.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "build/examples_detection/demo_yolo.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_build_examples_detection_demo_yolo.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_build_examples_detection_demo_yolo.py:

03. Predict with pre-trained YOLO models
==========================================

This article shows how to play with pre-trained YOLO models with only a few
lines of code.

First let's import some necessary libraries:

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.. code-block:: default


    from gluoncv import model_zoo, data, utils
    from matplotlib import pyplot as plt








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Load a pretrained model
-------------------------

Let's get an YOLOv3 model trained with on Pascal VOC
dataset with Darknet53 as the base model. By specifying
``pretrained=True``, it will automatically download the model from the model
zoo if necessary. For more pretrained models, please refer to
:doc:`../../model_zoo/index`.

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.. code-block:: default


    net = model_zoo.get_model('yolo3_darknet53_voc', pretrained=True)





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    Downloading /root/.mxnet/models/yolo3_darknet53_voc-f5ece5ce.zip from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/yolo3_darknet53_voc-f5ece5ce.zip...

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Pre-process an image
--------------------

Next we download an image, and pre-process with preset data transforms. Here we
specify that we resize the short edge of the image to 512 px. You can
feed an arbitrarily sized image.
Once constraint for YOLO is that input height and width can be divided by 32.

You can provide a list of image file names, such as ``[im_fname1, im_fname2,
...]`` to :py:func:`gluoncv.data.transforms.presets.yolo.load_test` if you
want to load multiple image together.

This function returns two results. The first is a NDArray with shape
`(batch_size, RGB_channels, height, width)`. It can be fed into the
model directly. The second one contains the images in numpy format to
easy to be plotted. Since we only loaded a single image, the first dimension
of `x` is 1.

.. GENERATED FROM PYTHON SOURCE LINES 43-50

.. code-block:: default


    im_fname = utils.download('https://raw.githubusercontent.com/zhreshold/' +
                              'mxnet-ssd/master/data/demo/dog.jpg',
                              path='dog.jpg')
    x, img = data.transforms.presets.yolo.load_test(im_fname, short=512)
    print('Shape of pre-processed image:', x.shape)





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    Downloading dog.jpg from https://raw.githubusercontent.com/zhreshold/mxnet-ssd/master/data/demo/dog.jpg...

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    Shape of pre-processed image: (1, 3, 512, 683)




.. GENERATED FROM PYTHON SOURCE LINES 51-61

Inference and display
---------------------

The forward function will return all detected bounding boxes, and the
corresponding predicted class IDs and confidence scores. Their shapes are
`(batch_size, num_bboxes, 1)`, `(batch_size, num_bboxes, 1)`, and
`(batch_size, num_bboxes, 4)`, respectively.

We can use :py:func:`gluoncv.utils.viz.plot_bbox` to visualize the
results. We slice the results for the first image and feed them into `plot_bbox`:

.. GENERATED FROM PYTHON SOURCE LINES 61-67

.. code-block:: default


    class_IDs, scores, bounding_boxs = net(x)

    ax = utils.viz.plot_bbox(img, bounding_boxs[0], scores[0],
                             class_IDs[0], class_names=net.classes)
    plt.show()



.. image-sg:: /build/examples_detection/images/sphx_glr_demo_yolo_001.png
   :alt: demo yolo
   :srcset: /build/examples_detection/images/sphx_glr_demo_yolo_001.png
   :class: sphx-glr-single-img






.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  8.161 seconds)


.. _sphx_glr_download_build_examples_detection_demo_yolo.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: demo_yolo.py <demo_yolo.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: demo_yolo.ipynb <demo_yolo.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_