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A Pytorch Implementation of "Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro"(ICCV17)
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DCGAN-tensorflow
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README.md

Person-reid-GAN-pytorch

A Pytorch Implementation of "Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro"(ICCV17), the official code is available here(in matlab).

We arrived Rank@1=93.55%, mAP=90.67% only with a very easy model.

Random Erasing is added to help train as a data augmentation method. the details of Random Erasing is available here

re-rank strategy is used to deal with the initial result, the details of re-rank method is available here

Model Structure(we simply alter the model from ResNet and DenseNet)

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

Prerequisites

  • Python 2.7
  • GPU
  • Numpy
  • Pytorch
  • Torchvision

Getting started

Installation

  • Install Pytorch(the version is 0.2.0_3) from http://pytorch.org/
  • Install Torchvision from the source
git clone https://github.com/pytorch/vision
cd vision
python setup.py install

Dataset & Preparation

Download Market1501 Dataset

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.

Preparation: change the name of the folder to 0 to n-1(where n is the number of class, i.e. the number of person), the folder name is the label of each person (all the pictures under the same folder are the same person)

python changeIndex.py

the usage of the DCGAN model is under the DCGAN folder, first use market1501 to train the dcgan model, then generated some pictures using the trained DCGAN model, then you can use different numbers of generated images to help train the model using LSRO.

the generated images are in the gen_0000 folder, you can copy this folder under your training set, for more details, you can refer to DCGAN-TENSORFLOW folder

Our baseline code is only finetuned from resNet or DenseNet,
we use pretained DenseNet as baseline to train our model, the archieved result are as follows:

Rank@1 mAP Note
0.921 0.793 ----
0.934     0.907 re-rank

using LSRO loss and added some image generated by DCGAN model, the achieved result are as follow:

**Batchsize Multi/Single GPU training Rank@1 mAP Note**
32 Single 0.9162 0.7887 add 0 generated image
32 Single 0.9355 0.9067 after re-rank
32 Multi 0.8367 0.6442 add 0 generated image
32 Multi 0.8655 0.8143 after re-rank
64 Multi 0.843 0.646 add 0 generated image
64 Multi 0.872 0.815 after re-rank
32 Single 0.919 0.798 add 6000 generated image
32 Single 0.932 0.9012 after re-rank
64 Multi 0.909 0.779 add 12000 generated image
64 Multi 0.931 0.896 after re-rank
32 Single 0.925 0.801 add 12000 generated image
32 Single 0.939 0.904 after re-rank
64 Multi 0.915 0.790 add 18000 generated image
64 Multi 0.933 0.899 after re-rank
64 Multi 0.909 0.773 add 24000 generated image
64 Multi 0.924 0.887 after re-rank
32 Single 0.918 0.790 add 24000 generated image
32 Single 0.932 0.899 after re-rank

To save trained model, we make a dir.

mkdir model 

Train

Train the baseline by

python train_baseline.py --use_dense

--name the name of model.(ResNet or DesNet)

--data_dir the path of the training data.

--batchsize batch size.

--erasing_p random erasing probability.

Test

Use trained model to extract feature by

python test.py   --which_epoch 99  --use_dense

--gpu_ids which gpu to run.

--name the dir name of trained model.

--which_epoch select the i-th model.

--data_dir the path of the testing data.

--batchsize batch size.

Evaluation

python evaluate.py

It will output Rank@1, Rank@5, Rank@10 and mAP results.

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.

Conclusion

when the baseline result is not so high, the generated images can help model training(see multi-gpu training, add GAN images VS not add) , thus can improve the performance(more robust) , while when the baseline result is high(rank-1, 0.934), its difficult to improve the result. when batchsize is set to 32, the result is the best, and single-gpu training achieves better result than multi-gpu training.

Thanks

  • Many, many thanks to layumi for his Great work!

p.s. If you have any questions, you can open an issue!

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