pinglmlcv / Person_reID_baseline_pytorch

A tiny, friendly, strong pytorch implement of person re-identification baseline. Tutorial 👉

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

Table of contents


Now we have supported:

  • Float16 to save GPU memory based on apex
  • Part-based Convolutional Baseline(PCB)
  • Multiple Query Evaluation
  • Re-Ranking
  • Random Erasing
  • ResNet/DenseNet
  • Visualize Training Curves
  • Visualize Ranking Result
  • Visualize Heatmap
  • Linear Warm-up

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

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.

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 --fp16

What's new: Visualizing ranking result is added.

python --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 --multi

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 --PCB --batchsize 64 --name PCB-64
python --PCB --name PCB-64

What's new: You may try 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
[ResNet-50] 88.84% 71.59% python --train_all
[DenseNet-121] 90.17% 74.02% python --name ft_net_dense --use_dense --train_all
[PCB] 92.64% 77.47% python --name PCB --PCB --train_all --lr 0.02
[ResNet-50 (fp16)] 88.03% 71.40% python --name fp16 --fp16 --train_all
[ResNet-50 (all tricks)] 91.83% 78.32% python --warm_epoch 5 --stride 1 --erasing_p 0.5 --batchsize 8 --lr 0.02 --name warm5_s1_b8_lr2_p0.5

Model Structure

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


  • Python 3.6
  • GPU Memory >= 6G
  • Numpy
  • Pytorch 0.3+
  • [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


git clone
cd vision
python install
  • [Optinal] You may skip it. Install apex from the source
git clone
cd apex
python 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


Remember to change the dataset path to your own path.

Futhermore, you also can test our code on DukeMTMC-reID Dataset. Our baseline code is not such high on DukeMTMC-reID Rank@1=64.23%, mAP=43.92%. Hyperparameters are need to be tuned.


Train a model by

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


Use trained model to extract feature by

python --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.



It will output Rank@1, Rank@5, Rank@10 and mAP results. You may also try 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).



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.


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 and to read the double-digit camera ID.

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


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

  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)},

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

  author    = {Yifan Sun and
               Liang Zheng and
               Weijian Deng and
               Shengjin Wang},
  title     = {SVDNet for Pedestrian Retrieval},
  booktitle   = {ICCV},
  year      = {2017},

  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},

Basic Model

  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)},

Related Repos

  1. Pedestrian Alignment Network GitHub stars
  2. 2stream Person re-ID GitHub stars
  3. Pedestrian GAN GitHub stars
  4. Language Person Search GitHub stars
  5. DG-Net GitHub stars


A tiny, friendly, strong pytorch implement of person re-identification baseline. Tutorial 👉

License:MIT License


Language:Python 100.0%