[fbsync] docs: Added SSD300 to the new doc (#5934)

Summary:
* docs: Added SSD300 to the new doc

To resolve #5897

* Minor fixes

* ufmt

Reviewed By: YosuaMichael

Differential Revision: D36204387

fbshipit-source-id: 57ba33406aa52e62d2dcca3fd73ca96f5440f717

Co-authored-by: Nicolas Hug <[email protected]>

Author: 2 people

docs/source/models/ssd300.rst

@@ -0,0 +1,24 @@
+SSD300
+======
+
+.. currentmodule:: torchvision.models.detection
+
+The SSD300 model is based on the `SSD: Single Shot MultiBox Detector 
+<https://arxiv.org/abs/1512.02325>`__ paper.
+
+
+Model builders
+--------------
+
+The following model builders can be used to instanciate a SSD300 model, with or
+without pre-trained weights. All the model builders internally rely on the
+``torchvision.models.detection.SSD`` base class. Please refer to the `source
+code
+<https://github.com/pytorch/vision/blob/main/torchvision/models/detection/ssd.py>`_ for
+more details about this class.
+
+.. autosummary::
+    :toctree: generated/
+    :template: function.rst
+
+    ssd300_vgg16

docs/source/models_new.rst

@@ -51,6 +51,7 @@ weights:
    models/resnext
    models/shufflenetv2
    models/squeezenet
+   models/ssd300
    models/swin_transformer
    models/vgg
    models/vision_transformer

torchvision/models/detection/ssd.py

@@ -578,9 +578,8 @@ def ssd300_vgg16(
     trainable_backbone_layers: Optional[int] = None,
     **kwargs: Any,
 ) -> SSD:
-    """Constructs an SSD model with input size 300x300 and a VGG16 backbone.
-
-    Reference: `"SSD: Single Shot MultiBox Detector" <https://arxiv.org/abs/1512.02325>`_.
+    """The SSD300 model is based on the `SSD: Single Shot MultiBox Detector
+    <https://arxiv.org/abs/1512.02325>`_ paper.
 
     The input to the model is expected to be a list of tensors, each of shape [C, H, W], one for each
     image, and should be in 0-1 range. Different images can have different sizes but they will be resized
@@ -615,13 +614,26 @@ def ssd300_vgg16(
         >>> predictions = model(x)
 
     Args:
-        weights (SSD300_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.detection.SSD300_VGG16_Weights`, optional): The pretrained
+                weights to use. See
+                :class:`~torchvision.models.detection.SSD300_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.
         num_classes (int, optional): number of output classes of the model (including the background)
-        weights_backbone (VGG16_Weights, optional): The pretrained weights for the backbone
+        weights_backbone (:class:`~torchvision.models.VGG16_Weights`, optional): The pretrained weights for the
+            backbone
         trainable_backbone_layers (int, optional): number of trainable (not frozen) layers starting from final block.
             Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable. If ``None`` is
             passed (the default) this value is set to 4.
+        **kwargs: parameters passed to the ``torchvision.models.detection.SSD``
+            base class. Please refer to the `source code
+            <https://github.com/pytorch/vision/blob/main/torchvision/models/detection/ssd.py>`_
+            for more details about this class.
+
+    .. autoclass:: torchvision.models.detection.SSD300_VGG16_Weights
+        :members:
     """
     weights = SSD300_VGG16_Weights.verify(weights)
     weights_backbone = VGG16_Weights.verify(weights_backbone)