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Overview of Open-ReID
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Open Re-ID is a lightweight library of person re-identification for research
purpose. It aims to provide a uniform interface for different datasets, a full
set of models and evaluation metrics, as well as examples to reproduce (near)
state-of-the-art results. Open-ReID is mainly based on `PyTorch
<http://pytorch.org/>`_.

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Structure
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Open-ReID is structured into three levels, as shown in the figure below.

.. _fig-structure:
.. figure:: /figures/structure.png

API Level
   At bottom, there are decoupled modules each providing unit functions. For
   example, the ``datasets`` module has a uniform interface for many popular
   datasets, while commonly used evaluation metrics, such as CMC and mean AP are
   implemented in the ``evaluation_metrics`` module, which accept both
   ``torch.Tensor`` and ``numpy.ndarray`` as inputs.

SDK Level
   In the middle, several classes interact with underlying APIs to provide
   routines for standard tasks. For example, the ``Trainer`` can be used to
   train a deep model on training set, and ``Evaluator`` can evaluate the model
   on validation and test sets.

Application Level
   At top, we provide several examples using Open-ReID. For example, one can
   easily train a CNN with different kinds of loss functions on different
   datasets, to achieve certain baselines or state-of-the-art results.