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KaiyangZhou 2019-08-26 13:25:09 +01:00
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README.rst
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@ -18,9 +18,11 @@ It features:
- visualization tools (tensorboard, ranks, etc.)
Code: https://github.com/KaiyangZhou/deep-person-reid.
Documentation: https://kaiyangzhou.github.io/deep-person-reid/.
Code: https://github.com/KaiyangZhou/deep-person-reid.
How-to instructions: https://kaiyangzhou.github.io/deep-person-reid/user_guide.
Model zoo: https://kaiyangzhou.github.io/deep-person-reid/MODEL_ZOO.
@ -129,7 +131,7 @@ A unified interface
-----------------------
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. "configs/" contains some predefined configs which you can use as a starting point.
Below we provide examples to train and test `OSNet <https://arxiv.org/abs/1905.00953>`_. Assume :code:`PATH_TO_DATA` is the directory containing reid datasets.
Below we provide examples 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.
Conventional setting
^^^^^^^^^^^^^^^^^^^^^
@ -158,7 +160,7 @@ The config file sets Market1501 as the default dataset. If you wanna use DukeMTM
--gpu-devices 0 \
data.save_dir log/osnet_x1_0_dukemtmcreid_softmax_cosinelr
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 also find the `tensorboard <https://pytorch.org/docs/stable/tensorboard.html>`_ file. To visualize the learning curves, 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.
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.
Evaluation is automatically performed at the end of training. To run the test again using the trained model, do