Pytorch implementation of deep person re-identification models.
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deep-person-reid

PyTorch implementation of deep person re-identification models.

We support

  • multi-GPU training.
  • both image-based and video-based reid.
  • unified interface for different reid models.
  • easy dataset preparation.
  • end-to-end training and evaluation.
  • standard dataset splits used by most papers.
  • fast cython-based evaluation.

Get started

  1. cd to the folder where you want to download this repo.
  2. Run git clone https://github.com/KaiyangZhou/deep-person-reid.
  3. Install dependencies by pip install -r requirements.txt.
  4. To accelerate evaluation (10x faster), you can use cython-based evaluation code (developed by luzai). First cd to eval_lib, then do make or python setup.py build_ext -i. After that, run python test_cython_eval.py to test if the package is successfully installed.

Datasets

Image reid datasets:

  • Market1501 [7]
  • CUHK03 [13]
  • DukeMTMC-reID [16, 17]
  • MSMT17 [22]
  • VIPeR [28]
  • GRID [29]
  • CUHK01 [30]
  • PRID450S [31]
  • SenseReID [32]

Video reid datasets:

  • MARS [8]
  • iLIDS-VID [11]
  • PRID2011 [12]
  • DukeMTMC-VideoReID [16, 23]

Instructions regarding how to prepare (and do evaluation on) these datasets can be found here.

Models

  • torchreid/models/resnet.py: ResNet50 [1], ResNet101 [1], ResNet50M [2].
  • torchreid/models/resnext.py: ResNeXt101 [26].
  • torchreid/models/seresnet.py: SEResNet50 [25], SEResNet101 [25], SEResNeXt50 [25], SEResNeXt101 [25].
  • torchreid/models/densenet.py: DenseNet121 [3].
  • torchreid/models/mudeep.py: MuDeep [10].
  • torchreid/models/hacnn.py: HACNN [15].
  • torchreid/models/squeezenet.py: SqueezeNet [18].
  • torchreid/models/mobilenetv2.py: MobileNetV2 [19].
  • torchreid/models/shufflenet.py: ShuffleNet [20].
  • torchreid/models/xception.py: Xception [21].
  • torchreid/models/inceptionv4.py: InceptionV4 [24].
  • torchreid/models/inceptionresnetv2.py: InceptionResNetV2 [24].

See torchreid/models/__init__.py for details regarding what keys to use to call these models.

Benchmarks can be found here.

Train

Training codes are implemented in

  • train_imgreid_xent.py: train image model with cross entropy loss.
  • train_imgreid_xent_htri.py: train image model with combination of cross entropy loss and hard triplet loss.
  • train_vidreid_xent.py: train video model with cross entropy loss.
  • train_vidreid_xent_htri.py: train video model with combination of cross entropy loss and hard triplet loss.

For example, to train an image reid model using ResNet50 and cross entropy loss, run

python train_imgreid_xent.py -d market1501 -a resnet50 --optim adam --lr 0.0003 --max-epoch 60 --stepsize 20 40 --train-batch 32 --test-batch 100 --save-dir log/resnet50-xent-market1501 --gpu-devices 0

To use multiple GPUs, you can set --gpu-devices 0,1,2,3.

Note: To resume training, you can use --resume path/to/.pth.tar to load a checkpoint from which saved model weights and start_epoch will be used. Learning rate needs to be initialized carefully. If you just wanna load a pretrained model by discarding layers that do not match in size (e.g. classification layer), use --load-weights path/to/.pth.tar instead.

Please refer to the code for more details.

Test

Say you have downloaded ResNet50 trained with xent on market1501. The path to this model is 'saved-models/resnet50_xent_market1501.pth.tar' (create a directory to store model weights mkdir saved-models/ beforehand). Then, run the following command to test

python train_imgreid_xent.py -d market1501 -a resnet50 --evaluate --resume saved-models/resnet50_xent_market1501.pth.tar --save-dir log/resnet50-xent-market1501 --test-batch 100 --gpu-devices 0

Likewise, to test video reid model, you should have a pretrained model saved under saved-models/, e.g. saved-models/resnet50_xent_mars.pth.tar, then run

python train_vid_model_xent.py -d mars -a resnet50 --evaluate --resume saved-models/resnet50_xent_mars.pth.tar --save-dir log/resnet50-xent-mars --test-batch 2 --gpu-devices 0

Note that --test-batch in video reid represents number of tracklets. If you set this argument to 2, and sample 15 images per tracklet, the resulting number of images per batch is 2*15=30. Adjust this argument according to your GPU memory.

Visualizing ranked results

Ranked results can be visualized via --visualize-ranks, which works along with --evaluate. Ranked images will be saved in save_dir/ranked_results where save_dir is the directory you specify with --save-dir.

train

Issue

Before raising an issue, please have a look at the history issues where you may find answers. If those answers do not solve your problem, raise a new issue (choose an informative title) and include the following details in your question: (1) environmental settings, e.g. python version, torch/torchvision version, etc. (2) command that leads to the errors. (3) screenshot of error logs if available. If you find any errors in the code, please inform me by opening a new issue.

If you wanna contribute to this project, e.g. implementing new losses, please open an issue for discussion or directly email me.

Misc

Citation

Please link this project in your paper.

References

[1] He et al. Deep Residual Learning for Image Recognition. CVPR 2016.
[2] Yu et al. The Devil is in the Middle: Exploiting Mid-level Representations for Cross-Domain Instance Matching. arXiv:1711.08106.
[3] Huang et al. Densely Connected Convolutional Networks. CVPR 2017.
[4] Hermans et al. In Defense of the Triplet Loss for Person Re-Identification. arXiv:1703.07737.
[5] Szegedy et al. Rethinking the Inception Architecture for Computer Vision. CVPR 2016.
[6] Kingma and Ba. Adam: A Method for Stochastic Optimization. ICLR 2015.
[7] Zheng et al. Scalable Person Re-identification: A Benchmark. ICCV 2015.
[8] Zheng et al. MARS: A Video Benchmark for Large-Scale Person Re-identification. ECCV 2016.
[9] Wen et al. A Discriminative Feature Learning Approach for Deep Face Recognition. ECCV 2016
[10] Qian et al. Multi-scale Deep Learning Architectures for Person Re-identification. ICCV 2017.
[11] Wang et al. Person Re-Identification by Video Ranking. ECCV 2014.
[12] Hirzer et al. Person Re-Identification by Descriptive and Discriminative Classification. SCIA 2011.
[13] Li et al. DeepReID: Deep Filter Pairing Neural Network for Person Re-identification. CVPR 2014.
[14] Zhong et al. Re-ranking Person Re-identification with k-reciprocal Encoding. CVPR 2017
[15] Li et al. Harmonious Attention Network for Person Re-identification. CVPR 2018.
[16] Ristani et al. Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking. ECCVW 2016.
[17] Zheng et al. Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro. ICCV 2017.
[18] Iandola et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size. arXiv:1602.07360.
[19] Sandler et al. MobileNetV2: Inverted Residuals and Linear Bottlenecks. CVPR 2018.
[20] Zhang et al. ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. CVPR 2018.
[21] Chollet. Xception: Deep Learning with Depthwise Separable Convolutions. CVPR 2017.
[22] Wei et al. Person Transfer GAN to Bridge Domain Gap for Person Re-Identification. CVPR 2018.
[23] Wu et al. Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification by Stepwise Learning. CVPR 2018.
[24] Szegedy et al. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. ICLRW 2016.
[25] Hu et al. Squeeze-and-Excitation Networks. CVPR 2018.
[26] Xie et al. Aggregated Residual Transformations for Deep Neural Networks. CVPR 2017.
[27] Chen et al. Dual Path Networks. NIPS 2017.
[28] Gray et al. Evaluating appearance models for recognition, reacquisition, and tracking. PETS 2007.
[29] Loy et al. Multi-camera activity correlation analysis. CVPR 2009.
[30] Li et al. Human Reidentification with Transferred Metric Learning. ACCV 2012.
[31] Roth et al. Mahalanobis Distance Learning for Person Re-Identification. PR 2014.
[32] Zhao et al. Spindle Net: Person Re-identification with Human Body Region Guided Feature Decomposition and Fusion. CVPR 2017.