author    = {Yifan Sun and
               Liang Zheng and
               Weijian Deng and
               Shengjin Wang},
  title     = {SVDNet for Pedestrian Retrieval},
  booktitle   = {ICCV},
  year      = {2017},
}

@article{hermans2017defense,
  title={In Defense of the Triplet Loss for Person Re-Identification},
  author={Hermans, Alexander and Beyer, Lucas and Leibe, Bastian},
  journal={arXiv preprint arXiv:1703.07737},
  year={2017}
}

Basic Model

@article{zheng2018discriminatively,
  title={A discriminatively learned CNN embedding for person reidentification},
  author={Zheng, Zhedong and Zheng, Liang and Yang, Yi},
  journal={ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)},
  volume={14},
  number={1},
  pages={13},
  year={2018},
  publisher={ACM}
}

@article{zheng2020vehiclenet,
  title={VehicleNet: Learning Robust Visual Representation for Vehicle Re-identification},
  author={Zheng, Zhedong and Ruan, Tao and Wei, Yunchao and Yang, Yi and Mei, Tao},
  journal={IEEE Transaction on Multimedia (TMM)},
  year={2020}
}