301 lines
11 KiB
ReStructuredText
301 lines
11 KiB
ReStructuredText
Torchreid
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===========
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.. image:: https://img.shields.io/github/license/KaiyangZhou/deep-person-reid
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:alt: GitHub license
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:target: https://github.com/KaiyangZhou/deep-person-reid/blob/master/LICENSE
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Torchreid is a library for deep-learning person re-identification in `PyTorch <https://pytorch.org/>`_.
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It features:
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- multi-GPU training
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- support both image- and video-reid
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- end-to-end training and evaluation
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- incredibly easy preparation of reid datasets
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- multi-dataset training
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- cross-dataset evaluation
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- standard protocol used by most research papers
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- highly extensible (easy to add models, datasets, training methods, etc.)
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- implementations of state-of-the-art deep reid models
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- access to pretrained reid models
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- advanced training techniques
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- visualization tools (tensorboard, ranks, etc.)
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Code: https://github.com/KaiyangZhou/deep-person-reid.
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Documentation: https://kaiyangzhou.github.io/deep-person-reid/.
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How-to instructions: https://kaiyangzhou.github.io/deep-person-reid/user_guide.
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Model zoo: https://kaiyangzhou.github.io/deep-person-reid/MODEL_ZOO.
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Tech report: https://arxiv.org/abs/1910.10093.
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You can find some research projects that are built on top of Torchreid `here <https://github.com/KaiyangZhou/deep-person-reid/tree/master/projects>`_.
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What's new
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------------
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- [Apr 2020] Code for reproducing the experiments of `deep mutual learning <https://zpascal.net/cvpr2018/Zhang_Deep_Mutual_Learning_CVPR_2018_paper.pdf>`_ in `OSNet <https://arxiv.org/pdf/1905.00953v6.pdf>`_ (Supp. B) has been released at ``projects/DML``.
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- [Apr 2020] Upgraded to ``1.2.0``. The engine class has been made more model-agnostic to improve extensibility. See `Engine <torchreid/engine/engine.py>`_ and `ImageSoftmaxEngine <torchreid/engine/image/softmax.py>`_ for more details. Credit to `Dassl.pytorch <https://github.com/KaiyangZhou/Dassl.pytorch>`_.
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- [Dec 2019] Our `OSNet paper <https://arxiv.org/pdf/1905.00953v6.pdf>`_ has been updated, with additional experiments (in section B of the supplementary) showing some useful techniques for improving OSNet's performance in practice.
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- [Nov 2019] ``ImageDataManager`` can load training data from target datasets by setting ``load_train_targets=True``, and the train-loader can be accessed with ``train_loader_t = datamanager.train_loader_t``. This feature is useful for domain adaptation research.
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Installation
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---------------
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Make sure `conda <https://www.anaconda.com/distribution/>`_ is installed.
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.. code-block:: bash
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# cd to your preferred directory and clone this repo
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git clone https://github.com/KaiyangZhou/deep-person-reid.git
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# create environment
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cd deep-person-reid/
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conda create --name torchreid python=3.7
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conda activate torchreid
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# install dependencies
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# make sure `which python` and `which pip` point to the correct path
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pip install -r requirements.txt
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# install torch and torchvision (select the proper cuda version to suit your machine)
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conda install pytorch torchvision cudatoolkit=9.0 -c pytorch
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# install torchreid (don't need to re-build it if you modify the source code)
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python setup.py develop
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Get started: 30 seconds to Torchreid
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-------------------------------------
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1. Import ``torchreid``
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.. code-block:: python
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import torchreid
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2. Load data manager
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.. code-block:: python
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datamanager = torchreid.data.ImageDataManager(
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root='reid-data',
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sources='market1501',
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targets='market1501',
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height=256,
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width=128,
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batch_size_train=32,
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batch_size_test=100,
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transforms=['random_flip', 'random_crop']
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)
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3 Build model, optimizer and lr_scheduler
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.. code-block:: python
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model = torchreid.models.build_model(
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name='resnet50',
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num_classes=datamanager.num_train_pids,
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loss='softmax',
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pretrained=True
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)
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model = model.cuda()
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optimizer = torchreid.optim.build_optimizer(
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model,
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optim='adam',
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lr=0.0003
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)
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scheduler = torchreid.optim.build_lr_scheduler(
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optimizer,
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lr_scheduler='single_step',
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stepsize=20
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)
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4. Build engine
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.. code-block:: python
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engine = torchreid.engine.ImageSoftmaxEngine(
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datamanager,
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model,
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optimizer=optimizer,
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scheduler=scheduler,
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label_smooth=True
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)
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5. Run training and test
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.. code-block:: python
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engine.run(
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save_dir='log/resnet50',
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max_epoch=60,
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eval_freq=10,
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print_freq=10,
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test_only=False
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)
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A unified interface
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-----------------------
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In "deep-person-reid/scripts/", we provide a unified interface to train and test a model. See "scripts/main.py" and "scripts/default_config.py" for more details. The folder "configs/" contains some predefined configs which you can use as a starting point.
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Below we provide an example to train and test `OSNet (Zhou et al. ICCV'19) <https://arxiv.org/abs/1905.00953>`_. Assume :code:`PATH_TO_DATA` is the directory containing reid datasets. The environmental variable :code:`CUDA_VISIBLE_DEVICES` is omitted, which you need to specify if you have a pool of gpus and want to use a specific set of them.
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Conventional setting
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^^^^^^^^^^^^^^^^^^^^^
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To train OSNet on Market1501, do
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.. code-block:: bash
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python scripts/main.py \
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--config-file configs/im_osnet_x1_0_softmax_256x128_amsgrad_cosine.yaml \
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--transforms random_flip random_erase \
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--root $PATH_TO_DATA
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The config file sets Market1501 as the default dataset. If you wanna use DukeMTMC-reID, do
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.. code-block:: bash
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python scripts/main.py \
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--config-file configs/im_osnet_x1_0_softmax_256x128_amsgrad_cosine.yaml \
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-s dukemtmcreid \
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-t dukemtmcreid \
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--transforms random_flip random_erase \
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--root $PATH_TO_DATA \
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data.save_dir log/osnet_x1_0_dukemtmcreid_softmax_cosinelr
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The code will automatically (download and) load the ImageNet pretrained weights. After the training is done, the model will be saved as "log/osnet_x1_0_market1501_softmax_cosinelr/model.pth.tar-250". Under the same folder, you can find the `tensorboard <https://pytorch.org/docs/stable/tensorboard.html>`_ file. To visualize the learning curves using tensorboard, you can run :code:`tensorboard --logdir=log/osnet_x1_0_market1501_softmax_cosinelr` in the terminal and visit :code:`http://localhost:6006/` in your web browser.
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Evaluation is automatically performed at the end of training. To run the test again using the trained model, do
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.. code-block:: bash
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python scripts/main.py \
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--config-file configs/im_osnet_x1_0_softmax_256x128_amsgrad_cosine.yaml \
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--root $PATH_TO_DATA \
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model.load_weights log/osnet_x1_0_market1501_softmax_cosinelr/model.pth.tar-250 \
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test.evaluate True
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Cross-domain setting
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^^^^^^^^^^^^^^^^^^^^^
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Suppose you wanna train OSNet on DukeMTMC-reID and test its performance on Market1501, you can do
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.. code-block:: bash
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python scripts/main.py \
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--config-file configs/im_osnet_x1_0_softmax_256x128_amsgrad.yaml \
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-s dukemtmcreid \
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-t market1501 \
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--transforms random_flip color_jitter \
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--root $PATH_TO_DATA
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Here we only test the cross-domain performance. However, if you also want to test the performance on the source dataset, i.e. DukeMTMC-reID, you can set :code:`-t dukemtmcreid market1501`, which will evaluate the model on the two datasets separately.
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Different from the same-domain setting, here we replace :code:`random_erase` with :code:`color_jitter`. This can improve the generalization performance on the unseen target dataset.
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Pretrained models are available in the `Model Zoo <https://kaiyangzhou.github.io/deep-person-reid/MODEL_ZOO.html>`_.
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Datasets
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--------
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Image-reid datasets
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^^^^^^^^^^^^^^^^^^^^^
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- `Market1501 <https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Zheng_Scalable_Person_Re-Identification_ICCV_2015_paper.pdf>`_
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- `CUHK03 <https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Li_DeepReID_Deep_Filter_2014_CVPR_paper.pdf>`_
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- `DukeMTMC-reID <https://arxiv.org/abs/1701.07717>`_
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- `MSMT17 <https://arxiv.org/abs/1711.08565>`_
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- `VIPeR <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.331.7285&rep=rep1&type=pdf>`_
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- `GRID <http://www.eecs.qmul.ac.uk/~txiang/publications/LoyXiangGong_cvpr_2009.pdf>`_
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- `CUHK01 <http://www.ee.cuhk.edu.hk/~xgwang/papers/liZWaccv12.pdf>`_
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- `SenseReID <http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhao_Spindle_Net_Person_CVPR_2017_paper.pdf>`_
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- `QMUL-iLIDS <http://www.eecs.qmul.ac.uk/~sgg/papers/ZhengGongXiang_BMVC09.pdf>`_
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- `PRID <https://pdfs.semanticscholar.org/4c1b/f0592be3e535faf256c95e27982db9b3d3d3.pdf>`_
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Video-reid datasets
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^^^^^^^^^^^^^^^^^^^^^^^
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- `MARS <http://www.liangzheng.org/1320.pdf>`_
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- `iLIDS-VID <https://www.eecs.qmul.ac.uk/~sgg/papers/WangEtAl_ECCV14.pdf>`_
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- `PRID2011 <https://pdfs.semanticscholar.org/4c1b/f0592be3e535faf256c95e27982db9b3d3d3.pdf>`_
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- `DukeMTMC-VideoReID <http://openaccess.thecvf.com/content_cvpr_2018/papers/Wu_Exploit_the_Unknown_CVPR_2018_paper.pdf>`_
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Models
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-------
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ImageNet classification models
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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- `ResNet <https://arxiv.org/abs/1512.03385>`_
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- `ResNeXt <https://arxiv.org/abs/1611.05431>`_
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- `SENet <https://arxiv.org/abs/1709.01507>`_
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- `DenseNet <https://arxiv.org/abs/1608.06993>`_
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- `Inception-ResNet-V2 <https://arxiv.org/abs/1602.07261>`_
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- `Inception-V4 <https://arxiv.org/abs/1602.07261>`_
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- `Xception <https://arxiv.org/abs/1610.02357>`_
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- `IBN-Net <https://arxiv.org/abs/1807.09441>`_
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Lightweight models
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^^^^^^^^^^^^^^^^^^^
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- `NASNet <https://arxiv.org/abs/1707.07012>`_
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- `MobileNetV2 <https://arxiv.org/abs/1801.04381>`_
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- `ShuffleNet <https://arxiv.org/abs/1707.01083>`_
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- `ShuffleNetV2 <https://arxiv.org/abs/1807.11164>`_
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- `SqueezeNet <https://arxiv.org/abs/1602.07360>`_
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ReID-specific models
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^^^^^^^^^^^^^^^^^^^^^^
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- `MuDeep <https://arxiv.org/abs/1709.05165>`_
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- `ResNet-mid <https://arxiv.org/abs/1711.08106>`_
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- `HACNN <https://arxiv.org/abs/1802.08122>`_
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- `PCB <https://arxiv.org/abs/1711.09349>`_
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- `MLFN <https://arxiv.org/abs/1803.09132>`_
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- `OSNet <https://arxiv.org/abs/1905.00953>`_
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- `OSNet-AIN <https://arxiv.org/abs/1910.06827>`_
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Useful links
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-------------
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- `OSNet-IBN1-Lite (test-only code with lite docker container) <https://github.com/RodMech/OSNet-IBN1-Lite>`_
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- `Deep Learning for Person Re-identification: A Survey and Outlook <https://github.com/mangye16/ReID-Survey>`_
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Citation
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---------
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If you find this code useful to your research, please cite the following papers.
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.. code-block:: bash
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@article{torchreid,
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title={Torchreid: A Library for Deep Learning Person Re-Identification in Pytorch},
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author={Zhou, Kaiyang and Xiang, Tao},
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journal={arXiv preprint arXiv:1910.10093},
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year={2019}
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}
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@inproceedings{zhou2019osnet,
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title={Omni-Scale Feature Learning for Person Re-Identification},
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author={Zhou, Kaiyang and Yang, Yongxin and Cavallaro, Andrea and Xiang, Tao},
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booktitle={ICCV},
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year={2019}
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}
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@article{zhou2019learning,
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title={Learning Generalisable Omni-Scale Representations for Person Re-Identification},
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author={Zhou, Kaiyang and Yang, Yongxin and Cavallaro, Andrea and Xiang, Tao},
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journal={arXiv preprint arXiv:1910.06827},
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year={2019}
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}
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