.. _sphx_glr_build_examples_classification_demo_cifar10.py:

1. Getting Started with Pre-trained Model on CIFAR10
=======================================================

`CIFAR10 <https://www.cs.toronto.edu/~kriz/cifar.html>`__ is a
dataset of tiny (32x32) images with labels, collected by Alex Krizhevsky,
Vinod Nair, and Geoffrey Hinton. It is widely used as benchmark in
computer vision research.

|image-cifar10|

.. |image-cifar10| image:: https://raw.githubusercontent.com/dmlc/web-data/master/gluoncv/datasets/cifar10.png

In this tutorial, we will demonstrate how to load a pre-trained model from ``Gluon Model Zoo``
and classify images from the Internet or your local disk.

Step by Step
------------------

Let's first try out a pre-trained cifar model with a few lines of python code.

First, please follow the `installation guide <../../index.html#installation>`__
to install ``MXNet`` and ``GluonCV`` if you haven't done so yet.



.. code-block:: python


    import matplotlib.pyplot as plt

    from mxnet import gluon, nd, image
    from mxnet.gluon.data.vision import transforms
    from gluoncv import utils
    from gluoncv.model_zoo import get_model







Then, we download and show the example image:



.. code-block:: python


    url = 'https://raw.githubusercontent.com/dmlc/web-data/master/gluoncv/classification/plane-draw.jpeg'
    im_fname = utils.download(url)

    with open(im_fname, 'rb') as f:
        img = image.imdecode(f.read())

    plt.imshow(img.asnumpy())
    plt.show()




.. image:: /build/examples_classification/images/sphx_glr_demo_cifar10_001.png
    :align: center




In case you don't recognize it, the image is a poorly-drawn airplane :)

Now we define transformations for the image.



.. code-block:: python


    transform_fn = transforms.Compose([
        transforms.Resize(32),
        transforms.CenterCrop(32),
        transforms.ToTensor(),
        transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010])
    ])







This transformation function does three things:
resize and crop the image to 32x32 in size,
transpose it to `num_channels*height*width`,
and normalize with mean and standard deviation calculated across all CIFAR10 images.

What does the transformed image look like?



.. code-block:: python


    img = transform_fn(img)
    plt.imshow(nd.transpose(img, (1,2,0)).asnumpy())
    plt.show()




.. image:: /build/examples_classification/images/sphx_glr_demo_cifar10_002.png
    :align: center




Can't recognize anything? *Don't panic!* Neither do I.
The transformation makes it more "model-friendly", instead of "human-friendly".

Next, we load a pre-trained model.



.. code-block:: python


    net = get_model('cifar_resnet110_v2', classes=10, pretrained=True)







Finally, we prepare the image and feed it to the model



.. code-block:: python


    pred = net(img.expand_dims(axis=0))

    class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
                   'dog', 'frog', 'horse', 'ship', 'truck']
    ind = nd.argmax(pred, axis=1).astype('int')
    print('The input picture is classified as [%s], with probability %.3f.'%
          (class_names[ind.asscalar()], nd.softmax(pred)[0][ind].asscalar()))





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

 Out::

    The input picture is classified as [airplane], with probability 0.528.


Play with the scripts
---------------------

Here is a script that does all the previous steps in one go.

:download:`Download demo_cifar10.py<../../../scripts/classification/cifar/demo_cifar10.py>`

Feed in your own image to see how well it does the job.
Keep in mind that ``CIFAR10`` is a small dataset with only 10
classes. Models trained on ``CIFAR10`` only recognize objects from those
10 classes. Thus, it may surprise you if we feed one image to the model
which doesn't belong to any of the 10 classes

For instance we can test it with the following photo of Mt. Baker:

|image-mtbaker|

::

    python demo_cifar10.py --model cifar_resnet110_v2 --input-pic mt_baker.jpg

The result is:

::

    The input picture is classified to be [airplane], with probability 0.857.

Next Step
---------

Congratulations! You’ve just finished reading the first tutorial.
There are a lot more to help you learn GluonCV.

If you would like to dive deeper into training on ``CIFAR10``,
feel free to read the next `tutorial on CIFAR10 <dive_deep_cifar10.html>`__.

Or, if you would like to try a larger scale dataset with 1000 classes of common objects
please read `Getting Started with ImageNet Pre-trained Models <demo_imagenet.html>`__.

.. |image-mtbaker| image:: https://raw.githubusercontent.com/dmlc/web-data/master/gluoncv/classification/mt_baker.jpg


**Total running time of the script:** ( 0 minutes  0.236 seconds)



.. only :: html

 .. container:: sphx-glr-footer


  .. container:: sphx-glr-download

     :download:`Download Python source code: demo_cifar10.py <demo_cifar10.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: demo_cifar10.ipynb <demo_cifar10.ipynb>`


.. only:: html

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

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