torchvision.models
==================

The models subpackage contains definitions for the following model
architectures:

-  `AlexNet`_
-  `VGG`_
-  `ResNet`_
-  `SqueezeNet`_
-  `DenseNet`_
-  `Inception`_ v3
-  `GoogLeNet`_
-  `ShuffleNet`_ v2
-  `MobileNet`_ v2
-  `ResNeXt`_

You can construct a model with random weights by calling its constructor:

.. code:: python

    import torchvision.models as models
    resnet18 = models.resnet18()
    alexnet = models.alexnet()
    vgg16 = models.vgg16()
    squeezenet = models.squeezenet1_0()
    densenet = models.densenet161()
    inception = models.inception_v3()
    googlenet = models.googlenet()
    shufflenet = models.shufflenet_v2_x1_0()
    mobilenet = models.mobilenet_v2()
    resnext50_32x4d = models.resnext50_32x4d()

We provide pre-trained models, using the PyTorch :mod:`torch.utils.model_zoo`.
These can be constructed by passing ``pretrained=True``:

.. code:: python

    import torchvision.models as models
    resnet18 = models.resnet18(pretrained=True)
    alexnet = models.alexnet(pretrained=True)
    squeezenet = models.squeezenet1_0(pretrained=True)
    vgg16 = models.vgg16(pretrained=True)
    densenet = models.densenet161(pretrained=True)
    inception = models.inception_v3(pretrained=True)
    googlenet = models.googlenet(pretrained=True)
    shufflenet = models.shufflenet_v2_x1_0(pretrained=True)
    mobilenet = models.mobilenet_v2(pretrained=True)
    resnext50_32x4d = models.resnext50_32x4d(pretrained=True)

Instancing a pre-trained model will download its weights to a cache directory.
This directory can be set using the `TORCH_MODEL_ZOO` environment variable. See
:func:`torch.utils.model_zoo.load_url` for details.

Some models use modules which have different training and evaluation
behavior, such as batch normalization. To switch between these modes, use
``model.train()`` or ``model.eval()`` as appropriate. See
:meth:`~torch.nn.Module.train` or :meth:`~torch.nn.Module.eval` for details. 

All pre-trained models expect input images normalized in the same way,
i.e. mini-batches of 3-channel RGB images of shape (3 x H x W),
where H and W are expected to be at least 224.
The images have to be loaded in to a range of [0, 1] and then normalized
using ``mean = [0.485, 0.456, 0.406]`` and ``std = [0.229, 0.224, 0.225]``.
You can use the following transform to normalize::

    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                     std=[0.229, 0.224, 0.225])

An example of such normalization can be found in the imagenet example
`here <https://github.com/pytorch/examples/blob/42e5b996718797e45c46a25c55b031e6768f8440/imagenet/main.py#L89-L101>`_

ImageNet 1-crop error rates (224x224)

================================  =============   =============
Network                           Top-1 error     Top-5 error
================================  =============   =============
AlexNet                           43.45           20.91
VGG-11                            30.98           11.37
VGG-13                            30.07           10.75
VGG-16                            28.41           9.62
VGG-19                            27.62           9.12
VGG-11 with batch normalization   29.62           10.19
VGG-13 with batch normalization   28.45           9.63
VGG-16 with batch normalization   26.63           8.50
VGG-19 with batch normalization   25.76           8.15
ResNet-18                         30.24           10.92
ResNet-34                         26.70           8.58
ResNet-50                         23.85           7.13
ResNet-101                        22.63           6.44
ResNet-152                        21.69           5.94
SqueezeNet 1.0                    41.90           19.58
SqueezeNet 1.1                    41.81           19.38
Densenet-121                      25.35           7.83
Densenet-169                      24.00           7.00
Densenet-201                      22.80           6.43
Densenet-161                      22.35           6.20
Inception v3                      22.55           6.44
GoogleNet                         30.22           10.47
ShuffleNet V2                     30.64           11.68
MobileNet V2                      28.12           9.71
ResNeXt-50-32x4d                  22.38           6.30
ResNeXt-101-32x8d                 20.69           5.47
================================  =============   =============


.. _AlexNet: https://arxiv.org/abs/1404.5997
.. _VGG: https://arxiv.org/abs/1409.1556
.. _ResNet: https://arxiv.org/abs/1512.03385
.. _SqueezeNet: https://arxiv.org/abs/1602.07360
.. _DenseNet: https://arxiv.org/abs/1608.06993
.. _Inception: https://arxiv.org/abs/1512.00567
.. _GoogLeNet: https://arxiv.org/abs/1409.4842
.. _ShuffleNet: https://arxiv.org/abs/1807.11164
.. _MobileNet: https://arxiv.org/abs/1801.04381
.. _ResNeXt: https://arxiv.org/abs/1611.05431

.. currentmodule:: torchvision.models

Alexnet
-------

.. autofunction:: alexnet

VGG
---

.. autofunction:: vgg11
.. autofunction:: vgg11_bn
.. autofunction:: vgg13
.. autofunction:: vgg13_bn
.. autofunction:: vgg16
.. autofunction:: vgg16_bn
.. autofunction:: vgg19
.. autofunction:: vgg19_bn


ResNet
------

.. autofunction:: resnet18
.. autofunction:: resnet34
.. autofunction:: resnet50
.. autofunction:: resnet101
.. autofunction:: resnet152

SqueezeNet
----------

.. autofunction:: squeezenet1_0
.. autofunction:: squeezenet1_1

DenseNet
---------

.. autofunction:: densenet121
.. autofunction:: densenet169
.. autofunction:: densenet161
.. autofunction:: densenet201

Inception v3
------------

.. autofunction:: inception_v3

GoogLeNet
------------

.. autofunction:: googlenet

ShuffleNet v2
-------------

.. autofunction:: shufflenet

MobileNet v2
-------------

.. autofunction:: mobilenet_v2

ResNext
-------------

.. autofunction:: resnext50_32x4d
.. autofunction:: resnext101_32x8d

