2019-03-24 17:22:43 +00:00
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2020-04-16 13:17:01 +01:00
< title > Torchreid — torchreid 1.2.0 documentation< / title >
2019-03-24 17:22:43 +00:00
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2020-04-16 13:17:01 +01:00
1.2.0
2019-03-24 17:22:43 +00:00
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< h1 > Torchreid< a class = "headerlink" href = "#torchreid" title = "Permalink to this headline" > ¶< / a > < / h1 >
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< p > Torchreid is a library for deep-learning person re-identification in < a class = "reference external" href = "https://pytorch.org/" > PyTorch< / a > .< / p >
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< p > It features:< / p >
< ul class = "simple" >
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< li > < p > multi-GPU training< / p > < / li >
< li > < p > support both image- and video-reid< / p > < / li >
< li > < p > end-to-end training and evaluation< / p > < / li >
< li > < p > incredibly easy preparation of reid datasets< / p > < / li >
< li > < p > multi-dataset training< / p > < / li >
< li > < p > cross-dataset evaluation< / p > < / li >
< li > < p > standard protocol used by most research papers< / p > < / li >
< li > < p > highly extensible (easy to add models, datasets, training methods, etc.)< / p > < / li >
< li > < p > implementations of state-of-the-art deep reid models< / p > < / li >
< li > < p > access to pretrained reid models< / p > < / li >
< li > < p > advanced training techniques< / p > < / li >
< li > < p > visualization tools (tensorboard, ranks, etc.)< / p > < / li >
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< / ul >
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< p > Code: < a class = "reference external" href = "https://github.com/KaiyangZhou/deep-person-reid" > https://github.com/KaiyangZhou/deep-person-reid< / a > .< / p >
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< p > Documentation: < a class = "reference external" href = "https://kaiyangzhou.github.io/deep-person-reid/" > https://kaiyangzhou.github.io/deep-person-reid/< / a > .< / p >
< p > How-to instructions: < a class = "reference external" href = "https://kaiyangzhou.github.io/deep-person-reid/user_guide" > https://kaiyangzhou.github.io/deep-person-reid/user_guide< / a > .< / p >
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< p > Model zoo: < a class = "reference external" href = "https://kaiyangzhou.github.io/deep-person-reid/MODEL_ZOO" > https://kaiyangzhou.github.io/deep-person-reid/MODEL_ZOO< / a > .< / p >
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< p > Tech report: < a class = "reference external" href = "https://arxiv.org/abs/1910.10093" > https://arxiv.org/abs/1910.10093< / a > .< / p >
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< p > You can find some research projects that are built on top of Torchreid < a class = "reference external" href = "https://github.com/KaiyangZhou/deep-person-reid/tree/master/projects" > here< / a > .< / p >
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< div class = "section" id = "what-s-new" >
< h2 > What’ < a class = "headerlink" href = "#what-s-new" title = "Permalink to this headline" > ¶< / a > < / h2 >
< ul class = "simple" >
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< li > < p > [Apr 2020] Code for reproducing the experiments of < a class = "reference external" href = "https://zpascal.net/cvpr2018/Zhang_Deep_Mutual_Learning_CVPR_2018_paper.pdf" > deep mutual learning< / a > in the < a class = "reference external" href = "https://arxiv.org/pdf/1905.00953v6.pdf" > OSNet paper< / a > (Supp. B) has been released at < code class = "docutils literal notranslate" > < span class = "pre" > projects/DML< / span > < / code > .< / p > < / li >
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< li > < p > [Apr 2020] Upgraded to < code class = "docutils literal notranslate" > < span class = "pre" > 1.2.0< / span > < / code > . The engine class has been made more model-agnostic to improve extensibility. See < a class = "reference external" href = "torchreid/engine/engine.py" > Engine< / a > and < a class = "reference external" href = "torchreid/engine/image/softmax.py" > ImageSoftmaxEngine< / a > for more details. Credit to < a class = "reference external" href = "https://github.com/KaiyangZhou/Dassl.pytorch" > Dassl.pytorch< / a > .< / p > < / li >
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< li > < p > [Dec 2019] Our < a class = "reference external" href = "https://arxiv.org/pdf/1905.00953v6.pdf" > OSNet paper< / a > has been updated, with additional experiments (in section B of the supplementary) showing some useful techniques for improving OSNet’ < / p > < / li >
< li > < p > [Nov 2019] < code class = "docutils literal notranslate" > < span class = "pre" > ImageDataManager< / span > < / code > can load training data from target datasets by setting < code class = "docutils literal notranslate" > < span class = "pre" > load_train_targets=True< / span > < / code > , and the train-loader can be accessed with < code class = "docutils literal notranslate" > < span class = "pre" > train_loader_t< / span > < span class = "pre" > =< / span > < span class = "pre" > datamanager.train_loader_t< / span > < / code > . This feature is useful for domain adaptation research.< / p > < / li >
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< / ul >
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< div class = "section" id = "installation" >
< h2 > Installation< a class = "headerlink" href = "#installation" title = "Permalink to this headline" > ¶< / a > < / h2 >
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< p > Make sure < a class = "reference external" href = "https://www.anaconda.com/distribution/" > conda< / a > is installed.< / p >
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< div class = "highlight-bash notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "c1" > # cd to your preferred directory and clone this repo< / span >
git clone https://github.com/KaiyangZhou/deep-person-reid.git
< span class = "c1" > # create environment< / span >
< span class = "nb" > cd< / span > deep-person-reid/
conda create --name torchreid < span class = "nv" > python< / span > < span class = "o" > =< / span > < span class = "m" > 3< / span > .7
conda activate torchreid
< span class = "c1" > # install dependencies< / span >
< span class = "c1" > # make sure `which python` and `which pip` point to the correct path< / span >
pip install -r requirements.txt
< span class = "c1" > # install torch and torchvision (select the proper cuda version to suit your machine)< / span >
conda install pytorch torchvision < span class = "nv" > cudatoolkit< / span > < span class = "o" > =< / span > < span class = "m" > 9< / span > .0 -c pytorch
< span class = "c1" > # install torchreid (don' t need to re-build it if you modify the source code)< / span >
python setup.py develop
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< / pre > < / div >
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< / div >
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< div class = "section" id = "get-started-30-seconds-to-torchreid" >
< h2 > Get started: 30 seconds to Torchreid< a class = "headerlink" href = "#get-started-30-seconds-to-torchreid" title = "Permalink to this headline" > ¶< / a > < / h2 >
< ol class = "arabic simple" >
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< li > < p > Import < code class = "docutils literal notranslate" > < span class = "pre" > torchreid< / span > < / code > < / p > < / li >
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< / ol >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > import< / span > < span class = "nn" > torchreid< / span >
< / pre > < / div >
< / div >
< ol class = "arabic simple" start = "2" >
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< li > < p > Load data manager< / p > < / li >
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< / ol >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > datamanager< / span > < span class = "o" > =< / span > < span class = "n" > torchreid< / span > < span class = "o" > .< / span > < span class = "n" > data< / span > < span class = "o" > .< / span > < span class = "n" > ImageDataManager< / span > < span class = "p" > (< / span >
< span class = "n" > root< / span > < span class = "o" > =< / span > < span class = "s1" > ' reid-data' < / span > < span class = "p" > ,< / span >
< span class = "n" > sources< / span > < span class = "o" > =< / span > < span class = "s1" > ' market1501' < / span > < span class = "p" > ,< / span >
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< span class = "n" > targets< / span > < span class = "o" > =< / span > < span class = "s1" > ' market1501' < / span > < span class = "p" > ,< / span >
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< span class = "n" > height< / span > < span class = "o" > =< / span > < span class = "mi" > 256< / span > < span class = "p" > ,< / span >
< span class = "n" > width< / span > < span class = "o" > =< / span > < span class = "mi" > 128< / span > < span class = "p" > ,< / span >
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< span class = "n" > batch_size_train< / span > < span class = "o" > =< / span > < span class = "mi" > 32< / span > < span class = "p" > ,< / span >
< span class = "n" > batch_size_test< / span > < span class = "o" > =< / span > < span class = "mi" > 100< / span > < span class = "p" > ,< / span >
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< span class = "n" > transforms< / span > < span class = "o" > =< / span > < span class = "p" > [< / span > < span class = "s1" > ' random_flip' < / span > < span class = "p" > ,< / span > < span class = "s1" > ' random_crop' < / span > < span class = "p" > ]< / span >
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< span class = "p" > )< / span >
< / pre > < / div >
< / div >
< p > 3 Build model, optimizer and lr_scheduler< / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > model< / span > < span class = "o" > =< / span > < span class = "n" > torchreid< / span > < span class = "o" > .< / span > < span class = "n" > models< / span > < span class = "o" > .< / span > < span class = "n" > build_model< / span > < span class = "p" > (< / span >
< span class = "n" > name< / span > < span class = "o" > =< / span > < span class = "s1" > ' resnet50' < / span > < span class = "p" > ,< / span >
< span class = "n" > num_classes< / span > < span class = "o" > =< / span > < span class = "n" > datamanager< / span > < span class = "o" > .< / span > < span class = "n" > num_train_pids< / span > < span class = "p" > ,< / span >
< span class = "n" > loss< / span > < span class = "o" > =< / span > < span class = "s1" > ' softmax' < / span > < span class = "p" > ,< / span >
< span class = "n" > pretrained< / span > < span class = "o" > =< / span > < span class = "bp" > True< / span >
< span class = "p" > )< / span >
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< span class = "n" > model< / span > < span class = "o" > =< / span > < span class = "n" > model< / span > < span class = "o" > .< / span > < span class = "n" > cuda< / span > < span class = "p" > ()< / span >
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< span class = "n" > optimizer< / span > < span class = "o" > =< / span > < span class = "n" > torchreid< / span > < span class = "o" > .< / span > < span class = "n" > optim< / span > < span class = "o" > .< / span > < span class = "n" > build_optimizer< / span > < span class = "p" > (< / span >
< span class = "n" > model< / span > < span class = "p" > ,< / span >
< span class = "n" > optim< / span > < span class = "o" > =< / span > < span class = "s1" > ' adam' < / span > < span class = "p" > ,< / span >
< span class = "n" > lr< / span > < span class = "o" > =< / span > < span class = "mf" > 0.0003< / span >
< span class = "p" > )< / span >
< span class = "n" > scheduler< / span > < span class = "o" > =< / span > < span class = "n" > torchreid< / span > < span class = "o" > .< / span > < span class = "n" > optim< / span > < span class = "o" > .< / span > < span class = "n" > build_lr_scheduler< / span > < span class = "p" > (< / span >
< span class = "n" > optimizer< / span > < span class = "p" > ,< / span >
< span class = "n" > lr_scheduler< / span > < span class = "o" > =< / span > < span class = "s1" > ' single_step' < / span > < span class = "p" > ,< / span >
< span class = "n" > stepsize< / span > < span class = "o" > =< / span > < span class = "mi" > 20< / span >
< span class = "p" > )< / span >
< / pre > < / div >
< / div >
< ol class = "arabic simple" start = "4" >
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< li > < p > Build engine< / p > < / li >
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< / ol >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > engine< / span > < span class = "o" > =< / span > < span class = "n" > torchreid< / span > < span class = "o" > .< / span > < span class = "n" > engine< / span > < span class = "o" > .< / span > < span class = "n" > ImageSoftmaxEngine< / span > < span class = "p" > (< / span >
< span class = "n" > datamanager< / span > < span class = "p" > ,< / span >
< span class = "n" > model< / span > < span class = "p" > ,< / span >
< span class = "n" > optimizer< / span > < span class = "o" > =< / span > < span class = "n" > optimizer< / span > < span class = "p" > ,< / span >
< span class = "n" > scheduler< / span > < span class = "o" > =< / span > < span class = "n" > scheduler< / span > < span class = "p" > ,< / span >
< span class = "n" > label_smooth< / span > < span class = "o" > =< / span > < span class = "bp" > True< / span >
< span class = "p" > )< / span >
< / pre > < / div >
< / div >
< ol class = "arabic simple" start = "5" >
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< li > < p > Run training and test< / p > < / li >
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< / ol >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > engine< / span > < span class = "o" > .< / span > < span class = "n" > run< / span > < span class = "p" > (< / span >
< span class = "n" > save_dir< / span > < span class = "o" > =< / span > < span class = "s1" > ' log/resnet50' < / span > < span class = "p" > ,< / span >
< span class = "n" > max_epoch< / span > < span class = "o" > =< / span > < span class = "mi" > 60< / span > < span class = "p" > ,< / span >
< span class = "n" > eval_freq< / span > < span class = "o" > =< / span > < span class = "mi" > 10< / span > < span class = "p" > ,< / span >
< span class = "n" > print_freq< / span > < span class = "o" > =< / span > < span class = "mi" > 10< / span > < span class = "p" > ,< / span >
< span class = "n" > test_only< / span > < span class = "o" > =< / span > < span class = "bp" > False< / span >
< span class = "p" > )< / span >
< / pre > < / div >
< / div >
< / div >
< div class = "section" id = "a-unified-interface" >
< h2 > A unified interface< a class = "headerlink" href = "#a-unified-interface" title = "Permalink to this headline" > ¶< / a > < / h2 >
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< p > 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.< / p >
< p > Below we provide an example to train and test < a class = "reference external" href = "https://arxiv.org/abs/1905.00953" > OSNet (Zhou et al. ICCV’ < / a > . Assume < code class = "code docutils literal notranslate" > < span class = "pre" > PATH_TO_DATA< / span > < / code > is the directory containing reid datasets. The environmental variable < code class = "code docutils literal notranslate" > < span class = "pre" > CUDA_VISIBLE_DEVICES< / span > < / code > is omitted, which you need to specify if you have a pool of gpus and want to use a specific set of them.< / p >
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< div class = "section" id = "conventional-setting" >
< h3 > Conventional setting< a class = "headerlink" href = "#conventional-setting" title = "Permalink to this headline" > ¶< / a > < / h3 >
< p > To train OSNet on Market1501, do< / p >
< div class = "highlight-bash notranslate" > < div class = "highlight" > < pre > < span > < / span > python scripts/main.py < span class = "se" > \< / span >
--config-file configs/im_osnet_x1_0_softmax_256x128_amsgrad_cosine.yaml < span class = "se" > \< / span >
--transforms random_flip random_erase < span class = "se" > \< / span >
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--root < span class = "nv" > $PATH_TO_DATA< / span >
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< / pre > < / div >
< / div >
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< p > The config file sets Market1501 as the default dataset. If you wanna use DukeMTMC-reID, do< / p >
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< div class = "highlight-bash notranslate" > < div class = "highlight" > < pre > < span > < / span > python scripts/main.py < span class = "se" > \< / span >
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--config-file configs/im_osnet_x1_0_softmax_256x128_amsgrad_cosine.yaml < span class = "se" > \< / span >
-s dukemtmcreid < span class = "se" > \< / span >
-t dukemtmcreid < span class = "se" > \< / span >
--transforms random_flip random_erase < span class = "se" > \< / span >
--root < span class = "nv" > $PATH_TO_DATA< / span > < span class = "se" > \< / span >
data.save_dir log/osnet_x1_0_dukemtmcreid_softmax_cosinelr
< / pre > < / div >
< / div >
< p > 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 < a class = "reference external" href = "https://pytorch.org/docs/stable/tensorboard.html" > tensorboard< / a > file. To visualize the learning curves using tensorboard, you can run < code class = "code docutils literal notranslate" > < span class = "pre" > tensorboard< / span > < span class = "pre" > --logdir=log/osnet_x1_0_market1501_softmax_cosinelr< / span > < / code > in the terminal and visit < code class = "code docutils literal notranslate" > < span class = "pre" > http://localhost:6006/< / span > < / code > in your web browser.< / p >
< p > Evaluation is automatically performed at the end of training. To run the test again using the trained model, do< / p >
< div class = "highlight-bash notranslate" > < div class = "highlight" > < pre > < span > < / span > python scripts/main.py < span class = "se" > \< / span >
--config-file configs/im_osnet_x1_0_softmax_256x128_amsgrad_cosine.yaml < span class = "se" > \< / span >
--root < span class = "nv" > $PATH_TO_DATA< / span > < span class = "se" > \< / span >
model.load_weights log/osnet_x1_0_market1501_softmax_cosinelr/model.pth.tar-250 < span class = "se" > \< / span >
test.evaluate True
< / pre > < / div >
< / div >
< / div >
< div class = "section" id = "cross-domain-setting" >
< h3 > Cross-domain setting< a class = "headerlink" href = "#cross-domain-setting" title = "Permalink to this headline" > ¶< / a > < / h3 >
< p > Suppose you wanna train OSNet on DukeMTMC-reID and test its performance on Market1501, you can do< / p >
< div class = "highlight-bash notranslate" > < div class = "highlight" > < pre > < span > < / span > python scripts/main.py < span class = "se" > \< / span >
--config-file configs/im_osnet_x1_0_softmax_256x128_amsgrad.yaml < span class = "se" > \< / span >
-s dukemtmcreid < span class = "se" > \< / span >
-t market1501 < span class = "se" > \< / span >
--transforms random_flip color_jitter < span class = "se" > \< / span >
2019-12-03 10:36:12 +00:00
--root < span class = "nv" > $PATH_TO_DATA< / span >
2019-08-23 22:50:43 +01:00
< / pre > < / div >
< / div >
2019-08-26 13:27:00 +01:00
< p > 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 class = "code docutils literal notranslate" > < span class = "pre" > -t< / span > < span class = "pre" > dukemtmcreid< / span > < span class = "pre" > market1501< / span > < / code > , which will evaluate the model on the two datasets separately.< / p >
< p > Different from the same-domain setting, here we replace < code class = "code docutils literal notranslate" > < span class = "pre" > random_erase< / span > < / code > with < code class = "code docutils literal notranslate" > < span class = "pre" > color_jitter< / span > < / code > . This can improve the generalization performance on the unseen target dataset.< / p >
< p > Pretrained models are available in the < a class = "reference external" href = "https://kaiyangzhou.github.io/deep-person-reid/MODEL_ZOO.html" > Model Zoo< / a > .< / p >
< / div >
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< / div >
< div class = "section" id = "datasets" >
< h2 > Datasets< a class = "headerlink" href = "#datasets" title = "Permalink to this headline" > ¶< / a > < / h2 >
< div class = "section" id = "image-reid-datasets" >
< h3 > Image-reid datasets< a class = "headerlink" href = "#image-reid-datasets" title = "Permalink to this headline" > ¶< / a > < / h3 >
< ul class = "simple" >
2019-11-27 11:10:02 +00:00
< li > < p > < a class = "reference external" href = "https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Zheng_Scalable_Person_Re-Identification_ICCV_2015_paper.pdf" > Market1501< / a > < / p > < / li >
< li > < p > < a class = "reference external" href = "https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Li_DeepReID_Deep_Filter_2014_CVPR_paper.pdf" > CUHK03< / a > < / p > < / li >
< li > < p > < a class = "reference external" href = "https://arxiv.org/abs/1701.07717" > DukeMTMC-reID< / a > < / p > < / li >
< li > < p > < a class = "reference external" href = "https://arxiv.org/abs/1711.08565" > MSMT17< / a > < / p > < / li >
< li > < p > < a class = "reference external" href = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.331.7285&rep=rep1&type=pdf" > VIPeR< / a > < / p > < / li >
< li > < p > < a class = "reference external" href = "http://www.eecs.qmul.ac.uk/~txiang/publications/LoyXiangGong_cvpr_2009.pdf" > GRID< / a > < / p > < / li >
< li > < p > < a class = "reference external" href = "http://www.ee.cuhk.edu.hk/~xgwang/papers/liZWaccv12.pdf" > CUHK01< / a > < / p > < / li >
< li > < p > < a class = "reference external" href = "http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhao_Spindle_Net_Person_CVPR_2017_paper.pdf" > SenseReID< / a > < / p > < / li >
< li > < p > < a class = "reference external" href = "http://www.eecs.qmul.ac.uk/~sgg/papers/ZhengGongXiang_BMVC09.pdf" > QMUL-iLIDS< / a > < / p > < / li >
< li > < p > < a class = "reference external" href = "https://pdfs.semanticscholar.org/4c1b/f0592be3e535faf256c95e27982db9b3d3d3.pdf" > PRID< / a > < / p > < / li >
2019-03-24 17:22:43 +00:00
< / ul >
< / div >
< div class = "section" id = "video-reid-datasets" >
< h3 > Video-reid datasets< a class = "headerlink" href = "#video-reid-datasets" title = "Permalink to this headline" > ¶< / a > < / h3 >
< ul class = "simple" >
2019-11-27 11:10:02 +00:00
< li > < p > < a class = "reference external" href = "http://www.liangzheng.org/1320.pdf" > MARS< / a > < / p > < / li >
< li > < p > < a class = "reference external" href = "https://www.eecs.qmul.ac.uk/~sgg/papers/WangEtAl_ECCV14.pdf" > iLIDS-VID< / a > < / p > < / li >
< li > < p > < a class = "reference external" href = "https://pdfs.semanticscholar.org/4c1b/f0592be3e535faf256c95e27982db9b3d3d3.pdf" > PRID2011< / a > < / p > < / li >
< li > < p > < a class = "reference external" href = "http://openaccess.thecvf.com/content_cvpr_2018/papers/Wu_Exploit_the_Unknown_CVPR_2018_paper.pdf" > DukeMTMC-VideoReID< / a > < / p > < / li >
2019-03-24 17:22:43 +00:00
< / ul >
< / div >
< / div >
< div class = "section" id = "models" >
< h2 > Models< a class = "headerlink" href = "#models" title = "Permalink to this headline" > ¶< / a > < / h2 >
< div class = "section" id = "imagenet-classification-models" >
< h3 > ImageNet classification models< a class = "headerlink" href = "#imagenet-classification-models" title = "Permalink to this headline" > ¶< / a > < / h3 >
< ul class = "simple" >
2019-11-27 11:10:02 +00:00
< li > < p > < a class = "reference external" href = "https://arxiv.org/abs/1512.03385" > ResNet< / a > < / p > < / li >
< li > < p > < a class = "reference external" href = "https://arxiv.org/abs/1611.05431" > ResNeXt< / a > < / p > < / li >
< li > < p > < a class = "reference external" href = "https://arxiv.org/abs/1709.01507" > SENet< / a > < / p > < / li >
< li > < p > < a class = "reference external" href = "https://arxiv.org/abs/1608.06993" > DenseNet< / a > < / p > < / li >
< li > < p > < a class = "reference external" href = "https://arxiv.org/abs/1602.07261" > Inception-ResNet-V2< / a > < / p > < / li >
< li > < p > < a class = "reference external" href = "https://arxiv.org/abs/1602.07261" > Inception-V4< / a > < / p > < / li >
< li > < p > < a class = "reference external" href = "https://arxiv.org/abs/1610.02357" > Xception< / a > < / p > < / li >
< li > < p > < a class = "reference external" href = "https://arxiv.org/abs/1807.09441" > IBN-Net< / a > < / p > < / li >
2019-03-24 17:22:43 +00:00
< / ul >
< / div >
< div class = "section" id = "lightweight-models" >
< h3 > Lightweight models< a class = "headerlink" href = "#lightweight-models" title = "Permalink to this headline" > ¶< / a > < / h3 >
< ul class = "simple" >
2019-11-27 11:10:02 +00:00
< li > < p > < a class = "reference external" href = "https://arxiv.org/abs/1707.07012" > NASNet< / a > < / p > < / li >
< li > < p > < a class = "reference external" href = "https://arxiv.org/abs/1801.04381" > MobileNetV2< / a > < / p > < / li >
< li > < p > < a class = "reference external" href = "https://arxiv.org/abs/1707.01083" > ShuffleNet< / a > < / p > < / li >
< li > < p > < a class = "reference external" href = "https://arxiv.org/abs/1807.11164" > ShuffleNetV2< / a > < / p > < / li >
< li > < p > < a class = "reference external" href = "https://arxiv.org/abs/1602.07360" > SqueezeNet< / a > < / p > < / li >
2019-03-24 17:22:43 +00:00
< / ul >
< / div >
< div class = "section" id = "reid-specific-models" >
< h3 > ReID-specific models< a class = "headerlink" href = "#reid-specific-models" title = "Permalink to this headline" > ¶< / a > < / h3 >
< ul class = "simple" >
2019-11-27 11:10:02 +00:00
< li > < p > < a class = "reference external" href = "https://arxiv.org/abs/1709.05165" > MuDeep< / a > < / p > < / li >
< li > < p > < a class = "reference external" href = "https://arxiv.org/abs/1711.08106" > ResNet-mid< / a > < / p > < / li >
< li > < p > < a class = "reference external" href = "https://arxiv.org/abs/1802.08122" > HACNN< / a > < / p > < / li >
< li > < p > < a class = "reference external" href = "https://arxiv.org/abs/1711.09349" > PCB< / a > < / p > < / li >
< li > < p > < a class = "reference external" href = "https://arxiv.org/abs/1803.09132" > MLFN< / a > < / p > < / li >
< li > < p > < a class = "reference external" href = "https://arxiv.org/abs/1905.00953" > OSNet< / a > < / p > < / li >
< li > < p > < a class = "reference external" href = "https://arxiv.org/abs/1910.06827" > OSNet-AIN< / a > < / p > < / li >
2019-03-24 17:22:43 +00:00
< / ul >
< / div >
< / div >
2019-11-21 23:46:58 +00:00
< div class = "section" id = "useful-links" >
< h2 > Useful links< a class = "headerlink" href = "#useful-links" title = "Permalink to this headline" > ¶< / a > < / h2 >
< ul class = "simple" >
2019-11-27 11:10:02 +00:00
< li > < p > < a class = "reference external" href = "https://github.com/RodMech/OSNet-IBN1-Lite" > OSNet-IBN1-Lite (test-only code with lite docker container)< / a > < / p > < / li >
2020-01-16 16:46:36 +00:00
< li > < p > < a class = "reference external" href = "https://github.com/mangye16/ReID-Survey" > Deep Learning for Person Re-identification: A Survey and Outlook< / a > < / p > < / li >
2019-03-24 17:22:43 +00:00
< / ul >
< / div >
< div class = "section" id = "citation" >
< h2 > Citation< a class = "headerlink" href = "#citation" title = "Permalink to this headline" > ¶< / a > < / h2 >
2019-11-08 14:19:48 +00:00
< p > If you find this code useful to your research, please cite the following papers.< / p >
2019-10-23 16:12:46 +01:00
< div class = "highlight-bash notranslate" > < div class = "highlight" > < pre > < span > < / span > @article< span class = "o" > {< / span > torchreid,
< span class = "nv" > title< / span > < span class = "o" > ={< / span > Torchreid: A Library < span class = "k" > for< / span > Deep Learning Person Re-Identification in Pytorch< span class = "o" > }< / span > ,
< span class = "nv" > author< / span > < span class = "o" > ={< / span > Zhou, Kaiyang and Xiang, Tao< span class = "o" > }< / span > ,
< span class = "nv" > journal< / span > < span class = "o" > ={< / span > arXiv preprint arXiv:1910.10093< span class = "o" > }< / span > ,
< span class = "nv" > year< / span > < span class = "o" > ={< / span > < span class = "m" > 2019< / span > < span class = "o" > }< / span >
< span class = "o" > }< / span >
@inproceedings< span class = "o" > {< / span > zhou2019osnet,
2019-06-05 22:18:45 +01:00
< span class = "nv" > title< / span > < span class = "o" > ={< / span > Omni-Scale Feature Learning < span class = "k" > for< / span > Person Re-Identification< span class = "o" > }< / span > ,
< span class = "nv" > author< / span > < span class = "o" > ={< / span > Zhou, Kaiyang and Yang, Yongxin and Cavallaro, Andrea and Xiang, Tao< span class = "o" > }< / span > ,
2019-10-23 16:12:46 +01:00
< span class = "nv" > booktitle< / span > < span class = "o" > ={< / span > ICCV< span class = "o" > }< / span > ,
< span class = "nv" > year< / span > < span class = "o" > ={< / span > < span class = "m" > 2019< / span > < span class = "o" > }< / span >
< span class = "o" > }< / span >
@article< span class = "o" > {< / span > zhou2019learning,
< span class = "nv" > title< / span > < span class = "o" > ={< / span > Learning Generalisable Omni-Scale Representations < span class = "k" > for< / span > Person Re-Identification< span class = "o" > }< / span > ,
< span class = "nv" > author< / span > < span class = "o" > ={< / span > Zhou, Kaiyang and Yang, Yongxin and Cavallaro, Andrea and Xiang, Tao< span class = "o" > }< / span > ,
< span class = "nv" > journal< / span > < span class = "o" > ={< / span > arXiv preprint arXiv:1910.06827< span class = "o" > }< / span > ,
2019-06-05 22:18:45 +01:00
< span class = "nv" > year< / span > < span class = "o" > ={< / span > < span class = "m" > 2019< / span > < span class = "o" > }< / span >
2019-05-06 10:48:46 +01:00
< span class = "o" > }< / span >
< / pre > < / div >
< / div >
2019-03-24 17:22:43 +00:00
< div class = "toctree-wrapper compound" >
< / div >
< div class = "toctree-wrapper compound" >
< / div >
< div class = "toctree-wrapper compound" >
< / div >
< / div >
< / div >
< div class = "section" id = "indices-and-tables" >
< h1 > Indices and tables< a class = "headerlink" href = "#indices-and-tables" title = "Permalink to this headline" > ¶< / a > < / h1 >
< ul class = "simple" >
2019-11-27 11:10:02 +00:00
< li > < p > < a class = "reference internal" href = "genindex.html" > < span class = "std std-ref" > Index< / span > < / a > < / p > < / li >
< li > < p > < a class = "reference internal" href = "py-modindex.html" > < span class = "std std-ref" > Module Index< / span > < / a > < / p > < / li >
2019-03-24 17:22:43 +00:00
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