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docs/build/html/.buildinfo

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# Sphinx build info version 1
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# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
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config: d4811b84281a8bf73de4e05076720b42
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config: 12ae803fbc9b69a8e785b52f812c9afd
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tags: 645f666f9bcd5a90fca523b33c5a78b7

docs/build/html/_sources/index.rst.txt

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.. [*] Even though this tutorial is based on Windows 10, most steps (excluding the setting of environmental variables) should apply for Linux, too.
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.. toctree::
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:maxdepth: 5
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:maxdepth: 4
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:caption: Contents:
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install

docs/build/html/_sources/install.rst.txt

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# From within TensorFlow/models/research/
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protoc object_detection/protos/*.proto --python_out=.
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If you are on Windows and using Protobuf 3.5 or later, the multi-file selection wildcard (i.e ``*.proto``) will not work but you can do one of the following:
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.. important::
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1. If you are using `Windows Powershell`:
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If you are on Windows and using Protobuf 3.5 or later, the multi-file selection wildcard (i.e ``*.proto``) will not work but you can do one of the following:
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.. code-block:: python
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.. tabs::
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# From within TensorFlow/models/research/
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Get-ChildItem object_detection/protos/*.proto | foreach {protoc "object_detection/protos/$($_.Name)" --python_out=.}
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.. tab:: Windows Powershell
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.. code-block:: python
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2. Alternatively, if you are using `Command Prompt`
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# From within TensorFlow/models/research/
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Get-ChildItem object_detection/protos/*.proto | foreach {protoc "object_detection/protos/$($_.Name)" --python_out=.}
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.. code-block:: python
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# From within TensorFlow/models/research/
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for /f %i in ('dir /b object_detection\protos\*.proto') do protoc object_detection\protos\%i --python_out=.
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.. tab:: Command Prompt
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.. code-block:: python
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# From within TensorFlow/models/research/
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for /f %i in ('dir /b object_detection\protos\*.proto') do protoc object_detection\protos\%i --python_out=.
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.. [#] NOTE: You MUST open a new `Anaconda/Command Prompt` for the changes in the environment variables to take effect.
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1. As ``Tensorflow\models\research\object_detection`` is the core package for object detection, it's convenient to add the specific folder to our environmental variables.
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- Linux: This can be done by either adding to ``~/.bashrc`` or running the following:
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.. tabs::
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.. tab:: Linux
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This can be done by either adding to ``~/.bashrc`` or running the following:
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.. code-block:: bash
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export PYTHONPATH=$PYTHONPATH:<PATH_TO_TF>/TensorFlow/models/research/object_detection
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- Windows: The following folder must be added to your ``PYTHONPATH`` environment variable (See :ref:`set_env`):
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.. tab:: Windows
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The following folder must be added to your ``PYTHONPATH`` environment variable (See :ref:`set_env`):
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- ``<PATH_TO_TF>\TensorFlow\models\research\object_detection``
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- ``<PATH_TO_TF>\TensorFlow\models\research\object_detection``
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.. note:: The above can also be achieved, in both Linux and Windows environments, by running the following from ``Tensorflow\models\research``:
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2. For whatever reason, some of the TensorFlow packages that are required to perform object detection, do not come pre-installed with our tensorflow installation.
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- Linux: The `Installation docs <https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md>`_ suggest that you either run, or add to ``~/.bashrc`` file, the following command, which adds these packages to your PYTHONPATH:
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.. tabs::
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.. tab:: Linux
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The `Installation docs <https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md>`_ suggest that you either run, or add to ``~/.bashrc`` file, the following command, which adds these packages to your PYTHONPATH:
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.. code-block:: bash
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# From within tensorflow/models/research/
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export PYTHONPATH=$PYTHONPATH:<PATH_TO_TF>/TensorFlow/models/research:<PATH_TO_TF>/TensorFlow/models/research/slim
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- Windows: The only way that I found works best, is to simply add the following folders to your ``PYTHONPATH`` environment variable (See also :ref:`set_env`):
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.. tab:: Windows
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The only way that I found works best, is to simply add the following folders to your ``PYTHONPATH`` environment variable (See also :ref:`set_env`):
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- ``<PATH_TO_TF>\TensorFlow\models\research``
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The ``pycocotools`` package should be installed if you are interested in using COCO evaluation metrics.
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1. Windows:
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.. tabs::
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Run the following command to install ``pycocotools`` with Windows support:
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.. tab:: Windows
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.. code-block:: bash
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Run the following command to install ``pycocotools`` with Windows support:
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pip install git+https://github.com/philferriere/cocoapi.git#subdirectory=PythonAPI
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.. code-block:: bash
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pip install git+https://github.com/philferriere/cocoapi.git#subdirectory=PythonAPI
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Note that, according to the `package's instructions <https://github.com/philferriere/cocoapi#this-clones-readme>`_, Visual C++ 2015 build tools must be installed and on your path. If they are not, make sure to install them from `here <https://go.microsoft.com/fwlink/?LinkId=691126>`_.
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Note that, according to the `package's instructions <https://github.com/philferriere/cocoapi#this-clones-readme>`_, Visual C++ 2015 build tools must be installed and on your path. If they are not, make sure to install them from `here <https://go.microsoft.com/fwlink/?LinkId=691126>`_.
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Download `cocoapi <https://github.com/cocodataset/cocoapi>`_ to a directory of your choice, then ``make`` and copy the pycocotools subfolder to the ``Tensorflow/models/research`` directory, as such:
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.. tab:: Linux
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Download `cocoapi <https://github.com/cocodataset/cocoapi>`_ to a directory of your choice, then ``make`` and copy the pycocotools subfolder to the ``Tensorflow/models/research`` directory, as such:
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.. code-block:: bash
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.. code-block:: bash
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git clone https://github.com/cocodataset/cocoapi.git
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cd cocoapi/PythonAPI
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make
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cp -r pycocotools <PATH_TO_TF>/TensorFlow/models/research/
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git clone https://github.com/cocodataset/cocoapi.git
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cd cocoapi/PythonAPI
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make
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cp -r pycocotools <PATH_TO_TF>/TensorFlow/models/research/
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The default metrics are based on those used in Pascal VOC evaluation. To use the COCO object detection metrics add `metrics_set: "coco_detection_metrics"` to the `eval_config` message in the config file. To use the COCO instance segmentation metrics add `metrics_set: "coco_mask_metrics"` to the `eval_config` message in the config file.
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- If the above line is present in the printed debugging, it means that you have not installed the correct version of the cuDNN libraries. In this case make sure you re-do the :ref:`cudnn_install` step, making sure you instal cuDNN v7.0.5.
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.. _labelImg_install:
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LabelImg Installation

docs/build/html/_static/basic.css

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*
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* Sphinx stylesheet -- basic theme.
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*
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* :copyright: Copyright 2007-2018 by the Sphinx team, see AUTHORS.
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* :copyright: Copyright 2007-2019 by the Sphinx team, see AUTHORS.
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* :license: BSD, see LICENSE for details.
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*
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*/
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div.sphinxsidebar #searchbox form.search {
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overflow: hidden;
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}
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div.sphinxsidebar #searchbox input[type="text"] {
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width: 80%;
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hyphens: manual;
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}
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/* -- hlist styles ---------------------------------------------------------- */
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table.hlist td {
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vertical-align: top;
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}
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/* -- other body styles ----------------------------------------------------- */
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ol.arabic {

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