Model doc revamp - first PR (#5821)

* First PR for model doc revamp

* Deactivating fail on warning, temporarily

* Remove commnet

* Minor changes

* Typos

* Added TODO in Makefile

* Keep old models.rst file intact, move new docs into new models_new.rst file

.gitignore

@@ -15,6 +15,7 @@ docs/build
 docs/source/auto_examples/
 docs/source/gen_modules/
 docs/source/generated/
+docs/source/models/generated/
 # pytorch-sphinx-theme gets installed here
 docs/src
 

docs/Makefile

@@ -6,7 +6,9 @@ ifneq ($(EXAMPLES_PATTERN),)
 endif
 
 # You can set these variables from the command line.
-SPHINXOPTS    = -W -j auto $(EXAMPLES_PATTERN_OPTS)
+# TODO: Once the models doc revamp is done, set back the -W option to raise
+# errors on warnings. See https://github.com/pytorch/vision/pull/5821#discussion_r850500693
+SPHINXOPTS    = -j auto $(EXAMPLES_PATTERN_OPTS)
 SPHINXBUILD   = sphinx-build
 SPHINXPROJ    = torchvision
 SOURCEDIR     = source

docs/requirements.txt

@@ -3,6 +3,7 @@ numpy
 sphinx-copybutton>=0.3.1
 sphinx-gallery>=0.9.0
 sphinx==3.5.4
+tabulate
 # This pin is only needed for sphinx<4.0.2. See https://github.com/pytorch/vision/issues/5673 for details
 Jinja2<3.1.*
 -e git+https://github.com/pytorch/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme

docs/source/conf.py

@@ -21,9 +21,13 @@
 # sys.path.insert(0, os.path.abspath('.'))
 
 import os
+import textwrap
+from pathlib import Path
 
 import pytorch_sphinx_theme
 import torchvision
+import torchvision.models as M
+from tabulate import tabulate
 
 
 # -- General configuration ------------------------------------------------
@@ -292,5 +296,57 @@ def inject_minigalleries(app, what, name, obj, options, lines):
         lines.append("\n")
 
 
+def inject_weight_metadata(app, what, name, obj, options, lines):
+
+    if obj.__name__.endswith("_Weights"):
+        lines[:] = ["The model builder above accepts the following values as the ``weights`` parameter:"]
+        lines.append("")
+        for field in obj:
+            lines += [f"**{str(field)}**:", ""]
+
+            table = []
+            for k, v in field.meta.items():
+                if k == "categories":
+                    continue
+                elif k == "recipe":
+                    v = f"`link <{v}>`__"
+                table.append((str(k), str(v)))
+            table = tabulate(table, tablefmt="rst")
+            lines += [".. table::", ""]
+            lines += textwrap.indent(table, " " * 4).split("\n")
+            lines.append("")
+
+
+def generate_classification_table():
+
+    weight_enums = [getattr(M, name) for name in dir(M) if name.endswith("_Weights")]
+    weights = [w for weight_enum in weight_enums for w in weight_enum]
+
+    column_names = ("**Weight**", "**Acc@1**", "**Acc@5**", "**Params**", "**Recipe**")
+    content = [
+        (
+            f":class:`{w} <{type(w).__name__}>`",
+            w.meta["acc@1"],
+            w.meta["acc@5"],
+            f"{w.meta['num_params']/1e6:.1f}M",
+            f"`link <{w.meta['recipe']}>`__",
+        )
+        for w in weights
+    ]
+    table = tabulate(content, headers=column_names, tablefmt="rst")
+
+    generated_dir = Path("generated")
+    generated_dir.mkdir(exist_ok=True)
+    with open(generated_dir / "classification_table.rst", "w+") as table_file:
+        table_file.write(".. table::\n")
+        table_file.write("    :widths: 100 10 10 20 10\n\n")
+        table_file.write(f"{textwrap.indent(table, ' ' * 4)}\n\n")
+
+
+generate_classification_table()
+
+
 def setup(app):
+
     app.connect("autodoc-process-docstring", inject_minigalleries)
+    app.connect("autodoc-process-docstring", inject_weight_metadata)

docs/source/index.rst

@@ -38,6 +38,7 @@ architectures, and common image transformations for computer vision.
    ops
    io
    feature_extraction
+   models_new
 
 .. toctree::
    :maxdepth: 1

docs/source/models/resnet.rst

+ResNet
+======
+
+.. currentmodule:: torchvision.models
+
+The ResNet model is based on the `Deep Residual Learning for Image Recognition
+<https://arxiv.org/abs/1512.03385>`_ paper.
+
+
+Model builders
+--------------
+
+The following model builders can be used to instanciate a ResNet model, with or
+without pre-trained weights. All the model builders internally rely on the
+``torchvision.models.resnet.ResNet`` base class. Please refer to the `source
+code
+<https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py>`_ for
+more details about this class.
+
+.. autosummary::
+    :toctree: generated/
+    :template: function.rst
+
+    resnet18
+    resnet34
+    resnet50
+    resnet101
+    resnet152

docs/source/models/vgg.rst

+VGG
+===
+
+.. currentmodule:: torchvision.models
+
+The VGG model is based on the `Very Deep Convolutional Networks for Large-Scale
+Image Recognition <https://arxiv.org/abs/1409.1556>`_ paper.
+
+
+Model builders
+--------------
+
+The following model builders can be used to instanciate a VGG model, with or
+without pre-trained weights. All the model buidlers internally rely on the
+``torchvision.models.vgg.VGG`` base class. Please refer to the `source code
+<https://github.com/pytorch/vision/blob/main/torchvision/models/vgg.py>`_ for
+more details about this class.
+
+.. autosummary::
+    :toctree: generated/
+    :template: function.rst
+
+    vgg11
+    vgg11_bn
+    vgg13
+    vgg13_bn
+    vgg16
+    vgg16_bn
+    vgg19
+    vgg19_bn

docs/source/models_new.rst

+.. _models_new:
+
+Models and pre-trained weights - New
+####################################
+
+.. note::
+
+    These are the new models docs, documenting the new multi-weight API.
+    TODO: Once all is done, remove the "- New" part in the title above, and
+    rename this file as models.rst
+
+
+The ``torchvision.models`` subpackage contains definitions of models for addressing
+different tasks, including: image classification, pixelwise semantic
+segmentation, object detection, instance segmentation, person
+keypoint detection, video classification, and optical flow.
+
+.. note ::
+    Backward compatibility is guaranteed for loading a serialized 
+    ``state_dict`` to the model created using old PyTorch version. 
+    On the contrary, loading entire saved models or serialized 
+    ``ScriptModules`` (seralized using older versions of PyTorch) 
+    may not preserve the historic behaviour. Refer to the following 
+    `documentation 
+    <https://pytorch.org/docs/stable/notes/serialization.html#id6>`_   
+
+
+Classification
+==============
+
+.. currentmodule:: torchvision.models
+
+The following classification models are available, with or without pre-trained
+weights:
+
+.. toctree::
+   :maxdepth: 1
+
+   models/resnet
+   models/vgg
+
+
+Table of all available classification weights
+---------------------------------------------
+
+Accuracies are reported on ImageNet
+
+.. include:: generated/classification_table.rst
+
+
+Object Detection, Instance Segmentation and Person Keypoint Detection
+=====================================================================
+
+TODO: Something similar to classification models: list of models + table of weights

torchvision/models/resnet.py

@@ -556,12 +556,23 @@ class Wide_ResNet101_2_Weights(WeightsEnum):
 
 @handle_legacy_interface(weights=("pretrained", ResNet18_Weights.IMAGENET1K_V1))
 def resnet18(*, weights: Optional[ResNet18_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet:
-    r"""ResNet-18 model from
-    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
+    """ResNet-18 from `Deep Residual Learning for Image Recognition <https://arxiv.org/pdf/1512.03385.pdf>`__.
 
     Args:
-        weights (ResNet18_Weights, optional): The pretrained weights for the model
-        progress (bool): If True, displays a progress bar of the download to stderr
+        weights (:class:`~torchvision.models.ResNet18_Weights`, optional): The
+            pretrained weights to use. See
+            :class:`~torchvision.models.ResNet18_Weights` below for
+            more details, and possible values. By default, no pre-trained
+            weights are used.
+        progress (bool, optional): If True, displays a progress bar of the
+            download to stderr. Default is True.
+        **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet``
+            base class. Please refer to the `source code
+            <https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py>`_
+            for more details about this class.
+
+    .. autoclass:: torchvision.models.ResNet18_Weights
+        :members:
     """
     weights = ResNet18_Weights.verify(weights)
 
@@ -570,12 +581,23 @@ def resnet18(*, weights: Optional[ResNet18_Weights] = None, progress: bool = Tru
 
 @handle_legacy_interface(weights=("pretrained", ResNet34_Weights.IMAGENET1K_V1))
 def resnet34(*, weights: Optional[ResNet34_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet:
-    r"""ResNet-34 model from
-    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
+    """ResNet-34 from `Deep Residual Learning for Image Recognition <https://arxiv.org/pdf/1512.03385.pdf>`__.
 
     Args:
-        weights (ResNet34_Weights, optional): The pretrained weights for the model
-        progress (bool): If True, displays a progress bar of the download to stderr
+        weights (:class:`~torchvision.models.ResNet34_Weights`, optional): The
+            pretrained weights to use. See
+            :class:`~torchvision.models.ResNet34_Weights` below for
+            more details, and possible values. By default, no pre-trained
+            weights are used.
+        progress (bool, optional): If True, displays a progress bar of the
+            download to stderr. Default is True.
+        **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet``
+            base class. Please refer to the `source code
+            <https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py>`_
+            for more details about this class.
+
+    .. autoclass:: torchvision.models.ResNet34_Weights
+        :members:
     """
     weights = ResNet34_Weights.verify(weights)
 
@@ -584,12 +606,23 @@ def resnet34(*, weights: Optional[ResNet34_Weights] = None, progress: bool = Tru
 
 @handle_legacy_interface(weights=("pretrained", ResNet50_Weights.IMAGENET1K_V1))
 def resnet50(*, weights: Optional[ResNet50_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet:
-    r"""ResNet-50 model from
-    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
+    """ResNet-50 from `Deep Residual Learning for Image Recognition <https://arxiv.org/pdf/1512.03385.pdf>`__.
 
     Args:
-        weights (ResNet50_Weights, optional): The pretrained weights for the model
-        progress (bool): If True, displays a progress bar of the download to stderr
+        weights (:class:`~torchvision.models.ResNet50_Weights`, optional): The
+            pretrained weights to use. See
+            :class:`~torchvision.models.ResNet50_Weights` below for
+            more details, and possible values. By default, no pre-trained
+            weights are used.
+        progress (bool, optional): If True, displays a progress bar of the
+            download to stderr. Default is True.
+        **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet``
+            base class. Please refer to the `source code
+            <https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py>`_
+            for more details about this class.
+
+    .. autoclass:: torchvision.models.ResNet50_Weights
+        :members:
     """
     weights = ResNet50_Weights.verify(weights)
 
@@ -598,12 +631,23 @@ def resnet50(*, weights: Optional[ResNet50_Weights] = None, progress: bool = Tru
 
 @handle_legacy_interface(weights=("pretrained", ResNet101_Weights.IMAGENET1K_V1))
 def resnet101(*, weights: Optional[ResNet101_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet:
-    r"""ResNet-101 model from
-    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
+    """ResNet-101 from `Deep Residual Learning for Image Recognition <https://arxiv.org/pdf/1512.03385.pdf>`__.
 
     Args:
-        weights (ResNet101_Weights, optional): The pretrained weights for the model
-        progress (bool): If True, displays a progress bar of the download to stderr
+        weights (:class:`~torchvision.models.ResNet101_Weights`, optional): The
+            pretrained weights to use. See
+            :class:`~torchvision.models.ResNet101_Weights` below for
+            more details, and possible values. By default, no pre-trained
+            weights are used.
+        progress (bool, optional): If True, displays a progress bar of the
+            download to stderr. Default is True.
+        **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet``
+            base class. Please refer to the `source code
+            <https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py>`_
+            for more details about this class.
+
+    .. autoclass:: torchvision.models.ResNet101_Weights
+        :members:
     """
     weights = ResNet101_Weights.verify(weights)
 
@@ -612,12 +656,23 @@ def resnet101(*, weights: Optional[ResNet101_Weights] = None, progress: bool = T
 
 @handle_legacy_interface(weights=("pretrained", ResNet152_Weights.IMAGENET1K_V1))
 def resnet152(*, weights: Optional[ResNet152_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet:
-    r"""ResNet-152 model from
-    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
+    """ResNet-152 from `Deep Residual Learning for Image Recognition <https://arxiv.org/pdf/1512.03385.pdf>`__.
 
     Args:
-        weights (ResNet152_Weights, optional): The pretrained weights for the model
-        progress (bool): If True, displays a progress bar of the download to stderr
+        weights (:class:`~torchvision.models.ResNet152_Weights`, optional): The
+            pretrained weights to use. See
+            :class:`~torchvision.models.ResNet152_Weights` below for
+            more details, and possible values. By default, no pre-trained
+            weights are used.
+        progress (bool, optional): If True, displays a progress bar of the
+            download to stderr. Default is True.
+        **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet``
+            base class. Please refer to the `source code
+            <https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py>`_
+            for more details about this class.
+
+    .. autoclass:: torchvision.models.ResNet152_Weights
+        :members:
     """
     weights = ResNet152_Weights.verify(weights)
 

torchvision/models/vgg.py

@@ -252,13 +252,23 @@ class VGG19_BN_Weights(WeightsEnum):
 
 @handle_legacy_interface(weights=("pretrained", VGG11_Weights.IMAGENET1K_V1))
 def vgg11(*, weights: Optional[VGG11_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
-    r"""VGG 11-layer model (configuration "A") from
-    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_.
-    The required minimum input size of the model is 32x32.
+    """VGG-11 from `Very Deep Convolutional Networks for Large-Scale Image Recognition <https://arxiv.org/abs/1409.1556>`__.
 
     Args:
-        weights (VGG11_Weights, optional): The pretrained weights for the model
-        progress (bool): If True, displays a progress bar of the download to stderr
+        weights (:class:`~torchvision.models.VGG11_Weights`, optional): The
+            pretrained weights to use. See
+            :class:`~torchvision.models.VGG11_Weights` below for
+            more details, and possible values. By default, no pre-trained
+            weights are used.
+        progress (bool, optional): If True, displays a progress bar of the
+            download to stderr. Default is True.
+        **kwargs: parameters passed to the ``torchvision.models.vgg.VGG``
+            base class. Please refer to the `source code
+            <https://github.com/pytorch/vision/blob/main/torchvision/models/vgg.py>`_
+            for more details about this class.
+
+    .. autoclass:: torchvision.models.VGG11_Weights
+        :members:
     """
     weights = VGG11_Weights.verify(weights)
 
@@ -267,13 +277,23 @@ def vgg11(*, weights: Optional[VGG11_Weights] = None, progress: bool = True, **k
 
 @handle_legacy_interface(weights=("pretrained", VGG11_BN_Weights.IMAGENET1K_V1))
 def vgg11_bn(*, weights: Optional[VGG11_BN_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
-    r"""VGG 11-layer model (configuration "A") with batch normalization
-    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_.
-    The required minimum input size of the model is 32x32.
+    """VGG-11-BN from `Very Deep Convolutional Networks for Large-Scale Image Recognition <https://arxiv.org/abs/1409.1556>`__.
 
     Args:
-        weights (VGG11_BN_Weights, optional): The pretrained weights for the model
-        progress (bool): If True, displays a progress bar of the download to stderr
+        weights (:class:`~torchvision.models.VGG11_BN_Weights`, optional): The
+            pretrained weights to use. See
+            :class:`~torchvision.models.VGG11_BN_Weights` below for
+            more details, and possible values. By default, no pre-trained
+            weights are used.
+        progress (bool, optional): If True, displays a progress bar of the
+            download to stderr. Default is True.
+        **kwargs: parameters passed to the ``torchvision.models.vgg.VGG``
+            base class. Please refer to the `source code
+            <https://github.com/pytorch/vision/blob/main/torchvision/models/vgg.py>`_
+            for more details about this class.
+
+    .. autoclass:: torchvision.models.VGG11_BN_Weights
+        :members:
     """
     weights = VGG11_BN_Weights.verify(weights)
 
@@ -282,13 +302,23 @@ def vgg11_bn(*, weights: Optional[VGG11_BN_Weights] = None, progress: bool = Tru
 
 @handle_legacy_interface(weights=("pretrained", VGG13_Weights.IMAGENET1K_V1))
 def vgg13(*, weights: Optional[VGG13_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
-    r"""VGG 13-layer model (configuration "B")
-    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_.
-    The required minimum input size of the model is 32x32.
+    """VGG-13 from `Very Deep Convolutional Networks for Large-Scale Image Recognition <https://arxiv.org/abs/1409.1556>`__.
 
     Args:
-        weights (VGG13_Weights, optional): The pretrained weights for the model
-        progress (bool): If True, displays a progress bar of the download to stderr
+        weights (:class:`~torchvision.models.VGG13_Weights`, optional): The
+            pretrained weights to use. See
+            :class:`~torchvision.models.VGG13_Weights` below for
+            more details, and possible values. By default, no pre-trained
+            weights are used.
+        progress (bool, optional): If True, displays a progress bar of the
+            download to stderr. Default is True.
+        **kwargs: parameters passed to the ``torchvision.models.vgg.VGG``
+            base class. Please refer to the `source code
+            <https://github.com/pytorch/vision/blob/main/torchvision/models/vgg.py>`_
+            for more details about this class.
+
+    .. autoclass:: torchvision.models.VGG13_Weights
+        :members:
     """
     weights = VGG13_Weights.verify(weights)
 
@@ -297,13 +327,23 @@ def vgg13(*, weights: Optional[VGG13_Weights] = None, progress: bool = True, **k
 
 @handle_legacy_interface(weights=("pretrained", VGG13_BN_Weights.IMAGENET1K_V1))
 def vgg13_bn(*, weights: Optional[VGG13_BN_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
-    r"""VGG 13-layer model (configuration "B") with batch normalization
-    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_.
-    The required minimum input size of the model is 32x32.
+    """VGG-13-BN from `Very Deep Convolutional Networks for Large-Scale Image Recognition <https://arxiv.org/abs/1409.1556>`__.
 
     Args:
-        weights (VGG13_BN_Weights, optional): The pretrained weights for the model
-        progress (bool): If True, displays a progress bar of the download to stderr
+        weights (:class:`~torchvision.models.VGG13_BN_Weights`, optional): The
+            pretrained weights to use. See
+            :class:`~torchvision.models.VGG13_BN_Weights` below for
+            more details, and possible values. By default, no pre-trained
+            weights are used.
+        progress (bool, optional): If True, displays a progress bar of the
+            download to stderr. Default is True.
+        **kwargs: parameters passed to the ``torchvision.models.vgg.VGG``
+            base class. Please refer to the `source code
+            <https://github.com/pytorch/vision/blob/main/torchvision/models/vgg.py>`_
+            for more details about this class.
+
+    .. autoclass:: torchvision.models.VGG13_BN_Weights
+        :members:
     """
     weights = VGG13_BN_Weights.verify(weights)
 
@@ -312,13 +352,23 @@ def vgg13_bn(*, weights: Optional[VGG13_BN_Weights] = None, progress: bool = Tru
 
 @handle_legacy_interface(weights=("pretrained", VGG16_Weights.IMAGENET1K_V1))
 def vgg16(*, weights: Optional[VGG16_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
-    r"""VGG 16-layer model (configuration "D")
-    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_.
-    The required minimum input size of the model is 32x32.
+    """VGG-16 from `Very Deep Convolutional Networks for Large-Scale Image Recognition <https://arxiv.org/abs/1409.1556>`__.
 
     Args:
-        weights (VGG16_Weights, optional): The pretrained weights for the model
-        progress (bool): If True, displays a progress bar of the download to stderr
+        weights (:class:`~torchvision.models.VGG16_Weights`, optional): The
+            pretrained weights to use. See
+            :class:`~torchvision.models.VGG16_Weights` below for
+            more details, and possible values. By default, no pre-trained
+            weights are used.
+        progress (bool, optional): If True, displays a progress bar of the
+            download to stderr. Default is True.
+        **kwargs: parameters passed to the ``torchvision.models.vgg.VGG``
+            base class. Please refer to the `source code
+            <https://github.com/pytorch/vision/blob/main/torchvision/models/vgg.py>`_
+            for more details about this class.
+
+    .. autoclass:: torchvision.models.VGG16_Weights
+        :members:
     """
     weights = VGG16_Weights.verify(weights)
 
@@ -327,13 +377,23 @@ def vgg16(*, weights: Optional[VGG16_Weights] = None, progress: bool = True, **k
 
 @handle_legacy_interface(weights=("pretrained", VGG16_BN_Weights.IMAGENET1K_V1))
 def vgg16_bn(*, weights: Optional[VGG16_BN_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
-    r"""VGG 16-layer model (configuration "D") with batch normalization
-    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_.
-    The required minimum input size of the model is 32x32.
+    """VGG-16-BN from `Very Deep Convolutional Networks for Large-Scale Image Recognition <https://arxiv.org/abs/1409.1556>`__.
 
     Args:
-        weights (VGG16_BN_Weights, optional): The pretrained weights for the model
-        progress (bool): If True, displays a progress bar of the download to stderr
+        weights (:class:`~torchvision.models.VGG16_BN_Weights`, optional): The
+            pretrained weights to use. See
+            :class:`~torchvision.models.VGG16_BN_Weights` below for
+            more details, and possible values. By default, no pre-trained
+            weights are used.
+        progress (bool, optional): If True, displays a progress bar of the
+            download to stderr. Default is True.
+        **kwargs: parameters passed to the ``torchvision.models.vgg.VGG``
+            base class. Please refer to the `source code
+            <https://github.com/pytorch/vision/blob/main/torchvision/models/vgg.py>`_
+            for more details about this class.
+
+    .. autoclass:: torchvision.models.VGG16_BN_Weights
+        :members:
     """
     weights = VGG16_BN_Weights.verify(weights)
 
@@ -342,13 +402,23 @@ def vgg16_bn(*, weights: Optional[VGG16_BN_Weights] = None, progress: bool = Tru
 
 @handle_legacy_interface(weights=("pretrained", VGG19_Weights.IMAGENET1K_V1))
 def vgg19(*, weights: Optional[VGG19_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
-    r"""VGG 19-layer model (configuration "E")
-    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_.
-    The required minimum input size of the model is 32x32.
+    """VGG-19 from `Very Deep Convolutional Networks for Large-Scale Image Recognition <https://arxiv.org/abs/1409.1556>`__.
 
     Args:
-        weights (VGG19_Weights, optional): The pretrained weights for the model
-        progress (bool): If True, displays a progress bar of the download to stderr
+        weights (:class:`~torchvision.models.VGG19_Weights`, optional): The
+            pretrained weights to use. See
+            :class:`~torchvision.models.VGG19_Weights` below for
+            more details, and possible values. By default, no pre-trained
+            weights are used.
+        progress (bool, optional): If True, displays a progress bar of the
+            download to stderr. Default is True.
+        **kwargs: parameters passed to the ``torchvision.models.vgg.VGG``
+            base class. Please refer to the `source code
+            <https://github.com/pytorch/vision/blob/main/torchvision/models/vgg.py>`_
+            for more details about this class.
+
+    .. autoclass:: torchvision.models.VGG19_Weights
+        :members:
     """
     weights = VGG19_Weights.verify(weights)
 
@@ -357,13 +427,23 @@ def vgg19(*, weights: Optional[VGG19_Weights] = None, progress: bool = True, **k
 
 @handle_legacy_interface(weights=("pretrained", VGG19_BN_Weights.IMAGENET1K_V1))
 def vgg19_bn(*, weights: Optional[VGG19_BN_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
-    r"""VGG 19-layer model (configuration 'E') with batch normalization
-    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_.
-    The required minimum input size of the model is 32x32.
+    """VGG-19_BN from `Very Deep Convolutional Networks for Large-Scale Image Recognition <https://arxiv.org/abs/1409.1556>`__.
 
     Args:
-        weights (VGG19_BN_Weights, optional): The pretrained weights for the model
-        progress (bool): If True, displays a progress bar of the download to stderr
+        weights (:class:`~torchvision.models.VGG19_BN_Weights`, optional): The
+            pretrained weights to use. See
+            :class:`~torchvision.models.VGG19_BN_Weights` below for
+            more details, and possible values. By default, no pre-trained
+            weights are used.
+        progress (bool, optional): If True, displays a progress bar of the
+            download to stderr. Default is True.
+        **kwargs: parameters passed to the ``torchvision.models.vgg.VGG``
+            base class. Please refer to the `source code
+            <https://github.com/pytorch/vision/blob/main/torchvision/models/vgg.py>`_
+            for more details about this class.
+
+    .. autoclass:: torchvision.models.VGG19_BN_Weights
+        :members:
     """
     weights = VGG19_BN_Weights.verify(weights)