models.rst

torchvision.models
##################


The models subpackage contains definitions of models for addressing
different tasks, including: image classification, pixelwise semantic
segmentation, object detection, instance segmentation and person
keypoint detection.


Classification
==============

The models subpackage contains definitions for the following model
architectures for image classification:

-  `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_v2_x0_5
.. autofunction:: shufflenet_v2_x1_0
.. autofunction:: shufflenet_v2_x1_5
.. autofunction:: shufflenet_v2_x2_0

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

.. autofunction:: mobilenet_v2

ResNext
-------

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


Semantic Segmentation
=====================

The models subpackage contains definitions for the following model
architectures for semantic segmentation:

- `FCN ResNet101 <https://arxiv.org/abs/1411.4038>`_
- `DeepLabV3 ResNet101 <https://arxiv.org/abs/1706.05587>`_

As with image classification models, all pre-trained models expect input images normalized in the same way.
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]``.
They have been trained on images resized such that their minimum size is 520.

The pre-trained models have been trained on a subset of COCO train2017, on the 20 categories that are
present in the Pascal VOC dataset. You can see more information on how the subset has been selected in
``references/segmentation/coco_utils.py``. The classes that the pre-trained model outputs are the following,
in order:

  .. code-block:: python

      ['__background__', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus',
       'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike',
       'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor']

The accuracies of the pre-trained models evaluated on COCO val2017 are as follows

================================  =============  ====================
Network                           mean IoU       global pixelwise acc
================================  =============  ====================
FCN ResNet101                     63.7           91.9
DeepLabV3 ResNet101               67.4           92.4
================================  =============  ====================


Fully Convolutional Networks
----------------------------

.. autofunction:: torchvision.models.segmentation.fcn_resnet50
.. autofunction:: torchvision.models.segmentation.fcn_resnet101


DeepLabV3
---------

.. autofunction:: torchvision.models.segmentation.deeplabv3_resnet50
.. autofunction:: torchvision.models.segmentation.deeplabv3_resnet101


Object Detection, Instance Segmentation and Person Keypoint Detection
=====================================================================

The models subpackage contains definitions for the following model
architectures for detection:

- `Faster R-CNN ResNet-50 FPN <https://arxiv.org/abs/1506.01497>`_
- `Mask R-CNN ResNet-50 FPN <https://arxiv.org/abs/1703.06870>`_

The pre-trained models for detection, instance segmentation and
keypoint detection are initialized with the classification models
in torchvision.

The models expect a list of ``Tensor[C, H, W]``, in the range ``0-1``.
The models internally resize the images so that they have a minimum size
of ``800``. This option can be changed by passing the option ``min_size``
to the constructor of the models.


For object detection and instance segmentation, the pre-trained
models return the predictions of the following classes:

  .. code-block:: python

      COCO_INSTANCE_CATEGORY_NAMES = [
          '__background__', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
          'train', 'truck', 'boat', 'traffic', 'light', 'fire', 'hydrant', 'N/A', 'stop',
          'sign', 'parking', 'meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
          'elephant', 'bear', 'zebra', 'giraffe', 'N/A', 'backpack', 'umbrella', 'N/A', 'N/A',
          'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports', 'ball',
          'kite', 'baseball', 'bat', 'baseball', 'glove', 'skateboard', 'surfboard', 'tennis',
          'racket', 'bottle', 'N/A', 'wine', 'glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
          'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot', 'dog', 'pizza',
          'donut', 'cake', 'chair', 'couch', 'potted', 'plant', 'bed', 'N/A', 'dining', 'table',
          'N/A', 'N/A', 'toilet', 'N/A', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell',
          'phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'N/A', 'book',
          'clock', 'vase', 'scissors', 'teddy', 'bear', 'hair', 'drier', 'toothbrush'
      ]


Here are the summary of the accuracies for the models trained on
the instances set of COCO train2017 and evaluated on COCO val2017.

================================  =======  ========  ===========
Network                           box AP   mask AP   keypoint AP
================================  =======  ========  ===========
Faster R-CNN ResNet-50 FPN        37.0     -         -
Mask R-CNN ResNet-50 FPN          37.9     34.6      -
================================  =======  ========  ===========

For person keypoint detection, the accuracies for the pre-trained
models are as follows

================================  =======  ========  ===========
Network                           box AP   mask AP   keypoint AP
================================  =======  ========  ===========
Keypoint R-CNN ResNet-50 FPN      54.6     -         65.0
================================  =======  ========  ===========

For person keypoint detection, the pre-trained model return the
keypoints in the following order:

  .. code-block:: python

    COCO_PERSON_KEYPOINT_NAMES = [
        'nose',
        'left_eye',
        'right_eye',
        'left_ear',
        'right_ear',
        'left_shoulder',
        'right_shoulder',
        'left_elbow',
        'right_elbow',
        'left_wrist',
        'right_wrist',
        'left_hip',
        'right_hip',
        'left_knee',
        'right_knee',
        'left_ankle',
        'right_ankle'
    ]

Runtime characteristics
-----------------------

The implementations of the models for object detection, instance segmentation
and keypoint detection are efficient.

In the following table, we use 8 V100 GPUs, with CUDA 10.0 and CUDNN 7.4 to
report the results. During training, we use a batch size of 2 per GPU, and
during testing a batch size of 1 is used.

For test time, we report the time for the model evaluation and postprocessing
(including mask pasting in image), but not the time for computing the
precision-recall.

==============================  ===================  ==================  ===========
Network                         train time (s / it)  test time (s / it)  memory (GB)
==============================  ===================  ==================  ===========
Faster R-CNN ResNet-50 FPN      0.2288               0.0590              5.2
Mask R-CNN ResNet-50 FPN        0.2728               0.0903              5.4
Keypoint R-CNN ResNet-50 FPN    0.3789               0.1242              6.8
==============================  ===================  ==================  ===========


Faster R-CNN
------------

.. autofunction:: torchvision.models.detection.fasterrcnn_resnet50_fpn


Mask R-CNN
----------

.. autofunction:: torchvision.models.detection.maskrcnn_resnet50_fpn


Keypoint R-CNN
--------------

.. autofunction:: torchvision.models.detection.keypointrcnn_resnet50_fpn