Pytorch ReID
Strong, Small, Friendly
A tiny, friendly, strong baseline code for Person-reID (based on pytorch).
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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 [email protected]=88.24%, mAP=70.68% only with softmax loss.
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Small. With fp16 (supported by Nvidia apex), our baseline could be trained with only 2GB GPU memory.
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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 person re-ID, you may check out our Tutorial first (8 min read)
👍 .
Tutorial
Table of contents
- Features
- Some News
- Trained Model
- Prerequisites
- Getting Started
- Tips for training with other datasets
- Citation
- Related Repos
Features
Now we have supported:
- Circle Loss (CVPR 2020 Oral)
- Float16 to save GPU memory based on apex
- Part-based Convolutional Baseline(PCB)
- Multiple Query Evaluation
- Re-Ranking (GPU Version)
- 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 [email protected]=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
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.
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
01 March 2020 We release one new image retrieval dataset, called University-1652, for drone-view target localization and drone navigation
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 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 [email protected]=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 [email protected]=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 [email protected]=89.91% mAP=73.58%.
What's new: Re-ranking is added to evaluation. The re-ranked result is about [email protected]=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 | [email protected] | mAP | Reference |
|---|---|---|---|
| [ResNet-50] | 88.84% | 71.59% | python train.py --train_all |
| [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 |
| [PCB] | 92.64% | 77.47% | python train.py --name PCB --PCB --train_all --lr 0.02 |
| [ResNet-50 (fp16)] | 88.03% | 71.40% | python train.py --name fp16 --fp16 --train_all |
| [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 |
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+
- [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 [email protected]=64.23%, mAP=43.92%. Hyperparameters are need to be tuned.
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 [email protected], [email protected], [email protected] 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 [email protected], [email protected], [email protected] 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. https://github.com/layumi/Person_reID_baseline_pytorch/issues/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}
}
chould you share me how to solve this problem?Thank you!
All the best.
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