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


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


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

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

-  `AlexNet`_
-  `VGG`_
-  `ResNet`_
-  `SqueezeNet`_
-  `DenseNet`_
-  `Inception`_ v3
-  `GoogLeNet`_
-  `ShuffleNet`_ v2
-  `MobileNetV2`_
-  `MobileNetV3`_
-  `ResNeXt`_
-  `Wide ResNet`_
-  `MNASNet`_

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_v2 = models.mobilenet_v2()
    mobilenet_v3_large = models.mobilenet_v3_large()
    mobilenet_v3_small = models.mobilenet_v3_small()
    resnext50_32x4d = models.resnext50_32x4d()
    wide_resnet50_2 = models.wide_resnet50_2()
    mnasnet = models.mnasnet1_0()

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_v2 = models.mobilenet_v2(pretrained=True)
    mobilenet_v3_large = models.mobilenet_v3_large(pretrained=True)
    resnext50_32x4d = models.resnext50_32x4d(pretrained=True)
    wide_resnet50_2 = models.wide_resnet50_2(pretrained=True)
    mnasnet = models.mnasnet1_0(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>`_

The process for obtaining the values of `mean` and `std` is roughly equivalent
to::

    import torch
    from torchvision import datasets, transforms as T

    transform = T.Compose([T.Resize(256), T.CenterCrop(224), T.ToTensor()])
    dataset = datasets.ImageNet(".", split="train", transform=transform)

    means = []
    stds = []
    for img in subset(dataset):
        means.append(torch.mean(img))
        stds.append(torch.std(img))

    mean = torch.mean(torch.tensor(means))
    std = torch.mean(torch.tensor(stds))

Unfortunately, the concrete `subset` that was used is lost. For more
information see `this discussion <https://github.com/pytorch/vision/issues/1439>`_
or `these experiments <https://github.com/pytorch/vision/pull/1965>`_.

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
MobileNet V3 Large                25.96           8.66
ResNeXt-50-32x4d                  22.38           6.30
ResNeXt-101-32x8d                 20.69           5.47
Wide ResNet-50-2                  21.49           5.91
Wide ResNet-101-2                 21.16           5.72
MNASNet 1.0                       26.49           8.456
================================  =============   =============


.. _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
.. _MobileNetV2: https://arxiv.org/abs/1801.04381
.. _MobileNetV3: https://arxiv.org/abs/1905.02244
.. _ResNeXt: https://arxiv.org/abs/1611.05431
.. _MNASNet: https://arxiv.org/abs/1807.11626

.. 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

.. note ::
    This requires `scipy` to be installed


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

.. autofunction:: googlenet

.. note ::
    This requires `scipy` to be installed


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

MobileNet v3
-------------

.. autofunction:: mobilenet_v3_large
.. autofunction:: mobilenet_v3_small

ResNext
-------

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

Wide ResNet
-----------

.. autofunction:: wide_resnet50_2
.. autofunction:: wide_resnet101_2

MNASNet
--------

.. autofunction:: mnasnet0_5
.. autofunction:: mnasnet0_75
.. autofunction:: mnasnet1_0
.. autofunction:: mnasnet1_3

Quantized Models
----------------

The following architectures provide support for INT8 quantized models. You can get
a model with random weights by calling its constructor:

.. code:: python

    import torchvision.models as models
    googlenet = models.quantization.googlenet()
    inception_v3 = models.quantization.inception_v3()
    mobilenet_v2 = models.quantization.mobilenet_v2()
    mobilenet_v3_large = models.quantization.mobilenet_v3_large()
    mobilenet_v3_small = models.quantization.mobilenet_v3_small()
    resnet18 = models.quantization.resnet18()
    resnet50 = models.quantization.resnet50()
    resnext101_32x8d = models.quantization.resnext101_32x8d()
    shufflenet_v2_x0_5 = models.quantization.shufflenet_v2_x0_5()
    shufflenet_v2_x1_0 = models.quantization.shufflenet_v2_x1_0()
    shufflenet_v2_x1_5 = models.quantization.shufflenet_v2_x1_5()
    shufflenet_v2_x2_0 = models.quantization.shufflenet_v2_x2_0()

Obtaining a pre-trained quantized model can be done with a few lines of code:

.. code:: python

    import torchvision.models as models
    model = models.quantization.mobilenet_v2(pretrained=True, quantize=True)
    model.eval()
    # run the model with quantized inputs and weights
    out = model(torch.rand(1, 3, 224, 224))

We provide pre-trained quantized weights for the following models:

================================  =============  =============
Model                             Acc@1          Acc@5
================================  =============  =============
MobileNet V2                      71.658         90.150
MobileNet V3 Large                73.004         90.858
ShuffleNet V2                     68.360         87.582
ResNet 18                         69.494         88.882
ResNet 50                         75.920         92.814
ResNext 101 32x8d                 78.986         94.480
Inception V3                      77.176         93.354
GoogleNet                         69.826         89.404
================================  =============  =============


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

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

- `FCN ResNet50, ResNet101 <https://arxiv.org/abs/1411.4038>`_
- `DeepLabV3 ResNet50, ResNet101, MobileNetV3-Large <https://arxiv.org/abs/1706.05587>`_
- `LR-ASPP MobileNetV3-Large <https://arxiv.org/abs/1905.02244>`_

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 ResNet50                      60.5           91.4
FCN ResNet101                     63.7           91.9
DeepLabV3 ResNet50                66.4           92.4
DeepLabV3 ResNet101               67.4           92.4
DeepLabV3 MobileNetV3-Large       60.3           91.2
LR-ASPP MobileNetV3-Large         57.9           91.2
================================  =============  ====================


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
.. autofunction:: torchvision.models.segmentation.deeplabv3_mobilenet_v3_large


LR-ASPP
-------

.. autofunction:: torchvision.models.segmentation.lraspp_mobilenet_v3_large


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     -         -
Faster R-CNN MobileNetV3-Large FPN      32.8     -         -
Faster R-CNN MobileNetV3-Large 320 FPN  22.8     -         -
RetinaNet ResNet-50 FPN                 36.4     -         -
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
Faster R-CNN MobileNetV3-Large FPN      0.1020               0.0415              1.0
Faster R-CNN MobileNetV3-Large 320 FPN  0.0978               0.0376              0.6
RetinaNet ResNet-50 FPN                 0.2514               0.0939              4.1
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
.. autofunction:: torchvision.models.detection.fasterrcnn_mobilenet_v3_large_fpn
.. autofunction:: torchvision.models.detection.fasterrcnn_mobilenet_v3_large_320_fpn


RetinaNet
------------

.. autofunction:: torchvision.models.detection.retinanet_resnet50_fpn


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

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


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

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


Video classification
====================

We provide models for action recognition pre-trained on Kinetics-400.
They have all been trained with the scripts provided in ``references/video_classification``.

All pre-trained models expect input images normalized in the same way,
i.e. mini-batches of 3-channel RGB videos of shape (3 x T x H x W),
where H and W are expected to be 112, and T is a number of video frames in a clip.
The images have to be loaded in to a range of [0, 1] and then normalized
using ``mean = [0.43216, 0.394666, 0.37645]`` and ``std = [0.22803, 0.22145, 0.216989]``.


.. note::
  The normalization parameters are different from the image classification ones, and correspond
  to the mean and std from Kinetics-400.

.. note::
  For now, normalization code can be found in ``references/video_classification/transforms.py``,
  see the ``Normalize`` function there. Note that it differs from standard normalization for
  images because it assumes the video is 4d.

Kinetics 1-crop accuracies for clip length 16 (16x112x112)

================================  =============   =============
Network                           Clip acc@1      Clip acc@5
================================  =============   =============
ResNet 3D 18                      52.75           75.45
ResNet MC 18                      53.90           76.29
ResNet (2+1)D                     57.50           78.81
================================  =============   =============


ResNet 3D
----------

.. autofunction:: torchvision.models.video.r3d_18

ResNet Mixed Convolution
------------------------

.. autofunction:: torchvision.models.video.mc3_18

ResNet (2+1)D
-------------

.. autofunction:: torchvision.models.video.r2plus1d_18
