add v0.19.1 release

docs/Makefile

@@ -24,7 +24,7 @@ docset: html
 	convert $(SPHINXPROJ).docset/icon@2x.png -resize 16x16 $(SPHINXPROJ).docset/icon.png
 
 html-noplot:  # Avoids running the gallery examples, which may take time
-	$(SPHINXBUILD) -D plot_gallery=0 -b html $(ASPHINXOPTS) "${SOURCEDIR}" "$(BUILDDIR)"/html
+	$(SPHINXBUILD) -D plot_gallery=0 -b html "${SOURCEDIR}" "$(BUILDDIR)"/html
 	@echo
 	@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
 
@@ -32,6 +32,8 @@ clean:
 	rm -rf $(BUILDDIR)/*
 	rm -rf $(SOURCEDIR)/auto_examples/  # sphinx-gallery
 	rm -rf $(SOURCEDIR)/gen_modules/  # sphinx-gallery
+	rm -rf $(SOURCEDIR)/generated/  # autosummary
+	rm -rf $(SOURCEDIR)/models/generated  # autosummary
 
 .PHONY: help Makefile docset
 

docs/requirements.txt

-sphinx==2.4.4
-sphinx-gallery>=0.9.0
-sphinx-copybutton>=0.3.1
 matplotlib
 numpy
--e git+git://github.com/pytorch/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme
+sphinx-copybutton>=0.3.1
+sphinx-gallery>=0.11.1
+sphinx==5.0.0
+tabulate
+-e git+https://github.com/pytorch/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme
+pycocotools

docs/source/_static/css/custom_torchvision.css

-/* This rule (and possibly this entire file) should be removed once
+/* This rule should be removed once
 https://github.com/pytorch/pytorch_sphinx_theme/issues/125 is fixed.
 
 We override the rule so that the links to the notebooks aren't hidden in the
@@ -9,4 +9,27 @@ torchvision it just hides the links. So we have to put them back here */
 article.pytorch-article .sphx-glr-download-link-note.admonition.note,
 article.pytorch-article .reference.download.internal, article.pytorch-article .sphx-glr-signature {
   display: block;
-}
\ No newline at end of file
+}
+
+/* These 2 rules below are for the weight tables (generated in conf.py) to look
+ * better. In particular we make their row height shorter */
+.table-weights td, .table-weights th {
+  margin-bottom: 0.2rem;
+  padding: 0 !important;
+  line-height: 1 !important;
+}
+.table-weights p {
+    margin-bottom: 0.2rem !important;
+}
+
+/* Fix for Sphinx gallery 0.11
+See https://github.com/sphinx-gallery/sphinx-gallery/issues/990
+*/
+article.pytorch-article .sphx-glr-thumbnails .sphx-glr-thumbcontainer {
+    width: unset;
+    margin-right: 0;
+    margin-left: 0;
+}
+article.pytorch-article div.section div.wy-table-responsive tbody td {
+    width: 50%;
+}

docs/source/_static/img/pytorch-logo-flame.svg

@@ -30,4 +30,4 @@
      style="fill:#9e529f"
      id="path4698"
      d="m 24.075479,-7.6293945e-7 c -0.5,0 -1.8,2.49999996293945 -1.8,3.59999996293945 0,1.5 1,2 1.8,2 0.8,0 1.8,-0.5 1.8,-2 -0.1,-1.1 -1.4,-3.59999996293945 -1.8,-3.59999996293945 z"
-     class="st1" /></svg>
\ No newline at end of file
+     class="st1" /></svg>

docs/source/_templates/class.rst

+.. role:: hidden
+    :class: hidden-section
+.. currentmodule:: {{ module }}
+
+
+{{ name | underline}}
+
+.. autoclass:: {{ name }}
+    :members:

docs/source/_templates/class_dataset.rst

+.. role:: hidden
+    :class: hidden-section
+.. currentmodule:: {{ module }}
+
+
+{{ name | underline}}
+
+.. autoclass:: {{ name }}
+    :members:
+        __getitem__,
+        {% if "category_name" in methods %} category_name {% endif %}
+    :special-members:

docs/source/_templates/function.rst

+.. role:: hidden
+    :class: hidden-section
+.. currentmodule:: {{ module }}
+
+
+{{ name | underline}}
+
+.. autofunction:: {{ name }}

docs/source/beta_status.py

+from docutils import nodes
+from docutils.parsers.rst import Directive
+
+
+class BetaStatus(Directive):
+    has_content = True
+    text = "The {api_name} is in Beta stage, and backward compatibility is not guaranteed."
+    node = nodes.warning
+
+    def run(self):
+        text = self.text.format(api_name=" ".join(self.content))
+        return [self.node("", nodes.paragraph("", "", nodes.Text(text)))]
+
+
+def setup(app):
+    app.add_directive("betastatus", BetaStatus)
+    return {
+        "version": "0.1",
+        "parallel_read_safe": True,
+        "parallel_write_safe": True,
+    }

docs/source/conf.py

 #!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 #
 # PyTorch documentation build configuration file, created by
 # sphinx-quickstart on Fri Dec 23 13:31:47 2016.
@@ -21,79 +20,146 @@
 # import sys
 # sys.path.insert(0, os.path.abspath('.'))
 
-import torchvision
+import os
+import sys
+import textwrap
+from copy import copy
+from pathlib import Path
+
 import pytorch_sphinx_theme
+import torchvision
+import torchvision.models as M
+from sphinx_gallery.sorting import ExplicitOrder
+from tabulate import tabulate
 
+sys.path.append(os.path.abspath("."))
 
 # -- General configuration ------------------------------------------------
 
-# If your documentation needs a minimal Sphinx version, state it here.
-#
-# needs_sphinx = '1.0'
+# Required version of sphinx is set from docs/requirements.txt
 
 # Add any Sphinx extension module names here, as strings. They can be
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 # ones.
 extensions = [
-    'sphinx.ext.autodoc',
-    'sphinx.ext.autosummary',
-    'sphinx.ext.doctest',
-    'sphinx.ext.intersphinx',
-    'sphinx.ext.todo',
-    'sphinx.ext.coverage',
-    'sphinx.ext.mathjax',
-    'sphinx.ext.napoleon',
-    'sphinx.ext.viewcode',
-    'sphinx.ext.duration',
-    'sphinx_gallery.gen_gallery',
-    "sphinx_copybutton"
+    "sphinx.ext.autodoc",
+    "sphinx.ext.autosummary",
+    "sphinx.ext.doctest",
+    "sphinx.ext.intersphinx",
+    "sphinx.ext.todo",
+    "sphinx.ext.mathjax",
+    "sphinx.ext.napoleon",
+    "sphinx.ext.viewcode",
+    "sphinx.ext.duration",
+    "sphinx_gallery.gen_gallery",
+    "sphinx_copybutton",
+    "beta_status",
 ]
 
+# We override sphinx-gallery's example header to prevent sphinx-gallery from
+# creating a note at the top of the renderred notebook.
+# https://github.com/sphinx-gallery/sphinx-gallery/blob/451ccba1007cc523f39cbcc960ebc21ca39f7b75/sphinx_gallery/gen_rst.py#L1267-L1271
+# This is because we also want to add a link to google collab, so we write our own note in each example.
+from sphinx_gallery import gen_rst
+
+gen_rst.EXAMPLE_HEADER = """
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "{0}"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_{1}:
+
+"""
+
+
+class CustomGalleryExampleSortKey:
+    # See https://sphinx-gallery.github.io/stable/configuration.html#sorting-gallery-examples
+    # and https://github.com/sphinx-gallery/sphinx-gallery/blob/master/sphinx_gallery/sorting.py
+    def __init__(self, src_dir):
+        self.src_dir = src_dir
+
+    transforms_subsection_order = [
+        "plot_transforms_getting_started.py",
+        "plot_transforms_illustrations.py",
+        "plot_transforms_e2e.py",
+        "plot_cutmix_mixup.py",
+        "plot_custom_transforms.py",
+        "plot_tv_tensors.py",
+        "plot_custom_tv_tensors.py",
+    ]
+
+    def __call__(self, filename):
+        if "gallery/transforms" in self.src_dir:
+            try:
+                return self.transforms_subsection_order.index(filename)
+            except ValueError as e:
+                raise ValueError(
+                    "Looks like you added an example in gallery/transforms? "
+                    "You need to specify its order in docs/source/conf.py. Look for CustomGalleryExampleSortKey."
+                ) from e
+        else:
+            # For other subsections we just sort alphabetically by filename
+            return filename
+
+
 sphinx_gallery_conf = {
-    'examples_dirs': '../../gallery/',   # path to your example scripts
-    'gallery_dirs': 'auto_examples',  # path to where to save gallery generated output
-    'backreferences_dir': 'gen_modules/backreferences',
-    'doc_module': ('torchvision',),
+    "examples_dirs": "../../gallery/",  # path to your example scripts
+    "gallery_dirs": "auto_examples",  # path to where to save gallery generated output
+    "subsection_order": ExplicitOrder(["../../gallery/transforms", "../../gallery/others"]),
+    "backreferences_dir": "gen_modules/backreferences",
+    "doc_module": ("torchvision",),
+    "remove_config_comments": True,
+    "ignore_pattern": "helpers.py",
+    "within_subsection_order": CustomGalleryExampleSortKey,
 }
 
 napoleon_use_ivar = True
 napoleon_numpy_docstring = False
 napoleon_google_docstring = True
 
+
 # Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
+templates_path = ["_templates"]
 
 # The suffix(es) of source filenames.
 # You can specify multiple suffix as a list of string:
 #
-# source_suffix = ['.rst', '.md']
-source_suffix = '.rst'
+source_suffix = {
+    ".rst": "restructuredtext",
+}
 
 # The master toctree document.
-master_doc = 'index'
+master_doc = "index"
 
 # General information about the project.
-project = 'Torchvision'
-copyright = '2017-present, Torch Contributors'
-author = 'Torch Contributors'
+project = "Torchvision"
+copyright = "2017-present, Torch Contributors"
+author = "Torch Contributors"
 
 # The version info for the project you're documenting, acts as replacement for
 # |version| and |release|, also used in various other places throughout the
 # built documents.
-#
-# The short X.Y version.
-# TODO: change to [:2] at v1.0
-version = '0.10.0'
-# The full version, including alpha/beta/rc tags.
-# TODO: verify this works as expected
-release = torchvision.__version__
+# version: The short X.Y version.
+# release: The full version, including alpha/beta/rc tags.
+if os.environ.get("TORCHVISION_SANITIZE_VERSION_STR_IN_DOCS", None):
+    # Turn 1.11.0aHASH into 1.11 (major.minor only)
+    version = release = ".".join(torchvision.__version__.split(".")[:2])
+    html_title = " ".join((project, version, "documentation"))
+else:
+    version = f"main ({torchvision.__version__})"
+    release = "main"
+
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
 #
 # This is also used if you do content translation via gettext catalogs.
 # Usually you set "language" from the command line for these cases.
-language = None
+language = "en"
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
@@ -101,7 +167,7 @@ language = None
 exclude_patterns = []
 
 # The name of the Pygments (syntax highlighting) style to use.
-pygments_style = 'sphinx'
+pygments_style = "sphinx"
 
 # If true, `todo` and `todoList` produce output, else they produce nothing.
 todo_include_todos = True
@@ -112,7 +178,7 @@ todo_include_todos = True
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 #
-html_theme = 'pytorch_sphinx_theme'
+html_theme = "pytorch_sphinx_theme"
 html_theme_path = [pytorch_sphinx_theme.get_html_theme_path()]
 
 # Theme options are theme-specific and customize the look and feel of a theme
@@ -120,58 +186,57 @@ html_theme_path = [pytorch_sphinx_theme.get_html_theme_path()]
 # documentation.
 #
 html_theme_options = {
-    'collapse_navigation': False,
-    'display_version': True,
-    'logo_only': True,
-    'pytorch_project': 'docs',
-    'navigation_with_keys': True,
-    'analytics_id': 'UA-117752657-2',
+    "collapse_navigation": False,
+    "display_version": True,
+    "logo_only": True,
+    "pytorch_project": "docs",
+    "navigation_with_keys": True,
+    "analytics_id": "GTM-T8XT4PS",
 }
 
-html_logo = '_static/img/pytorch-logo-dark.svg'
+html_logo = "_static/img/pytorch-logo-dark.svg"
 
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
+html_static_path = ["_static"]
 
 # TODO: remove this once https://github.com/pytorch/pytorch_sphinx_theme/issues/125 is fixed
 html_css_files = [
-    'css/custom_torchvision.css',
+    "css/custom_torchvision.css",
 ]
 
 # -- Options for HTMLHelp output ------------------------------------------
 
 # Output file base name for HTML help builder.
-htmlhelp_basename = 'PyTorchdoc'
+htmlhelp_basename = "PyTorchdoc"
 
 
-# -- Options for LaTeX output ---------------------------------------------
+autosummary_generate = True
+
 
+# -- Options for LaTeX output ---------------------------------------------
 latex_elements = {
     # The paper size ('letterpaper' or 'a4paper').
     #
     # 'papersize': 'letterpaper',
-
     # The font size ('10pt', '11pt' or '12pt').
     #
     # 'pointsize': '10pt',
-
     # Additional stuff for the LaTeX preamble.
     #
     # 'preamble': '',
-
     # Latex figure (float) alignment
     #
     # 'figure_align': 'htbp',
 }
 
+
 # Grouping the document tree into LaTeX files. List of tuples
 # (source start file, target name, title,
 #  author, documentclass [howto, manual, or own class]).
 latex_documents = [
-    (master_doc, 'pytorch.tex', 'torchvision Documentation',
-     'Torch Contributors', 'manual'),
+    (master_doc, "pytorch.tex", "torchvision Documentation", "Torch Contributors", "manual"),
 ]
 
 
@@ -179,10 +244,7 @@ latex_documents = [
 
 # One entry per manual page. List of tuples
 # (source start file, name, description, authors, manual section).
-man_pages = [
-    (master_doc, 'torchvision', 'torchvision Documentation',
-     [author], 1)
-]
+man_pages = [(master_doc, "torchvision", "torchvision Documentation", [author], 1)]
 
 
 # -- Options for Texinfo output -------------------------------------------
@@ -191,27 +253,33 @@ man_pages = [
 # (source start file, target name, title, author,
 #  dir menu entry, description, category)
 texinfo_documents = [
-    (master_doc, 'torchvision', 'torchvision Documentation',
-     author, 'torchvision', 'One line description of project.',
-     'Miscellaneous'),
+    (
+        master_doc,
+        "torchvision",
+        "torchvision Documentation",
+        author,
+        "torchvision",
+        "One line description of project.",
+        "Miscellaneous",
+    ),
 ]
 
 
 # Example configuration for intersphinx: refer to the Python standard library.
 intersphinx_mapping = {
-    'python': ('https://docs.python.org/', None),
-    'torch': ('https://pytorch.org/docs/stable/', None),
-    'numpy': ('http://docs.scipy.org/doc/numpy/', None),
-    'PIL': ('https://pillow.readthedocs.io/en/stable/', None),
-    'matplotlib': ('https://matplotlib.org/stable/', None),
+    "python": ("https://docs.python.org/3/", None),
+    "torch": ("https://pytorch.org/docs/stable/", None),
+    "numpy": ("https://numpy.org/doc/stable/", None),
+    "PIL": ("https://pillow.readthedocs.io/en/stable/", None),
+    "matplotlib": ("https://matplotlib.org/stable/", None),
 }
 
 # -- A patch that prevents Sphinx from cross-referencing ivar tags -------
 # See http://stackoverflow.com/a/41184353/3343043
 
 from docutils import nodes
-from sphinx.util.docfields import TypedField
 from sphinx import addnodes
+from sphinx.util.docfields import TypedField
 
 
 def patched_make_field(self, types, domain, items, **kw):
@@ -221,40 +289,39 @@ def patched_make_field(self, types, domain, items, **kw):
     # type: (list, unicode, tuple) -> nodes.field  # noqa: F821
     def handle_item(fieldarg, content):
         par = nodes.paragraph()
-        par += addnodes.literal_strong('', fieldarg)  # Patch: this line added
+        par += addnodes.literal_strong("", fieldarg)  # Patch: this line added
         # par.extend(self.make_xrefs(self.rolename, domain, fieldarg,
         #                           addnodes.literal_strong))
         if fieldarg in types:
-            par += nodes.Text(' (')
+            par += nodes.Text(" (")
             # NOTE: using .pop() here to prevent a single type node to be
             # inserted twice into the doctree, which leads to
             # inconsistencies later when references are resolved
             fieldtype = types.pop(fieldarg)
             if len(fieldtype) == 1 and isinstance(fieldtype[0], nodes.Text):
-                typename = u''.join(n.astext() for n in fieldtype)
-                typename = typename.replace('int', 'python:int')
-                typename = typename.replace('long', 'python:long')
-                typename = typename.replace('float', 'python:float')
-                typename = typename.replace('type', 'python:type')
-                par.extend(self.make_xrefs(self.typerolename, domain, typename,
-                                           addnodes.literal_emphasis, **kw))
+                typename = "".join(n.astext() for n in fieldtype)
+                typename = typename.replace("int", "python:int")
+                typename = typename.replace("long", "python:long")
+                typename = typename.replace("float", "python:float")
+                typename = typename.replace("type", "python:type")
+                par.extend(self.make_xrefs(self.typerolename, domain, typename, addnodes.literal_emphasis, **kw))
             else:
                 par += fieldtype
-            par += nodes.Text(')')
-        par += nodes.Text(' -- ')
+            par += nodes.Text(")")
+        par += nodes.Text(" -- ")
         par += content
         return par
 
-    fieldname = nodes.field_name('', self.label)
+    fieldname = nodes.field_name("", self.label)
     if len(items) == 1 and self.can_collapse:
         fieldarg, content = items[0]
         bodynode = handle_item(fieldarg, content)
     else:
         bodynode = self.list_type()
         for fieldarg, content in items:
-            bodynode += nodes.list_item('', handle_item(fieldarg, content))
-    fieldbody = nodes.field_body('', bodynode)
-    return nodes.field('', fieldname, fieldbody)
+            bodynode += nodes.list_item("", handle_item(fieldarg, content))
+    fieldbody = nodes.field_body("", bodynode)
+    return nodes.field("", fieldname, fieldbody)
 
 
 TypedField.make_field = patched_make_field
@@ -286,5 +353,172 @@ def inject_minigalleries(app, what, name, obj, options, lines):
         lines.append("\n")
 
 
+def inject_weight_metadata(app, what, name, obj, options, lines):
+    """This hook is used to generate docs for the models weights.
+
+    Objects like ResNet18_Weights are enums with fields, where each field is a Weight object.
+    Enums aren't easily documented in Python so the solution we're going for is to:
+
+    - add an autoclass directive in the model's builder docstring, e.g.
+
+    ```
+    .. autoclass:: torchvision.models.ResNet34_Weights
+        :members:
+    ```
+
+    (see resnet.py for an example)
+    - then this hook is called automatically when building the docs, and it generates the text that gets
+      used within the autoclass directive.
+    """
+
+    if getattr(obj, "__name__", "").endswith(("_Weights", "_QuantizedWeights")):
+
+        if len(obj) == 0:
+            lines[:] = ["There are no available pre-trained weights."]
+            return
+
+        lines[:] = [
+            "The model builder above accepts the following values as the ``weights`` parameter.",
+            f"``{obj.__name__}.DEFAULT`` is equivalent to ``{obj.DEFAULT}``. You can also use strings, e.g. "
+            f"``weights='DEFAULT'`` or ``weights='{str(list(obj)[0]).split('.')[1]}'``.",
+        ]
+
+        if obj.__doc__ != "An enumeration.":
+            # We only show the custom enum doc if it was overridden. The default one from Python is "An enumeration"
+            lines.append("")
+            lines.append(obj.__doc__)
+
+        lines.append("")
+
+        for field in obj:
+            meta = copy(field.meta)
+
+            lines += [f"**{str(field)}**:", ""]
+            lines += [meta.pop("_docs")]
+
+            if field == obj.DEFAULT:
+                lines += [f"Also available as ``{obj.__name__}.DEFAULT``."]
+            lines += [""]
+
+            table = []
+            metrics = meta.pop("_metrics")
+            for dataset, dataset_metrics in metrics.items():
+                for metric_name, metric_value in dataset_metrics.items():
+                    table.append((f"{metric_name} (on {dataset})", str(metric_value)))
+
+            for k, v in meta.items():
+                if k in {"recipe", "license"}:
+                    v = f"`link <{v}>`__"
+                elif k == "min_size":
+                    v = f"height={v[0]}, width={v[1]}"
+                elif k in {"categories", "keypoint_names"} and isinstance(v, list):
+                    max_visible = 3
+                    v_sample = ", ".join(v[:max_visible])
+                    v = f"{v_sample}, ... ({len(v)-max_visible} omitted)" if len(v) > max_visible else v_sample
+                elif k == "_ops":
+                    v = f"{v:.2f}"
+                    k = "GIPS" if obj.__name__.endswith("_QuantizedWeights") else "GFLOPS"
+                elif k == "_file_size":
+                    k = "File size"
+                    v = f"{v:.1f} MB"
+
+                table.append((str(k), str(v)))
+            table = tabulate(table, tablefmt="rst")
+            lines += [".. rst-class:: table-weights"]  # Custom CSS class, see custom_torchvision.css
+            lines += [".. table::", ""]
+            lines += textwrap.indent(table, " " * 4).split("\n")
+            lines.append("")
+            lines.append(
+                f"The inference transforms are available at ``{str(field)}.transforms`` and "
+                f"perform the following preprocessing operations: {field.transforms().describe()}"
+            )
+            lines.append("")
+
+
+def generate_weights_table(module, table_name, metrics, dataset, include_patterns=None, exclude_patterns=None):
+    weights_endswith = "_QuantizedWeights" if module.__name__.split(".")[-1] == "quantization" else "_Weights"
+    weight_enums = [getattr(module, name) for name in dir(module) if name.endswith(weights_endswith)]
+    weights = [w for weight_enum in weight_enums for w in weight_enum]
+
+    if include_patterns is not None:
+        weights = [w for w in weights if any(p in str(w) for p in include_patterns)]
+    if exclude_patterns is not None:
+        weights = [w for w in weights if all(p not in str(w) for p in exclude_patterns)]
+
+    ops_name = "GIPS" if "QuantizedWeights" in weights_endswith else "GFLOPS"
+
+    metrics_keys, metrics_names = zip(*metrics)
+    column_names = ["Weight"] + list(metrics_names) + ["Params"] + [ops_name, "Recipe"]  # Final column order
+    column_names = [f"**{name}**" for name in column_names]  # Add bold
+
+    content = []
+    for w in weights:
+        row = [
+            f":class:`{w} <{type(w).__name__}>`",
+            *(w.meta["_metrics"][dataset][metric] for metric in metrics_keys),
+            f"{w.meta['num_params']/1e6:.1f}M",
+            f"{w.meta['_ops']:.2f}",
+            f"`link <{w.meta['recipe']}>`__",
+        ]
+
+        content.append(row)
+
+    column_widths = ["110"] + ["18"] * len(metrics_names) + ["18"] * 2 + ["10"]
+    widths_table = " ".join(column_widths)
+
+    table = tabulate(content, headers=column_names, tablefmt="rst")
+
+    generated_dir = Path("generated")
+    generated_dir.mkdir(exist_ok=True)
+    with open(generated_dir / f"{table_name}_table.rst", "w+") as table_file:
+        table_file.write(".. rst-class:: table-weights\n")  # Custom CSS class, see custom_torchvision.css
+        table_file.write(".. table::\n")
+        table_file.write(f"    :widths: {widths_table} \n\n")
+        table_file.write(f"{textwrap.indent(table, ' ' * 4)}\n\n")
+
+
+generate_weights_table(
+    module=M, table_name="classification", metrics=[("acc@1", "Acc@1"), ("acc@5", "Acc@5")], dataset="ImageNet-1K"
+)
+generate_weights_table(
+    module=M.quantization,
+    table_name="classification_quant",
+    metrics=[("acc@1", "Acc@1"), ("acc@5", "Acc@5")],
+    dataset="ImageNet-1K",
+)
+generate_weights_table(
+    module=M.detection,
+    table_name="detection",
+    metrics=[("box_map", "Box MAP")],
+    exclude_patterns=["Mask", "Keypoint"],
+    dataset="COCO-val2017",
+)
+generate_weights_table(
+    module=M.detection,
+    table_name="instance_segmentation",
+    metrics=[("box_map", "Box MAP"), ("mask_map", "Mask MAP")],
+    dataset="COCO-val2017",
+    include_patterns=["Mask"],
+)
+generate_weights_table(
+    module=M.detection,
+    table_name="detection_keypoint",
+    metrics=[("box_map", "Box MAP"), ("kp_map", "Keypoint MAP")],
+    dataset="COCO-val2017",
+    include_patterns=["Keypoint"],
+)
+generate_weights_table(
+    module=M.segmentation,
+    table_name="segmentation",
+    metrics=[("miou", "Mean IoU"), ("pixel_acc", "pixelwise Acc")],
+    dataset="COCO-val2017-VOC-labels",
+)
+generate_weights_table(
+    module=M.video, table_name="video", metrics=[("acc@1", "Acc@1"), ("acc@5", "Acc@5")], dataset="Kinetics-400"
+)
+
+
 def setup(app):
-    app.connect('autodoc-process-docstring', inject_minigalleries)
+
+    app.connect("autodoc-process-docstring", inject_minigalleries)
+    app.connect("autodoc-process-docstring", inject_weight_metadata)

docs/source/datasets.rst

-torchvision.datasets
-====================
+.. _datasets:
+
+Datasets
+========
+
+Torchvision provides many built-in datasets in the ``torchvision.datasets``
+module, as well as utility classes for building your own datasets.
+
+Built-in datasets
+-----------------
 
 All datasets are subclasses of :class:`torch.utils.data.Dataset`
 i.e, they have ``__getitem__`` and ``__len__`` methods implemented.
@@ -19,242 +27,157 @@ All the datasets have almost similar API. They all have two common arguments:
 ``transform`` and  ``target_transform`` to transform the input and target respectively.
 You can also create your own datasets using the provided :ref:`base classes <base_classes_datasets>`.
 
-Caltech
-~~~~~~~
-
-.. autoclass:: Caltech101
-  :members: __getitem__
-  :special-members:
-
-.. autoclass:: Caltech256
-  :members: __getitem__
-  :special-members:
-
-CelebA
-~~~~~~
-
-.. autoclass:: CelebA
-  :members: __getitem__
-  :special-members:
-
-CIFAR
-~~~~~
-
-.. autoclass:: CIFAR10
-  :members: __getitem__
-  :special-members:
-
-.. autoclass:: CIFAR100
-
-Cityscapes
-~~~~~~~~~~
-
-.. note ::
-    Requires Cityscape to be downloaded.
-
-.. autoclass:: Cityscapes
-  :members: __getitem__
-  :special-members:
-
-COCO
-~~~~
-
-.. note ::
-    These require the `COCO API to be installed`_
-
-.. _COCO API to be installed: https://github.com/pdollar/coco/tree/master/PythonAPI
-
-
-Captions
-^^^^^^^^
-
-.. autoclass:: CocoCaptions
-  :members: __getitem__
-  :special-members:
-
-
-Detection
-^^^^^^^^^
-
-.. autoclass:: CocoDetection
-  :members: __getitem__
-  :special-members:
-
-
-EMNIST
-~~~~~~
-
-.. autoclass:: EMNIST
-
-FakeData
-~~~~~~~~
-
-.. autoclass:: FakeData
-
-Fashion-MNIST
-~~~~~~~~~~~~~
-
-.. autoclass:: FashionMNIST
-
-Flickr
-~~~~~~
-
-.. autoclass:: Flickr8k
-  :members: __getitem__
-  :special-members:
-
-.. autoclass:: Flickr30k
-  :members: __getitem__
-  :special-members:
-
-HMDB51
-~~~~~~~
-
-.. autoclass:: HMDB51
-  :members: __getitem__
-  :special-members:
-
-ImageNet
-~~~~~~~~~~~
-
-.. autoclass:: ImageNet
-
-.. note ::
-    This requires `scipy` to be installed
-
-Kinetics-400
+Image classification
+~~~~~~~~~~~~~~~~~~~~
+
+.. autosummary::
+    :toctree: generated/
+    :template: class_dataset.rst
+
+    Caltech101
+    Caltech256
+    CelebA
+    CIFAR10
+    CIFAR100
+    Country211
+    DTD
+    EMNIST
+    EuroSAT
+    FakeData
+    FashionMNIST
+    FER2013
+    FGVCAircraft
+    Flickr8k
+    Flickr30k
+    Flowers102
+    Food101
+    GTSRB
+    INaturalist
+    ImageNet
+    Imagenette
+    KMNIST
+    LFWPeople
+    LSUN
+    MNIST
+    Omniglot
+    OxfordIIITPet
+    Places365
+    PCAM
+    QMNIST
+    RenderedSST2
+    SEMEION
+    SBU
+    StanfordCars
+    STL10
+    SUN397
+    SVHN
+    USPS
+
+Image detection or segmentation
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autosummary::
+    :toctree: generated/
+    :template: class_dataset.rst
+
+    CocoDetection
+    CelebA
+    Cityscapes
+    Kitti
+    OxfordIIITPet
+    SBDataset
+    VOCSegmentation
+    VOCDetection
+    WIDERFace
+
+Optical Flow
 ~~~~~~~~~~~~
 
-.. autoclass:: Kinetics400
-  :members: __getitem__
-  :special-members:
-
-KITTI
-~~~~~~~~~
-
-.. autoclass:: Kitti
-  :members: __getitem__
-  :special-members:
-
-KMNIST
-~~~~~~~~~~~~~
-
-.. autoclass:: KMNIST
-
-LSUN
-~~~~
-
-.. autoclass:: LSUN
-  :members: __getitem__
-  :special-members:
-
-MNIST
-~~~~~
-
-.. autoclass:: MNIST
-
-Omniglot
-~~~~~~~~
-
-.. autoclass:: Omniglot
-
-PhotoTour
-~~~~~~~~~
-
-.. autoclass:: PhotoTour
-  :members: __getitem__
-  :special-members:
-
-Places365
-~~~~~~~~~
-
-.. autoclass:: Places365
-  :members: __getitem__
-  :special-members:
-
-QMNIST
-~~~~~~
-
-.. autoclass:: QMNIST
-
-SBD
-~~~~~~
-
-.. autoclass:: SBDataset
-  :members: __getitem__
-  :special-members:
-
-SBU
-~~~
-
-.. autoclass:: SBU
-  :members: __getitem__
-  :special-members:
-
-SEMEION
-~~~~~~~
-
-.. autoclass:: SEMEION
-  :members: __getitem__
-  :special-members:
-
-STL10
-~~~~~
-
-.. autoclass:: STL10
-  :members: __getitem__
-  :special-members:
-
-SVHN
-~~~~~
+.. autosummary::
+    :toctree: generated/
+    :template: class_dataset.rst
+
+    FlyingChairs
+    FlyingThings3D
+    HD1K
+    KittiFlow
+    Sintel
+
+Stereo Matching
+~~~~~~~~~~~~~~~
+
+.. autosummary::
+    :toctree: generated/
+    :template: class_dataset.rst
+
+    CarlaStereo
+    Kitti2012Stereo
+    Kitti2015Stereo
+    CREStereo
+    FallingThingsStereo
+    SceneFlowStereo
+    SintelStereo
+    InStereo2k
+    ETH3DStereo
+    Middlebury2014Stereo
+
+Image pairs
+~~~~~~~~~~~
 
-.. autoclass:: SVHN
-  :members: __getitem__
-  :special-members:
+.. autosummary::
+    :toctree: generated/
+    :template: class_dataset.rst
 
-UCF101
-~~~~~~~
+    LFWPairs
+    PhotoTour
 
-.. autoclass:: UCF101
-  :members: __getitem__
-  :special-members:
+Image captioning
+~~~~~~~~~~~~~~~~
 
-USPS
-~~~~~
+.. autosummary::
+    :toctree: generated/
+    :template: class_dataset.rst
 
-.. autoclass:: USPS
-  :members: __getitem__
-  :special-members:
+    CocoCaptions
 
-VOC
-~~~~~~
+Video classification
+~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: VOCSegmentation
-  :members: __getitem__
-  :special-members:
+.. autosummary::
+    :toctree: generated/
+    :template: class_dataset.rst
 
-.. autoclass:: VOCDetection
-  :members: __getitem__
-  :special-members:
+    HMDB51
+    Kinetics
+    UCF101
 
-WIDERFace
-~~~~~~~~~
+Video prediction
+~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: WIDERFace
-  :members: __getitem__
-  :special-members:
+.. autosummary::
+    :toctree: generated/
+    :template: class_dataset.rst
 
+    MovingMNIST
 
 .. _base_classes_datasets:
 
 Base classes for custom datasets
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+--------------------------------
+
+.. autosummary::
+    :toctree: generated/
+    :template: class.rst
+
+    DatasetFolder
+    ImageFolder
+    VisionDataset
 
-.. autoclass:: DatasetFolder
-  :members: __getitem__, find_classes, make_dataset
-  :special-members:
+Transforms v2
+-------------
 
+.. autosummary::
+    :toctree: generated/
+    :template: function.rst
 
-.. autoclass:: ImageFolder
-  :members: __getitem__
-  :special-members:
+    wrap_dataset_for_transforms_v2

docs/source/docutils.conf

+# Necessary for the table generated by autosummary to look decent
+[html writers]
+table_style: colwidths-auto

docs/source/feature_extraction.rst

+Feature extraction for model inspection
+=======================================
+
+.. currentmodule:: torchvision.models.feature_extraction
+
+The ``torchvision.models.feature_extraction`` package contains
+feature extraction utilities that let us tap into our models to access intermediate
+transformations of our inputs. This could be useful for a variety of
+applications in computer vision. Just a few examples are:
+
+- Visualizing feature maps.
+- Extracting features to compute image descriptors for tasks like facial
+  recognition, copy-detection, or image retrieval.
+- Passing selected features to downstream sub-networks for end-to-end training
+  with a specific task in mind. For example, passing a hierarchy of features
+  to a Feature Pyramid Network with object detection heads.
+
+Torchvision provides :func:`create_feature_extractor` for this purpose.
+It works by following roughly these steps:
+
+1. Symbolically tracing the model to get a graphical representation of
+   how it transforms the input, step by step.
+2. Setting the user-selected graph nodes as outputs.
+3. Removing all redundant nodes (anything downstream of the output nodes).
+4. Generating python code from the resulting graph and bundling that into a
+   PyTorch module together with the graph itself.
+
+|
+
+The `torch.fx documentation <https://pytorch.org/docs/stable/fx.html>`_
+provides a more general and detailed explanation of the above procedure and
+the inner workings of the symbolic tracing.
+
+.. _about-node-names:
+
+**About Node Names**
+
+In order to specify which nodes should be output nodes for extracted
+features, one should be familiar with the node naming convention used here
+(which differs slightly from that used in ``torch.fx``). A node name is
+specified as a ``.`` separated path walking the module hierarchy from top level
+module down to leaf operation or leaf module. For instance ``"layer4.2.relu"``
+in ResNet-50 represents the output of the ReLU of the 2nd block of the 4th
+layer of the ``ResNet`` module. Here are some finer points to keep in mind:
+
+- When specifying node names for :func:`create_feature_extractor`, you may
+  provide a truncated version of a node name as a shortcut. To see how this
+  works, try creating a ResNet-50 model and printing the node names with
+  ``train_nodes, _ = get_graph_node_names(model) print(train_nodes)`` and
+  observe that the last node pertaining to ``layer4`` is
+  ``"layer4.2.relu_2"``. One may specify ``"layer4.2.relu_2"`` as the return
+  node, or just ``"layer4"`` as this, by convention, refers to the last node
+  (in order of execution) of ``layer4``.
+- If a certain module or operation is repeated more than once, node names get
+  an additional ``_{int}`` postfix to disambiguate. For instance, maybe the
+  addition (``+``) operation is used three times in the same ``forward``
+  method. Then there would be ``"path.to.module.add"``,
+  ``"path.to.module.add_1"``, ``"path.to.module.add_2"``. The counter is
+  maintained within the scope of the direct parent. So in ResNet-50 there is
+  a ``"layer4.1.add"`` and a ``"layer4.2.add"``. Because the addition
+  operations reside in different blocks, there is no need for a postfix to
+  disambiguate.
+
+
+**An Example**
+
+Here is an example of how we might extract features for MaskRCNN:
+
+.. code-block:: python
+
+  import torch
+  from torchvision.models import resnet50
+  from torchvision.models.feature_extraction import get_graph_node_names
+  from torchvision.models.feature_extraction import create_feature_extractor
+  from torchvision.models.detection.mask_rcnn import MaskRCNN
+  from torchvision.models.detection.backbone_utils import LastLevelMaxPool
+  from torchvision.ops.feature_pyramid_network import FeaturePyramidNetwork
+
+
+  # To assist you in designing the feature extractor you may want to print out
+  # the available nodes for resnet50.
+  m = resnet50()
+  train_nodes, eval_nodes = get_graph_node_names(resnet50())
+
+  # The lists returned, are the names of all the graph nodes (in order of
+  # execution) for the input model traced in train mode and in eval mode
+  # respectively. You'll find that `train_nodes` and `eval_nodes` are the same
+  # for this example. But if the model contains control flow that's dependent
+  # on the training mode, they may be different.
+
+  # To specify the nodes you want to extract, you could select the final node
+  # that appears in each of the main layers:
+  return_nodes = {
+      # node_name: user-specified key for output dict
+      'layer1.2.relu_2': 'layer1',
+      'layer2.3.relu_2': 'layer2',
+      'layer3.5.relu_2': 'layer3',
+      'layer4.2.relu_2': 'layer4',
+  }
+
+  # But `create_feature_extractor` can also accept truncated node specifications
+  # like "layer1", as it will just pick the last node that's a descendent of
+  # of the specification. (Tip: be careful with this, especially when a layer
+  # has multiple outputs. It's not always guaranteed that the last operation
+  # performed is the one that corresponds to the output you desire. You should
+  # consult the source code for the input model to confirm.)
+  return_nodes = {
+      'layer1': 'layer1',
+      'layer2': 'layer2',
+      'layer3': 'layer3',
+      'layer4': 'layer4',
+  }
+
+  # Now you can build the feature extractor. This returns a module whose forward
+  # method returns a dictionary like:
+  # {
+  #     'layer1': output of layer 1,
+  #     'layer2': output of layer 2,
+  #     'layer3': output of layer 3,
+  #     'layer4': output of layer 4,
+  # }
+  create_feature_extractor(m, return_nodes=return_nodes)
+
+  # Let's put all that together to wrap resnet50 with MaskRCNN
+
+  # MaskRCNN requires a backbone with an attached FPN
+  class Resnet50WithFPN(torch.nn.Module):
+      def __init__(self):
+          super(Resnet50WithFPN, self).__init__()
+          # Get a resnet50 backbone
+          m = resnet50()
+          # Extract 4 main layers (note: MaskRCNN needs this particular name
+          # mapping for return nodes)
+          self.body = create_feature_extractor(
+              m, return_nodes={f'layer{k}': str(v)
+                               for v, k in enumerate([1, 2, 3, 4])})
+          # Dry run to get number of channels for FPN
+          inp = torch.randn(2, 3, 224, 224)
+          with torch.no_grad():
+              out = self.body(inp)
+          in_channels_list = [o.shape[1] for o in out.values()]
+          # Build FPN
+          self.out_channels = 256
+          self.fpn = FeaturePyramidNetwork(
+              in_channels_list, out_channels=self.out_channels,
+              extra_blocks=LastLevelMaxPool())
+
+      def forward(self, x):
+          x = self.body(x)
+          x = self.fpn(x)
+          return x
+
+
+  # Now we can build our model!
+  model = MaskRCNN(Resnet50WithFPN(), num_classes=91).eval()
+
+
+API Reference
+-------------
+
+.. autosummary::
+    :toctree: generated/
+    :template: function.rst
+
+    create_feature_extractor
+    get_graph_node_names

docs/source/index.rst

@@ -31,18 +31,21 @@ architectures, and common image transformations for computer vision.
    :maxdepth: 2
    :caption: Package Reference
 
-   datasets
-   io
-   models
-   ops
    transforms
+   tv_tensors
+   models
+   datasets
    utils
+   ops
+   io
+   feature_extraction
 
 .. toctree::
    :maxdepth: 1
-   :caption: Examples
+   :caption: Examples and training references
 
    auto_examples/index
+   training_references
 
 .. automodule:: torchvision
    :members:
@@ -58,3 +61,9 @@ architectures, and common image transformations for computer vision.
    TorchElastic <https://pytorch.org/elastic/>
    TorchServe <https://pytorch.org/serve>
    PyTorch on XLA Devices <http://pytorch.org/xla/>
+
+
+Indices
+-------
+
+* :ref:`genindex`

docs/source/io.rst

-torchvision.io
-==============
+Decoding / Encoding images and videos
+=====================================
 
 .. currentmodule:: torchvision.io
 
 The :mod:`torchvision.io` package provides functions for performing IO
-operations. They are currently specific to reading and writing video and
-images.
+operations. They are currently specific to reading and writing images and
+videos.
+
+Images
+------
+
+.. autosummary::
+    :toctree: generated/
+    :template: function.rst
+
+    read_image
+    decode_image
+    encode_jpeg
+    decode_jpeg
+    write_jpeg
+    decode_gif
+    encode_png
+    decode_png
+    write_png
+    read_file
+    write_file
+
+.. autosummary::
+    :toctree: generated/
+    :template: class.rst
+
+    ImageReadMode
+
+
 
 Video
 -----
 
-.. autofunction:: read_video
-
-.. autofunction:: read_video_timestamps
+.. autosummary::
+    :toctree: generated/
+    :template: function.rst
 
-.. autofunction:: write_video
+    read_video
+    read_video_timestamps
+    write_video
 
 
 Fine-grained video API
-----------------------
+^^^^^^^^^^^^^^^^^^^^^^
 
 In addition to the :mod:`read_video` function, we provide a high-performance 
 lower-level API for more fine-grained control compared to the :mod:`read_video` function.
 It does all this whilst fully supporting torchscript.
 
-.. autoclass:: VideoReader
-    :members: __next__, get_metadata, set_current_stream, seek
+.. betastatus:: fine-grained video API
+
+.. autosummary::
+    :toctree: generated/
+    :template: class.rst
+
+    VideoReader
 
 
 Example of inspecting a video:
@@ -54,29 +88,3 @@ Example of inspecting a video:
     # the constructor we select a default video stream, but
     # in practice, we can set whichever stream we would like 
     video.set_current_stream("video:0")
-
-
-Image
------
-
-.. autoclass:: ImageReadMode
-
-.. autofunction:: read_image
-
-.. autofunction:: decode_image
-
-.. autofunction:: encode_jpeg
-
-.. autofunction:: decode_jpeg
-
-.. autofunction:: write_jpeg
-
-.. autofunction:: encode_png
-
-.. autofunction:: decode_png
-
-.. autofunction:: write_png
-
-.. autofunction:: read_file
-
-.. autofunction:: write_file

docs/source/models.rst

-torchvision.models
-##################
+.. _models:
 
+Models and pre-trained weights
+##############################
 
-The models subpackage contains definitions of models for addressing
+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 and video classification.
+keypoint detection, video classification, and optical flow.
 
+General information on pre-trained weights
+==========================================
 
-Classification
-==============
+TorchVision offers pre-trained weights for every provided architecture, using
+the PyTorch :mod:`torch.hub`. Instancing a pre-trained model will download its
+weights to a cache directory. This directory can be set using the `TORCH_HOME`
+environment variable. See :func:`torch.hub.load_state_dict_from_url` for details.
+
+.. note::
+
+    The pre-trained models provided in this library may have their own licenses or
+    terms and conditions derived from the dataset used for training. It is your
+    responsibility to determine whether you have permission to use the models for
+    your use case.
+
+.. 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`` (serialized 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>`_
 
-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:
+
+Initializing pre-trained models
+-------------------------------
+
+As of v0.13, TorchVision offers a new `Multi-weight support API
+<https://pytorch.org/blog/introducing-torchvision-new-multi-weight-support-api/>`_
+for loading different weights to the existing model builder methods:
 
 .. 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``:
+    from torchvision.models import resnet50, ResNet50_Weights
+
+    # Old weights with accuracy 76.130%
+    resnet50(weights=ResNet50_Weights.IMAGENET1K_V1)
+
+    # New weights with accuracy 80.858%
+    resnet50(weights=ResNet50_Weights.IMAGENET1K_V2)
+
+    # Best available weights (currently alias for IMAGENET1K_V2)
+    # Note that these weights may change across versions
+    resnet50(weights=ResNet50_Weights.DEFAULT)
+
+    # Strings are also supported
+    resnet50(weights="IMAGENET1K_V2")
+
+    # No weights - random initialization
+    resnet50(weights=None)
+
+
+Migrating to the new API is very straightforward. The following method calls between the 2 APIs are all equivalent:
 
 .. 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)
-    mobilenet_v3_small = models.mobilenet_v3_small(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.
+    from torchvision.models import resnet50, ResNet50_Weights
+
+    # Using pretrained weights:
+    resnet50(weights=ResNet50_Weights.IMAGENET1K_V1)
+    resnet50(weights="IMAGENET1K_V1")
+    resnet50(pretrained=True)  # deprecated
+    resnet50(True)  # deprecated
+
+    # Using no weights:
+    resnet50(weights=None)
+    resnet50()
+    resnet50(pretrained=False)  # deprecated
+    resnet50(False)  # deprecated
+
+Note that the ``pretrained`` parameter is now deprecated, using it will emit warnings and will be removed on v0.15.
+
+Using the pre-trained models
+----------------------------
+
+Before using the pre-trained models, one must preprocess the image
+(resize with right resolution/interpolation, apply inference transforms,
+rescale the values etc). There is no standard way to do this as it depends on
+how a given model was trained. It can vary across model families, variants or
+even weight versions. Using the correct preprocessing method is critical and
+failing to do so may lead to decreased accuracy or incorrect outputs.
+
+All the necessary information for the inference transforms of each pre-trained
+model is provided on its weights documentation. To simplify inference, TorchVision
+bundles the necessary preprocessing transforms into each model weight. These are
+accessible via the ``weight.transforms`` attribute:
+
+.. code:: python
+
+    # Initialize the Weight Transforms
+    weights = ResNet50_Weights.DEFAULT
+    preprocess = weights.transforms()
+
+    # Apply it to the input image
+    img_transformed = preprocess(img)
+
 
 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::
+.. code:: python
 
-    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
-                                     std=[0.229, 0.224, 0.225])
+    # Initialize model
+    weights = ResNet50_Weights.DEFAULT
+    model = resnet50(weights=weights)
 
-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>`_
+    # Set model to eval mode
+    model.eval()
 
-The process for obtaining the values of `mean` and `std` is roughly equivalent
-to::
+Listing and retrieving available models
+---------------------------------------
 
-    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)
-
-================================  =============   =============
-Model                             Acc@1           Acc@5
-================================  =============   =============
-AlexNet                           56.522          79.066
-VGG-11                            69.020          88.628
-VGG-13                            69.928          89.246
-VGG-16                            71.592          90.382
-VGG-19                            72.376          90.876
-VGG-11 with batch normalization   70.370          89.810
-VGG-13 with batch normalization   71.586          90.374
-VGG-16 with batch normalization   73.360          91.516
-VGG-19 with batch normalization   74.218          91.842
-ResNet-18                         69.758          89.078
-ResNet-34                         73.314          91.420
-ResNet-50                         76.130          92.862
-ResNet-101                        77.374          93.546
-ResNet-152                        78.312          94.046
-SqueezeNet 1.0                    58.092          80.420
-SqueezeNet 1.1                    58.178          80.624
-Densenet-121                      74.434          91.972
-Densenet-169                      75.600          92.806
-Densenet-201                      76.896          93.370
-Densenet-161                      77.138          93.560
-Inception v3                      77.294          93.450
-GoogleNet                         69.778          89.530
-ShuffleNet V2 x1.0                69.362          88.316
-ShuffleNet V2 x0.5                60.552          81.746
-MobileNet V2                      71.878          90.286
-MobileNet V3 Large                74.042          91.340
-MobileNet V3 Small                67.668          87.402
-ResNeXt-50-32x4d                  77.618          93.698
-ResNeXt-101-32x8d                 79.312          94.526
-Wide ResNet-50-2                  78.468          94.086
-Wide ResNet-101-2                 78.848          94.284
-MNASNet 1.0                       73.456          91.510
-MNASNet 0.5                       67.734          87.490
-================================  =============   =============
-
-
-.. _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
+As of v0.14, TorchVision offers a new mechanism which allows listing and
+retrieving models and weights by their names. Here are a few examples on how to
+use them:
 
-.. currentmodule:: torchvision.models
+.. code:: python
 
-Alexnet
--------
+    # List available models
+    all_models = list_models()
+    classification_models = list_models(module=torchvision.models)
 
-.. autofunction:: alexnet
+    # Initialize models
+    m1 = get_model("mobilenet_v3_large", weights=None)
+    m2 = get_model("quantized_mobilenet_v3_large", weights="DEFAULT")
 
-VGG
----
+    # Fetch weights
+    weights = get_weight("MobileNet_V3_Large_QuantizedWeights.DEFAULT")
+    assert weights == MobileNet_V3_Large_QuantizedWeights.DEFAULT
 
-.. autofunction:: vgg11
-.. autofunction:: vgg11_bn
-.. autofunction:: vgg13
-.. autofunction:: vgg13_bn
-.. autofunction:: vgg16
-.. autofunction:: vgg16_bn
-.. autofunction:: vgg19
-.. autofunction:: vgg19_bn
+    weights_enum = get_model_weights("quantized_mobilenet_v3_large")
+    assert weights_enum == MobileNet_V3_Large_QuantizedWeights
 
+    weights_enum2 = get_model_weights(torchvision.models.quantization.mobilenet_v3_large)
+    assert weights_enum == weights_enum2
 
-ResNet
-------
+Here are the available public functions to retrieve models and their corresponding weights:
 
-.. autofunction:: resnet18
-.. autofunction:: resnet34
-.. autofunction:: resnet50
-.. autofunction:: resnet101
-.. autofunction:: resnet152
+.. currentmodule:: torchvision.models
+.. autosummary::
+    :toctree: generated/
+    :template: function.rst
 
-SqueezeNet
-----------
+    get_model
+    get_model_weights
+    get_weight
+    list_models
 
-.. autofunction:: squeezenet1_0
-.. autofunction:: squeezenet1_1
+Using models from Hub
+---------------------
 
-DenseNet
----------
+Most pre-trained models can be accessed directly via PyTorch Hub without having TorchVision installed:
 
-.. autofunction:: densenet121
-.. autofunction:: densenet169
-.. autofunction:: densenet161
-.. autofunction:: densenet201
+.. code:: python
 
-Inception v3
-------------
+    import torch
 
-.. autofunction:: inception_v3
+    # Option 1: passing weights param as string
+    model = torch.hub.load("pytorch/vision", "resnet50", weights="IMAGENET1K_V2")
 
-.. note ::
-    This requires `scipy` to be installed
+    # Option 2: passing weights param as enum
+    weights = torch.hub.load("pytorch/vision", "get_weight", weights="ResNet50_Weights.IMAGENET1K_V2")
+    model = torch.hub.load("pytorch/vision", "resnet50", weights=weights)
 
+You can also retrieve all the available weights of a specific model via PyTorch Hub by doing:
 
-GoogLeNet
-------------
+.. code:: python
 
-.. autofunction:: googlenet
+    import torch
 
-.. note ::
-    This requires `scipy` to be installed
+    weight_enum = torch.hub.load("pytorch/vision", "get_model_weights", name="resnet50")
+    print([weight for weight in weight_enum])
+
+The only exception to the above are the detection models included on
+:mod:`torchvision.models.detection`. These models require TorchVision
+to be installed because they depend on custom C++ operators.
+
+Classification
+==============
 
+.. currentmodule:: torchvision.models
 
-ShuffleNet v2
--------------
+The following classification models are available, with or without pre-trained
+weights:
+
+.. toctree::
+   :maxdepth: 1
+
+   models/alexnet
+   models/convnext
+   models/densenet
+   models/efficientnet
+   models/efficientnetv2
+   models/googlenet
+   models/inception
+   models/maxvit
+   models/mnasnet
+   models/mobilenetv2
+   models/mobilenetv3
+   models/regnet
+   models/resnet
+   models/resnext
+   models/shufflenetv2
+   models/squeezenet
+   models/swin_transformer
+   models/vgg
+   models/vision_transformer
+   models/wide_resnet
+
+|
+
+Here is an example of how to use the pre-trained image classification models:
 
-.. autofunction:: shufflenet_v2_x0_5
-.. autofunction:: shufflenet_v2_x1_0
-.. autofunction:: shufflenet_v2_x1_5
-.. autofunction:: shufflenet_v2_x2_0
+.. code:: python
 
-MobileNet v2
--------------
+    from torchvision.io import read_image
+    from torchvision.models import resnet50, ResNet50_Weights
 
-.. autofunction:: mobilenet_v2
+    img = read_image("test/assets/encode_jpeg/grace_hopper_517x606.jpg")
 
-MobileNet v3
--------------
+    # Step 1: Initialize model with the best available weights
+    weights = ResNet50_Weights.DEFAULT
+    model = resnet50(weights=weights)
+    model.eval()
 
-.. autofunction:: mobilenet_v3_large
-.. autofunction:: mobilenet_v3_small
+    # Step 2: Initialize the inference transforms
+    preprocess = weights.transforms()
 
-ResNext
--------
+    # Step 3: Apply inference preprocessing transforms
+    batch = preprocess(img).unsqueeze(0)
 
-.. autofunction:: resnext50_32x4d
-.. autofunction:: resnext101_32x8d
+    # Step 4: Use the model and print the predicted category
+    prediction = model(batch).squeeze(0).softmax(0)
+    class_id = prediction.argmax().item()
+    score = prediction[class_id].item()
+    category_name = weights.meta["categories"][class_id]
+    print(f"{category_name}: {100 * score:.1f}%")
 
-Wide ResNet
------------
+The classes of the pre-trained model outputs can be found at ``weights.meta["categories"]``.
 
-.. autofunction:: wide_resnet50_2
-.. autofunction:: wide_resnet101_2
+Table of all available classification weights
+---------------------------------------------
 
-MNASNet
---------
+Accuracies are reported on ImageNet-1K using single crops:
 
-.. autofunction:: mnasnet0_5
-.. autofunction:: mnasnet0_75
-.. autofunction:: mnasnet1_0
-.. autofunction:: mnasnet1_3
+.. include:: generated/classification_table.rst
 
-Quantized Models
+Quantized models
 ----------------
 
-The following architectures provide support for INT8 quantized models. You can get
-a model with random weights by calling its constructor:
+.. currentmodule:: torchvision.models.quantization
 
-.. code:: python
+The following architectures provide support for INT8 quantized models, with or without
+pre-trained weights:
+
+.. toctree::
+   :maxdepth: 1
 
-    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()
-    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:
+   models/googlenet_quant
+   models/inception_quant
+   models/mobilenetv2_quant
+   models/mobilenetv3_quant
+   models/resnet_quant
+   models/resnext_quant
+   models/shufflenetv2_quant
+
+|
+
+Here is an example of how to use the pre-trained quantized image classification models:
 
 .. code:: python
 
-    import torchvision.models as models
-    model = models.quantization.mobilenet_v2(pretrained=True, quantize=True)
+    from torchvision.io import read_image
+    from torchvision.models.quantization import resnet50, ResNet50_QuantizedWeights
+
+    img = read_image("test/assets/encode_jpeg/grace_hopper_517x606.jpg")
+
+    # Step 1: Initialize model with the best available weights
+    weights = ResNet50_QuantizedWeights.DEFAULT
+    model = resnet50(weights=weights, 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:
+    # Step 2: Initialize the inference transforms
+    preprocess = weights.transforms()
+
+    # Step 3: Apply inference preprocessing transforms
+    batch = preprocess(img).unsqueeze(0)
+
+    # Step 4: Use the model and print the predicted category
+    prediction = model(batch).squeeze(0).softmax(0)
+    class_id = prediction.argmax().item()
+    score = prediction[class_id].item()
+    category_name = weights.meta["categories"][class_id]
+    print(f"{category_name}: {100 * score}%")
 
-================================  =============  =============
-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
-================================  =============  =============
+The classes of the pre-trained model outputs can be found at ``weights.meta["categories"]``.
 
 
+Table of all available quantized classification weights
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Accuracies are reported on ImageNet-1K using single crops:
+
+.. include:: generated/classification_quant_table.rst
+
 Semantic Segmentation
 =====================
 
-The models subpackage contains definitions for the following model
-architectures for semantic segmentation:
+.. currentmodule:: torchvision.models.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>`_
+.. betastatus:: segmentation module
 
-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 following semantic segmentation models are available, with or without
+pre-trained weights:
 
-For details on how to plot the masks of such models, you may refer to :ref:`semantic_seg_output`.
+.. toctree::
+   :maxdepth: 1
 
-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:
+   models/deeplabv3
+   models/fcn
+   models/lraspp
 
-  .. code-block:: python
+|
 
-      ['__background__', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus',
-       'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike',
-       'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor']
+Here is an example of how to use the pre-trained semantic segmentation models:
 
-The accuracies of the pre-trained models evaluated on COCO val2017 are as follows
+.. code:: python
 
-================================  =============  ====================
-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
-================================  =============  ====================
+    from torchvision.io.image import read_image
+    from torchvision.models.segmentation import fcn_resnet50, FCN_ResNet50_Weights
+    from torchvision.transforms.functional import to_pil_image
 
+    img = read_image("gallery/assets/dog1.jpg")
 
-Fully Convolutional Networks
-----------------------------
+    # Step 1: Initialize model with the best available weights
+    weights = FCN_ResNet50_Weights.DEFAULT
+    model = fcn_resnet50(weights=weights)
+    model.eval()
 
-.. autofunction:: torchvision.models.segmentation.fcn_resnet50
-.. autofunction:: torchvision.models.segmentation.fcn_resnet101
+    # Step 2: Initialize the inference transforms
+    preprocess = weights.transforms()
 
+    # Step 3: Apply inference preprocessing transforms
+    batch = preprocess(img).unsqueeze(0)
 
-DeepLabV3
----------
+    # Step 4: Use the model and visualize the prediction
+    prediction = model(batch)["out"]
+    normalized_masks = prediction.softmax(dim=1)
+    class_to_idx = {cls: idx for (idx, cls) in enumerate(weights.meta["categories"])}
+    mask = normalized_masks[0, class_to_idx["dog"]]
+    to_pil_image(mask).show()
 
-.. autofunction:: torchvision.models.segmentation.deeplabv3_resnet50
-.. autofunction:: torchvision.models.segmentation.deeplabv3_resnet101
-.. autofunction:: torchvision.models.segmentation.deeplabv3_mobilenet_v3_large
+The classes of the pre-trained model outputs can be found at ``weights.meta["categories"]``.
+The output format of the models is illustrated in :ref:`semantic_seg_output`.
 
 
-LR-ASPP
--------
+Table of all available semantic segmentation weights
+----------------------------------------------------
+
+All models are evaluated a subset of COCO val2017, on the 20 categories that are present in the Pascal VOC dataset:
+
+.. include:: generated/segmentation_table.rst
 
-.. autofunction:: torchvision.models.segmentation.lraspp_mobilenet_v3_large
 
 .. _object_det_inst_seg_pers_keypoint_det:
 
 Object Detection, Instance Segmentation and Person Keypoint Detection
 =====================================================================
 
-The models subpackage contains definitions for the following model
-architectures for detection:
-
-- `Faster R-CNN <https://arxiv.org/abs/1506.01497>`_
-- `Mask R-CNN <https://arxiv.org/abs/1703.06870>`_
-- `RetinaNet <https://arxiv.org/abs/1708.02002>`_
-- `SSD <https://arxiv.org/abs/1512.02325>`_
-- `SSDlite <https://arxiv.org/abs/1801.04381>`_
-
 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 but the behaviour varies depending
-on the model. Check the constructor of the models for more information. The
-output format of such models is illustrated in :ref:`instance_seg_output`.
-
-
-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     -         -
-SSD300 VGG16                            25.1     -         -
-SSDlite320 MobileNetV3-Large            21.3     -         -
-Mask R-CNN ResNet-50 FPN                37.9     34.6      -
-======================================  =======  ========  ===========
+in torchvision. The models expect a list of ``Tensor[C, H, W]``.
+Check the constructor of the models for more information.
+
+.. betastatus:: detection module
+
+Object Detection
+----------------
+
+.. currentmodule:: torchvision.models.detection
+
+The following object detection models are available, with or without pre-trained
+weights:
+
+.. toctree::
+   :maxdepth: 1
+
+   models/faster_rcnn
+   models/fcos
+   models/retinanet
+   models/ssd
+   models/ssdlite
+
+|
 
-For person keypoint detection, the accuracies for the pre-trained
-models are as follows
+Here is an example of how to use the pre-trained object detection models:
 
-================================  =======  ========  ===========
-Network                           box AP   mask AP   keypoint AP
-================================  =======  ========  ===========
-Keypoint R-CNN ResNet-50 FPN      54.6     -         65.0
-================================  =======  ========  ===========
+.. code:: python
+
+
+    from torchvision.io.image import read_image
+    from torchvision.models.detection import fasterrcnn_resnet50_fpn_v2, FasterRCNN_ResNet50_FPN_V2_Weights
+    from torchvision.utils import draw_bounding_boxes
+    from torchvision.transforms.functional import to_pil_image
+
+    img = read_image("test/assets/encode_jpeg/grace_hopper_517x606.jpg")
+
+    # Step 1: Initialize model with the best available weights
+    weights = FasterRCNN_ResNet50_FPN_V2_Weights.DEFAULT
+    model = fasterrcnn_resnet50_fpn_v2(weights=weights, box_score_thresh=0.9)
+    model.eval()
 
-For person keypoint detection, the pre-trained model return the
-keypoints in the following order:
+    # Step 2: Initialize the inference transforms
+    preprocess = weights.transforms()
 
-  .. code-block:: python
+    # Step 3: Apply inference preprocessing transforms
+    batch = [preprocess(img)]
 
-    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'
-    ]
+    # Step 4: Use the model and visualize the prediction
+    prediction = model(batch)[0]
+    labels = [weights.meta["categories"][i] for i in prediction["labels"]]
+    box = draw_bounding_boxes(img, boxes=prediction["boxes"],
+                              labels=labels,
+                              colors="red",
+                              width=4, font_size=30)
+    im = to_pil_image(box.detach())
+    im.show()
 
-Runtime characteristics
------------------------
+The classes of the pre-trained model outputs can be found at ``weights.meta["categories"]``.
+For details on how to plot the bounding boxes of the models, you may refer to :ref:`instance_seg_output`.
 
-The implementations of the models for object detection, instance segmentation
-and keypoint detection are efficient.
+Table of all available Object detection weights
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-In the following table, we use 8 GPUs to report the results. During training,
-we use a batch size of 2 per GPU for all models except SSD which uses 4
-and SSDlite which uses 24. During testing a batch size  of 1 is used.
+Box MAPs are reported on COCO val2017:
 
-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.
+.. include:: generated/detection_table.rst
 
-======================================  ===================  ==================  ===========
-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
-SSD300 VGG16                            0.2093               0.0744              1.5
-SSDlite320 MobileNetV3-Large            0.1773               0.0906              1.5
-Mask R-CNN ResNet-50 FPN                0.2728               0.0903              5.4
-Keypoint R-CNN ResNet-50 FPN            0.3789               0.1242              6.8
-======================================  ===================  ==================  ===========
 
+Instance Segmentation
+---------------------
 
-Faster R-CNN
-------------
+.. currentmodule:: torchvision.models.detection
 
-.. 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
+The following instance segmentation models are available, with or without pre-trained
+weights:
 
+.. toctree::
+   :maxdepth: 1
 
-RetinaNet
----------
+   models/mask_rcnn
 
-.. autofunction:: torchvision.models.detection.retinanet_resnet50_fpn
+|
 
 
-SSD
----
+For details on how to plot the masks of the models, you may refer to :ref:`instance_seg_output`.
 
-.. autofunction:: torchvision.models.detection.ssd300_vgg16
+Table of all available Instance segmentation weights
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
+Box and Mask MAPs are reported on COCO val2017:
 
-SSDlite
--------
+.. include:: generated/instance_segmentation_table.rst
 
-.. autofunction:: torchvision.models.detection.ssdlite320_mobilenet_v3_large
+Keypoint Detection
+------------------
 
+.. currentmodule:: torchvision.models.detection
 
-Mask R-CNN
-----------
+The following person keypoint detection models are available, with or without
+pre-trained weights:
 
-.. autofunction:: torchvision.models.detection.maskrcnn_resnet50_fpn
+.. toctree::
+   :maxdepth: 1
 
+   models/keypoint_rcnn
 
-Keypoint R-CNN
---------------
+|
 
-.. autofunction:: torchvision.models.detection.keypointrcnn_resnet50_fpn
+The classes of the pre-trained model outputs can be found at ``weights.meta["keypoint_names"]``.
+For details on how to plot the bounding boxes of the models, you may refer to :ref:`keypoint_output`.
 
+Table of all available Keypoint detection weights
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-Video classification
+Box and Keypoint MAPs are reported on COCO val2017:
+
+.. include:: generated/detection_keypoint_table.rst
+
+
+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``.
+.. currentmodule:: torchvision.models.video
 
-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]``.
+.. betastatus:: video module
 
+The following video classification models are available, with or without
+pre-trained weights:
 
-.. note::
-  The normalization parameters are different from the image classification ones, and correspond
-  to the mean and std from Kinetics-400.
+.. toctree::
+   :maxdepth: 1
 
-.. 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.
+   models/video_mvit
+   models/video_resnet
+   models/video_s3d
+   models/video_swin_transformer
+
+|
+
+Here is an example of how to use the pre-trained video classification models:
+
+.. code:: python
+
+
+    from torchvision.io.video import read_video
+    from torchvision.models.video import r3d_18, R3D_18_Weights
+
+    vid, _, _ = read_video("test/assets/videos/v_SoccerJuggling_g23_c01.avi", output_format="TCHW")
+    vid = vid[:32]  # optionally shorten duration
+
+    # Step 1: Initialize model with the best available weights
+    weights = R3D_18_Weights.DEFAULT
+    model = r3d_18(weights=weights)
+    model.eval()
+
+    # Step 2: Initialize the inference transforms
+    preprocess = weights.transforms()
+
+    # Step 3: Apply inference preprocessing transforms
+    batch = preprocess(vid).unsqueeze(0)
+
+    # Step 4: Use the model and print the predicted category
+    prediction = model(batch).squeeze(0).softmax(0)
+    label = prediction.argmax().item()
+    score = prediction[label].item()
+    category_name = weights.meta["categories"][label]
+    print(f"{category_name}: {100 * score}%")
+
+The classes of the pre-trained model outputs can be found at ``weights.meta["categories"]``.
 
-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
-================================  =============   =============
+Table of all available video classification weights
+---------------------------------------------------
 
+Accuracies are reported on Kinetics-400 using single crops for clip length 16:
 
-ResNet 3D
-----------
+.. include:: generated/video_table.rst
 
-.. autofunction:: torchvision.models.video.r3d_18
+Optical Flow
+============
 
-ResNet Mixed Convolution
-------------------------
+.. currentmodule:: torchvision.models.optical_flow
 
-.. autofunction:: torchvision.models.video.mc3_18
+The following Optical Flow models are available, with or without pre-trained
 
-ResNet (2+1)D
--------------
+.. toctree::
+   :maxdepth: 1
 
-.. autofunction:: torchvision.models.video.r2plus1d_18
+   models/raft

docs/source/models/alexnet.rst

+AlexNet
+=======
+
+.. currentmodule:: torchvision.models
+
+The AlexNet model was originally introduced in the
+`ImageNet Classification with Deep Convolutional Neural Networks
+<https://papers.nips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html>`__
+paper. The implemented architecture is slightly different from the original one,
+and is based on `One weird trick for parallelizing convolutional neural networks
+<https://arxiv.org/abs/1404.5997>`__.
+
+
+Model builders
+--------------
+
+The following model builders can be used to instantiate an AlexNet model, with or
+without pre-trained weights. All the model builders internally rely on the
+``torchvision.models.alexnet.AlexNet`` base class. Please refer to the `source
+code
+<https://github.com/pytorch/vision/blob/main/torchvision/models/alexnet.py>`_ for
+more details about this class.
+
+.. autosummary::
+    :toctree: generated/
+    :template: function.rst
+
+    alexnet

docs/source/models/convnext.rst

+ConvNeXt
+========
+
+.. currentmodule:: torchvision.models
+
+The ConvNeXt model is based on the `A ConvNet for the 2020s
+<https://arxiv.org/abs/2201.03545>`_ paper.
+
+
+Model builders
+--------------
+
+The following model builders can be used to instantiate a ConvNeXt model, with or
+without pre-trained weights. All the model builders internally rely on the
+``torchvision.models.convnext.ConvNeXt`` base class. Please refer to the `source code
+<https://github.com/pytorch/vision/blob/main/torchvision/models/convnext.py>`_ for
+more details about this class.
+
+.. autosummary::
+   :toctree: generated/
+   :template: function.rst
+
+   convnext_tiny
+   convnext_small
+   convnext_base
+   convnext_large

docs/source/models/deeplabv3.rst

+DeepLabV3
+=========
+
+.. currentmodule:: torchvision.models.segmentation
+
+The DeepLabV3 model is based on the `Rethinking Atrous Convolution for Semantic
+Image Segmentation <https://arxiv.org/abs/1706.05587>`__ paper.
+
+.. betastatus:: segmentation module
+
+
+Model builders
+--------------
+
+The following model builders can be used to instantiate a DeepLabV3 model with
+different backbones, with or without pre-trained weights. All the model builders
+internally rely on the ``torchvision.models.segmentation.deeplabv3.DeepLabV3`` base class. Please
+refer to the `source code
+<https://github.com/pytorch/vision/blob/main/torchvision/models/segmentation/deeplabv3.py>`_
+for more details about this class.
+
+.. autosummary::
+    :toctree: generated/
+    :template: function.rst
+
+    deeplabv3_mobilenet_v3_large
+    deeplabv3_resnet50
+    deeplabv3_resnet101

docs/source/models/densenet.rst

+DenseNet
+========
+
+.. currentmodule:: torchvision.models
+
+The DenseNet model is based on the `Densely Connected Convolutional Networks
+<https://arxiv.org/abs/1608.06993>`_ paper.
+
+
+Model builders
+--------------
+
+The following model builders can be used to instantiate a DenseNet model, with or
+without pre-trained weights. All the model builders internally rely on the
+``torchvision.models.densenet.DenseNet`` base class. Please refer to the `source
+code
+<https://github.com/pytorch/vision/blob/main/torchvision/models/densenet.py>`_ for
+more details about this class.
+
+.. autosummary::
+    :toctree: generated/
+    :template: function.rst
+
+    densenet121
+    densenet161
+    densenet169
+    densenet201

docs/source/models/efficientnet.rst

+EfficientNet
+============
+
+.. currentmodule:: torchvision.models
+
+The EfficientNet model is based on the `EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks <https://arxiv.org/abs/1905.11946>`__
+paper.
+
+
+Model builders
+--------------
+
+The following model builders can be used to instantiate an EfficientNet model, with or
+without pre-trained weights. All the model builders internally rely on the
+``torchvision.models.efficientnet.EfficientNet`` base class. Please refer to the `source
+code
+<https://github.com/pytorch/vision/blob/main/torchvision/models/efficientnet.py>`_ for
+more details about this class.
+
+.. autosummary::
+    :toctree: generated/
+    :template: function.rst
+
+    efficientnet_b0
+    efficientnet_b1
+    efficientnet_b2
+    efficientnet_b3
+    efficientnet_b4
+    efficientnet_b5
+    efficientnet_b6
+    efficientnet_b7