Person Re-id in the 3D Space

[Pdf] [Code] [中文解读]

Thanks for your attention. In this repo, we provide the code for the paper [Parameter-Efficient Person Re-identification in the 3D Space ], published at IEEE Transactions on Neural Networks and Learning Systems (TNNLS) 2022.

News

• 9 Mar 2023 Market-1501 is in 3D. Please check our single 2D to 3D reconstruction work https://github.com/layumi/3D-Magic-Mirror.

• 29 Sep 2022. I updated Circle loss, parameter count and the latest snapshots trained on 4 datasets, including Market, Duke, CUHK and MSMT, in /snapshots. You can directly test it after dataset preparing.

• 31 Jul 2021. Circle loss is added. For the fair comparison with circle loss, I re-train almost all the models with a bigger batch size. The results are updated in the latest arXiv version.

• 30 Oct 2020. I simply modify code on three points to further improve the performance:

1. More training epochs help; (Since we are trained from scratch)

2. I replace the dgl to more efficient KNN implementation to accelebrate training; (DGL does not optimize KNN very well, and Matrix Multiplication works quicker. )

3. For MSMT-17 and Duke, some classes contain too many images, while other categories are under-explored. I apply the stratified sampling (--balance), which takes training samples of each class with equal probability.

• You may directly download my generated 3D data of the Market-1501 dataset at [OneDrive] or [GoogleDrive], and therefore you could skip the data preparation part. Just put the datasets in the same folder of the code.

├── 2DMarket\
│   ├── query/  
│   ├── train_all/
│   ├── ...
├── 3DMarket+bg\
│   ├── query/  
│   ├── train_all/
│   ├── ...
├── train.py
├── test.py 
├── ...

Prerequisites

• Python 3.6 or 3.7
• GPU Memory >= 4G (e.g., GTX1080)
• Pytorch = 1.4.0 (Not Latest. Latest version is incompatible, since it changes the C++ interfaces.)
• dgl

Install

Here I use the cuda10.1 by default.

conda create --name OG python=3.7
conda activate OG
conda install pytorch=1.4.0 torchvision=0.5.0 cudatoolkit=10.1 -c pytorch
conda install matplotlib requests
conda install -c dglteam dgl-cuda10.1=0.4.3
pip install -r requirements.txt

If you face any error, you may first try to re-install open3d. It helps. And make sure the gcc version is larger than 5.4.0. If you do not have the sudo permission, you may install gcc by conda as follows:

conda install -c brown-data-science gcc          (which is gcc-5.4.0)
gcc -v                                          (to see whether installation is successful)
ln libstdc++.so.6.0.26 libstdc++.so.6            (update lib in /anaconda3/env/OG/lib)
conda install gxx_linux-64
conda install gcc_linux-64

Prepare Data

• Download our prepared data. You may directly download my generated 3D data of the Market-1501 dataset at [OneDrive] or [GoogleDrive], and therefore you could totally skip the next data preparation part.

• 3D Part We first generate the 3D data via the code at https://github.com/layumi/hmr (I modified the code from https://github.com/akanazawa/hmr and added 2D-to-3D color mapping.)

I remove all 3D faces and only keep 3D points positions&RGB to save the storage & loading burden. You can use any text readers (such as vim) to see my generated obj files.

• 2D Part Download Market-1501, DukeMTMC-reID or MSMT17 and unzip them in the ../

Split the dataset and arrange them in the folder of ID by the following code.

python prepare_market.py # You may need to change the download path. 
python prepare_duke.py
python prepare_MSMT.py

Link the 2DDataset to this dir.

ln -s ../Your_Market/pytorch  ./2DMarket
ln -s ../Your_Duke/pytorch  ./2DDuke
ln -s ../Your_MSMT/pytorch  ./2DMSMT

Training

• 1. Market-1501

OG-Net 86.82 (69.02)

python train_M.py --batch-size 36 --name Efficient_ALL_Dense_b36_lr10_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1500_wa0.9_GeM_bn2_class3_amsgrad --slim 0.5 --flip --scale  --lrRate 10e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 1500  --feature_dims 64,128,256,512   --efficient  --wa --wa_start 0.9 --gem --norm_layer bn2   --amsgrad --class 3

OG-Net + Circle 87.80 (70.56)

python train_M.py --batch-size 36 --name Efficient_ALL_Dense_b36_lr10_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1500_wa0.9_GeM_bn2_balance_circle_amsgrad_gamma64 --slim 0.5 --flip --scale  --lrRate 10e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 1500  --feature_dims 64,128,256,512   --efficient  --wa --wa_start 0.9 --gem --norm_layer bn2 --balance  --circle --amsgrad --gamma 64

OG-Net-Small 86.79 (67.92)

python train_M.py --batch-size 36 --name Efficient_ALL_SDense_b36_lr10_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1000_wa0.9_GeM_bn2_balance_amsgrad --slim 0.5 --flip --scale  --lrRate 10e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 1000  --feature_dims 48,96,192,384   --efficient  --wa --wa_start 0.9 --gem --norm_layer bn2 --balance  --amsgrad 

OG-Net-Small + Circle 87.38 (70.48)

python train_M.py --batch-size 36 --name Efficient_ALL_SDense_b36_lr10_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1500_wa0.9_GeM_bn2_balance_circle_amsgrad_gamma64 --slim 0.5 --flip --scale  --lrRate 10e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 1500  --feature_dims 48,96,192,384   --efficient  --wa --wa_start 0.9 --gem --norm_layer bn2 --balance  --circle --amsgrad --gamma 64

OG-Net-Deep + Circle 88.81 (72.91)

python train_M.py --batch-size 30 --name Market_Efficient_ALL_2SDDense_b30_lr6_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1000_id2_bn_k9_conv2_balance  --id_skip 2 --slim 0.5 --flip --scale  --lrRate 6e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 1000  --feature_dims 48,96,96,192,192,384,384  --efficient --k 9  --num_conv 2  --dataset 2DMarket --balance --gem --norm_layer bn2 --circle --amsgrad --gamma 64

• 2. DukeMTMC-reID

OG-Net-Small 77.33 (57.74)

python train_M.py --batch-size 36 --name reEfficient_Duke_ALL_SDense_b36_lr10_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1000_class_GeM_bn2_amsgrad --slim 0.5 --flip --scale  --lrRate 10e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 1000  --feature_dims 48,96,192,384   --efficient --dataset 2DDuke --class --wa --wa_start 0.9 --gem --norm_layer bn2  --amsgrad 

OG-Net-Small + Circle 77.15 (58.51)

python train_M.py --batch-size 36 --name reEfficient_Duke_ALL_SDense_b36_lr10_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1000_balance_GeM_bn2_circle_amsgrad --slim 0.5 --flip --scale  --lrRate 10e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 1000  --feature_dims 48,96,192,384   --efficient --dataset 2DDuke --balance --wa --wa_start 0.9 --gem --norm_layer bn2 --circle --amsgrad

OG-Net 76.53 (57.92)

python train_M.py --batch-size 36 --name reEfficient_Duke_ALL_Dense_b36_lr10_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1000_class1_GeM_bn2_amsgrad --slim 0.5 --flip --scale  --lrRate 10e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 1000  --feature_dims 64,128,256,512   --efficient --dataset 2DDuke --class 1 --wa --wa_start 0.9 --gem --norm_layer bn2 --amsgrad  

OG-Net + Circle 78.37 (60.07)

python train_M.py --batch-size 36 --name reEfficient_Duke_ALL_Dense_b36_lr10_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1000_balance_GeM_bn2_circle_amsgrad_gamma64 --slim 0.5 --flip --scale  --lrRate 10e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 1000  --feature_dims 64,128,256,512   --efficient --dataset 2DDuke --balance --wa --wa_start 0.9 --gem --norm_layer bn2 --circle --amsgrad --gamma 64

OG-Net-Deep 76.97 (59.23)

python train_M.py --batch-size 36 --name Duke_Efficient_ALL_2SDDense_b36_lr6_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1000_id2_bn_k9_conv2_balance_noCircle  --id_skip 2 --slim 0.5 --flip --scale  --lrRate 6e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 1000  --feature_dims 48,96,96,192,192,384,384  --efficient --k 9  --num_conv 2  --dataset 2DDuke --balance --gem --norm_layer bn2 --amsgrad 

OG-Net-Deep + Circle 78.50 (60.7)

python train_M.py --batch-size 36 --name Duke_Efficient_ALL_2SDDense_b36_lr6_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1000_id2_bn_k9_conv2_balance  --id_skip 2 --slim 0.5 --flip --scale  --lrRate 6e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 1000  --feature_dims 48,96,96,192,192,384,384  --efficient --k 9  --num_conv 2  --dataset 2DDuke --balance --gem --norm_layer bn2 --circle --amsgrad --gamma 64

• 3. CUHK-NP

OG-Net 44.00 (39.28)

python train_M.py --batch-size 36 --name Efficient_CUHK_ALL_Dense_b36_lr8_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1000_class1_gem_bn2_amsgrad_wd1e-3 --slim 0.5 --flip --scale  --lrRate 8e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1 --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 1000  --feature_dims 64,128,256,512    --efficient --dataset 2DCUHK --class 1  --gem --norm_layer bn2  --amsgrad  --wd 1e-3 

OG-Net + Circle 48.29 (43.73)

python train_M.py --batch-size 36 --name Efficient_CUHK_ALL_Dense_b36_lr10_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1000_class3_gem_bn2_circle_amsgrad_wd1e-3_gamma96 --slim 0.5 --flip --scale  --lrRate 10e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 1000  --feature_dims 64,128,256,512    --efficient --dataset 2DCUHK --class 3 --gem --norm_layer bn2 --circle --amsgrad --wd 1e-3 --gamma 96

OG-Net-Small 43.07 (38.06)

python train_M.py --batch-size 36 --name Efficient_CUHK_ALL_SDense_b36_lr10_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1000_gem_bn2_amsgrad_wd1e-3_class1 --slim 0.5 --flip --scale  --lrRate 10e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam   --init 768  --cluster xyzrgb  --train_all   --num-epoch 1000  --feature_dims 48,96,192,384    --efficient --dataset 2DCUHK --gem --norm_layer bn2  --amsgrad --wd 1e-3  --class 1

OG-Net-Small + Circle 46.43 (41.79)

python train_M.py --batch-size 36 --name Efficient_CUHK_ALL_SDense_b36_lr8_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1000_balance_gem_bn2_circle_amsgrad_wd1e-3_gamma64 --slim 0.5 --flip --scale  --lrRate 8e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam   --init 768  --cluster xyzrgb  --train_all   --num-epoch 1000  --feature_dims 48,96,192,384    --efficient --dataset 2DCUHK --balance --gem --norm_layer bn2 --circle --amsgrad --wd 1e-3 --gamma 64

OG-Net-Deep 45.71 (41.15)

python train_M.py --batch-size 36 --name CUHK_Efficient_ALL_2SDDense_b36_lr6_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1500_id2_bn_k9_conv2_class3_Nocircle  --id_skip 2 --slim 0.5 --flip --scale  --lrRate 6e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 1500  --feature_dims 48,96,96,192,192,384,384  --efficient --k 9  --num_conv 2  --dataset 2DCUHK --class 3 --gem --norm_layer bn2 --amsgrad 

OG-Net-Deep + Circle 49.43 (45.71)

python train_M.py --batch-size 36 --name CUHK_Efficient_ALL_2SDDense_b36_lr6_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1500_id2_bn_k9_conv2_balance  --id_skip 2 --slim 0.5 --flip --scale  --lrRate 6e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 1500  --feature_dims 48,96,96,192,192,384,384  --efficient --k 9  --num_conv 2  --dataset 2DCUHK --balance --gem --norm_layer bn2 --circle --amsgrad --gamma 64

• 4. MSMT-17

OG-Net 44.27 (21.57)

python train_M.py --batch-size 36 --name reEfficient_MSMT_ALL_Dense_b36_lr6_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e600_balance_GeM_bn2_circle_amsgrad_gamma64_Nocircle --slim 0.5 --flip --scale  --lrRate 6e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 600  --feature_dims 64,128,256,512   --efficient --dataset 2DMSMT --balance --wa --wa_start 0.9 --gem --norm_layer bn2  --amsgrad 

OG-Net + Circle 45.28 (22.81)

python train_M.py --batch-size 36 --name reEfficient_MSMT_ALL_Dense_b36_lr6_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e600_balance_GeM_bn2_circle_amsgrad_gamma64 --slim 0.5 --flip --scale  --lrRate 6e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 600  --feature_dims 64,128,256,512   --efficient --dataset 2DMSMT --balance --wa --wa_start 0.9 --gem --norm_layer bn2 --circle --amsgrad --gamma 64

OG-Net-Small 43.84 (21.79)

python train_M.py --batch-size 36 --name reEfficient_MSMT_ALL_SDense_b36_lr6_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e600_balance_GeM_bn2_circle_amsgrad_gamma64 --slim 0.5 --flip --scale  --lrRate 6e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 600  --feature_dims 48,96,192,384   --efficient --dataset 2DMSMT --balance --wa --wa_start 0.9 --gem --norm_layer bn2 --circle --amsgrad --gamma 64

OG-Net-Small + Circle 42.44 (20.31)

python train_M.py --batch-size 36 --name reEfficient_MSMT_ALL_SDense_b36_lr6_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e600_class_GeM_bn2_amsgrad --slim 0.5 --flip --scale  --lrRate 6e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 600  --feature_dims 48,96,192,384   --efficient --dataset 2DMSMT --class 1  --wa --wa_start 0.9 --gem --norm_layer bn2  --amsgrad 

OG-Net-Deep 44.56 (21.41)

python train_M.py --batch-size 30 --name MSMT_Efficient_ALL_2SDDense_b30_lr4_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e600_id2_bn_k9_conv2_balance_nocircle  --id_skip 2 --slim 0.5 --flip --scale  --lrRate 4e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 600  --feature_dims 48,96,96,192,192,384,384  --efficient --k 9  --num_conv 2  --dataset 2DMSMT --balance --gem --norm_layer bn2 --amsgrad 

OG-Net-Deep + Circle 47.32 (24.07)

python train_M.py --batch-size 30 --name MSMT_Efficient_ALL_2SDDense_b30_lr4_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e600_id2_bn_k9_conv2_balance  --id_skip 2 --slim 0.5 --flip --scale  --lrRate 4e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 600  --feature_dims 48,96,96,192,192,384,384  --efficient --k 9  --num_conv 2  --dataset 2DMSMT --balance --gem --norm_layer bn2 --circle --amsgrad --gamma 64

Evaluation

• Market-1501

python test_M.py  --name  Market_Efficient_ALL_2SDDense_b30_lr6_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1000_id2_bn_k9_conv2_balance

• DukeMTMC-reID

python test_M.py  --data 2DDuke --name Duke_Efficient_ALL_2SDDense_b36_lr6_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1000_id2_bn_k9_conv2_balance

• CUHK

python test_M.py  --data 2DCUHK --name CUHK_Efficient_ALL_2SDDense_b36_lr6_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1500_id2_bn_k9_conv2_balance

• MSMT-17

python test_MSMT.py  --name MSMT_Efficient_ALL_2SDDense_b30_lr4_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e600_id2_bn_k9_conv2_balance

Pre-trained Models

Since OG-Net is really small, I has included trained models in this github repo ./snapshot.

If the model is trained on CUHK, Duke or MSMT, I will add dataset name in the model name, otherwise the model is trained on Market.

[ModelNet Performance]

I add OG-Net code to https://github.com/layumi/dgcnn
Results on ModelNet are 93.3 Top1 Accuracy / 90.5 MeanClass Top1 Accuracy.

Citation

You may cite it in your paper. Thanks a lot.

@article{zheng2022person,
  title={Parameter-Efficient Person Re-identification in the 3D Space},
  author={Zheng, Zhedong and Wang, Xiaohan and Zheng, Nenggan and Yang, Yi},
  journal={IEEE Transactions on Neural Networks and Learning Systems (TNNLS)},
  doi={10.1109/TNNLS.2022.3214834},
  note={\mbox{doi}:\url{10.1109/TNNLS.2022.3214834}},
  year={2022}
}

Related Work

We thank the great works of hmr, DGL, DGCNN and PointNet++. You may check their code at

• https://github.com/akanazawa/hmr
• https://github.com/dmlc/dgl/tree/master/examples/pytorch/pointcloud
• https://github.com/WangYueFt/dgcnn
• https://github.com/erikwijmans/Pointnet2_PyTorch

The baseline models used in the paper are modified from:

• https://github.com/layumi/Person_reID_baseline_pytorch

Acknowledge

I would like to thank the helpful comments and suggestions from Yaxiong Wang, Yuhang Ding, Qian Liu, Chuchu Han, Tianqi Tang, Zonghan Wu and Qipeng Guo.