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) 👍 .

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Tutorial

• 8 min Tutorial，8分钟教程
• 中文视频简介
• Answer to Tutorial

Table of contents

• Features
• Some News
• Trained Model
• Prerequisites
• Getting Started
  • Installation
  • Dataset Preparation
  • Train
  • Test
  • Evaluation
• Tips for training with other datasets
• How to Cite?
• Related Repos

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

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 .

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.

python train.py --fp16

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

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

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

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.

• 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.

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

• [Optional] 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] 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.

• [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. #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}
}

Related Repos

1. Pedestrian Alignment Network
2. 2stream Person re-ID
3. Pedestrian GAN
4. Language Person Search
5. DG-Net
6. 3D Person re-ID