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OpenDAS
vision
Commits
36c85c79
Unverified
Commit
36c85c79
authored
Apr 27, 2022
by
Nicolas Hug
Committed by
GitHub
Apr 27, 2022
Browse files
Fix alexnet docs (#5905)
Co-authored-by:
Vasilis Vryniotis
<
datumbox@users.noreply.github.com
>
parent
b53c91d0
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docs/source/models/alexnet.rst
docs/source/models/alexnet.rst
+3
-2
torchvision/models/alexnet.py
torchvision/models/alexnet.py
+20
-20
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docs/source/models/alexnet.rst
View file @
36c85c79
...
@@ -3,8 +3,9 @@ AlexNet
...
@@ -3,8 +3,9 @@ AlexNet
.. currentmodule:: torchvision.models
.. currentmodule:: torchvision.models
The AlexNet model is based on the `ImageNet Classification with Deep Convolutional Neural Networks
The AlexNet model is based on the
<https://papers.nips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html>`__
`ImageNet Classification with Deep Convolutional Neural Networks
<https://papers.nips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html>`__.
paper.
paper.
...
...
torchvision/models/alexnet.py
View file @
36c85c79
...
@@ -72,9 +72,9 @@ class AlexNet_Weights(WeightsEnum):
...
@@ -72,9 +72,9 @@ class AlexNet_Weights(WeightsEnum):
@
handle_legacy_interface
(
weights
=
(
"pretrained"
,
AlexNet_Weights
.
IMAGENET1K_V1
))
@
handle_legacy_interface
(
weights
=
(
"pretrained"
,
AlexNet_Weights
.
IMAGENET1K_V1
))
def
alexnet
(
*
,
weights
:
Optional
[
AlexNet_Weights
]
=
None
,
progress
:
bool
=
True
,
**
kwargs
:
Any
)
->
AlexNet
:
def
alexnet
(
*
,
weights
:
Optional
[
AlexNet_Weights
]
=
None
,
progress
:
bool
=
True
,
**
kwargs
:
Any
)
->
AlexNet
:
r
"""AlexNet model architecture from the
"""AlexNet model architecture from the
`ImageNet Classification with Deep Convolutional Neural Networks
`ImageNet Classification with Deep Convolutional Neural Networks
<https://papers.nips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html>`__ paper.
<https://papers.nips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html>`_ paper.
The required minimum input size of the model is 63x63.
The required minimum input size of the model is 63x63.
Args:
Args:
...
...
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