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) :+1: .
Share to
<a href="http://www.facebook.com/sharer.php?u=https://github.com/layumi/Person_reID_baseline_pytorch/" target="_blank">
<img src="https://github.com/layumi/Person_reID_baseline_pytorch/raw/master/docs/1_Facebook_colored_svg_copy-64.webp" alt="Facebook" width="60"/>
</a>
<!-- Twitter -->
<a href="https://twitter.com/share?url=https://github.com/layumi/Person_reID_baseline_pytorch/&text=Strong,%20Small,%20Friendly%20Pytorch%20re-ID&hashtags=pytorch re-ID" target="_blank">
<img src="https://github.com/layumi/Person_reID_baseline_pytorch/raw/master/docs/2018_social_media_popular_app_logo_twitter-64.webp" alt="Twitter" width="60"/>
</a>
<!-- Weibo -->
<a href="https://service.weibo.com/share/share.php?url=https://github.com/layumi/Person_reID_baseline_pytorch/&text=Strong,%20Small,%20Friendly%20Pytorch%20re-ID&hashtags=pytorch re-ID" target="_blank">
<img src="https://github.com/layumi/Person_reID_baseline_pytorch/raw/master/docs/Weibo-64.webp" alt="Weibo" width="60" />
</a>
<!-- LinkedIn -->
<a href="http://www.linkedin.com/shareArticle?mini=true&url=https://github.com/layumi/Person_reID_baseline_pytorch/" target="_blank">
<img src="https://github.com/layumi/Person_reID_baseline_pytorch/raw/master/docs/circle-linkedin-64.webp" alt="LinkedIn" width="60" />
</a>
<!-- Email -->
<a href="https://github.com/layumi/Person_reID_baseline_pytorch/blob/master/mailto:?Subject=Strong, Small, Friendly Pytorch Re-identification&Body=I%20saw%20this%20and%20thought%20of%20you!%20 https://github.com/layumi/Person_reID_baseline_pytorch/">
<img src="https://github.com/layumi/Person_reID_baseline_pytorch/raw/master/docs/Email-64.webp" alt="Email" width="60"/>
</a>
Now we have supported:
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 slightly different way.
12 Jan 2024 We are holding a workshop at ACM ICMR 2024 on Multimedia Object Re-ID. You are welcome to show your insights. See you at Phuket, Thailand!😃 The workshop link is https://www.zdzheng.xyz/MORE2024/ . Submission DDL is 15 April 2024. Good papers will be recommended to ACM TOMM Special Issue (CCF-B). (Re-submission is needed.)
12 Aug 2023 Large Person Langauge Model is currently available at Here accepted by ACM MM'23. You are welcomed to check it.
19 Mar 2023 We host a special session on IEEE Intelligent Transportation Systems Conference (ITSC), covering the object re-identification & point cloud topic. The paper ddl is by May 15, 2023 and the paper notification is at June 30, 2023. Please select the session code ``w7r4a'' during submission. More details can be found at Special Session Website.
9 Mar 2023 Market-1501 is in 3D. Please check our single 2D to 3D reconstruction work https://github.com/layumi/3D-Magic-Mirror .
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] (224x224) | 92.75% | 79.70% | python train.py --use_swin --name swin; python test.py --name swin |
[SwinV2 (all tricks+Circle 256x128)] | 92.93% | 82.99% | python train.py --use_swinv2 --name swinv2_p0.5_circle_w5_b16_lr0.03 --lr 0.03 --batch 16 --erasing_p 0.5 --circle --warm_epoch 5; python test.py --name swinv2_p0.5_circle_w5_b16_lr0.03 --batch 32 |
[Swin (all tricks+Circle 224x224)] | 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 224x224)] | 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 224x224)] | 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 |
--DG
is not tuned. Better hyper-parameter may lead to better results.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 |
You may learn more from model.py
.
We add one linear layer(bottleneck), one batchnorm layer and relu.
pip install timm
for Swin-Transformer with Pytorch >1.7.0pip install 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 .
pip install -r requirements.txt
git clone https://github.com/pytorch/vision
cd vision
python setup.py install
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 .
Download Market1501 Dataset [Google] [Baidu] Or use command line:
pip install gdown
pip install --upgrade gdown #!!important!!
gdown 0B8-rUzbwVRk0c054eEozWG9COHM
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)) Or use command line:
gdown 1jjE85dRCMOgRtvJ5RQV9-Afs-2_5dY3O
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 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, e.g., /home/yourname/Market/pytorch
--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
If you want to use multiple GPUs, you are suggested to use DDP (train_DDP.py
) instead of DP (train.py
). It is because DP lacks the torch supports and may face some NaN.
You could call train_DDP.py
by running DDP.sh
.
bash DDP.sh
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.
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).
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.
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.
https://github.com/layumi/Person_reID_baseline_pytorch/issues/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.
@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}
}