2025-05-30 nightly release (13ada56)

Author: pytorchbot

docs/source/training_references.rst

@@ -19,9 +19,9 @@ guarantees.
 
 In general, these scripts rely on the latest (not yet released) pytorch version
 or the latest torchvision version. This means that to use them, **you might need
-to install the latest pytorch and torchvision versions**, with e.g.::
+to install the latest pytorch and torchvision versions** following the `official
+instructions <https://pytorch.org/get-started/locally/>`_.
 
-    conda install pytorch torchvision -c pytorch-nightly
 
 If you need to rely on an older stable version of pytorch or torchvision, e.g.
 torchvision 0.10, then it's safer to use the scripts from that corresponding

torchvision/datasets/lfw.py

@@ -51,6 +51,10 @@ def __init__(
         self.data: list[Any] = []
 
         if download:
+            raise ValueError(
+                "LFW dataset is no longer available for download."
+                "Please download the dataset manually and place it in the specified directory"
+            )
             self.download()
 
         if not self._check_integrity():
@@ -90,6 +94,11 @@ def __len__(self) -> int:
 class LFWPeople(_LFW):
     """`LFW <http://vis-www.cs.umass.edu/lfw/>`_ Dataset.
 
+    .. warning:
+
+        The LFW dataset is no longer available for automatic download. Please
+        download it manually and place it in the specified directory.
+
     Args:
         root (str or ``pathlib.Path``): Root directory of dataset where directory
             ``lfw-py`` exists or will be saved to if download is set to True.
@@ -101,9 +110,7 @@ class LFWPeople(_LFW):
             and returns a transformed version. E.g, ``transforms.RandomCrop``
         target_transform (callable, optional): A function/transform that takes in the
             target and transforms it.
-        download (bool, optional): If true, downloads the dataset from the internet and
-            puts it in root directory. If dataset is already downloaded, it is not
-            downloaded again.
+        download (bool, optional): NOT SUPPORTED ANYMORE, leave to False.
         loader (callable, optional): A function to load an image given its path.
             By default, it uses PIL as its image loader, but users could also pass in
             ``torchvision.io.decode_image`` for decoding image data into tensors directly.
@@ -175,6 +182,11 @@ def extra_repr(self) -> str:
 class LFWPairs(_LFW):
     """`LFW <http://vis-www.cs.umass.edu/lfw/>`_ Dataset.
 
+    .. warning:
+
+        The LFW dataset is no longer available for automatic download. Please
+        download it manually and place it in the specified directory.
+
     Args:
         root (str or ``pathlib.Path``): Root directory of dataset where directory
             ``lfw-py`` exists or will be saved to if download is set to True.
@@ -186,9 +198,7 @@ class LFWPairs(_LFW):
             and returns a transformed version. E.g, ``transforms.RandomRotation``
         target_transform (callable, optional): A function/transform that takes in the
             target and transforms it.
-        download (bool, optional): If true, downloads the dataset from the internet and
-            puts it in root directory. If dataset is already downloaded, it is not
-            downloaded again.
+        download (bool, optional): NOT SUPPORTED ANYMORE, leave to False.
         loader (callable, optional): A function to load an image given its path.
             By default, it uses PIL as its image loader, but users could also pass in
             ``torchvision.io.decode_image`` for decoding image data into tensors directly.

torchvision/models/detection/mask_rcnn.py

@@ -50,7 +50,7 @@ class MaskRCNN(FasterRCNN):
           ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``.
         - labels (Int64Tensor[N]): the predicted labels for each image
         - scores (Tensor[N]): the scores or each prediction
-        - masks (UInt8Tensor[N, 1, H, W]): the predicted masks for each instance, in 0-1 range. In order to
+        - masks (FloatTensor[N, 1, H, W]): the predicted masks for each instance, in 0-1 range. In order to
           obtain the final segmentation masks, the soft masks can be thresholded, generally
           with a value of 0.5 (mask >= 0.5)