- torchvision.models.alexnet(*, weights: Optional[AlexNet_Weights] = None, progress: bool = True, **kwargs: Any) AlexNet [source]¶
AlexNet model architecture from One weird trick for parallelizing convolutional neural networks.
AlexNet was originally introduced in the ImageNet Classification with Deep Convolutional Neural Networks paper. Our implementation is based instead on the “One weird trick” paper above.
AlexNet_Weights, optional) – The pretrained weights to use. See
AlexNet_Weightsbelow for more details, and possible values. By default, no pre-trained weights are used.
progress (bool, optional) – If True, displays a progress bar of the download to stderr. Default is True.
**kwargs – parameters passed to the
torchvision.models.squeezenet.AlexNetbase class. Please refer to the source code for more details about this class.
- class torchvision.models.AlexNet_Weights(value)[source]¶
The model builder above accepts the following values as the
AlexNet_Weights.DEFAULTis equivalent to
AlexNet_Weights.IMAGENET1K_V1. You can also use strings, e.g.
These weights reproduce closely the results of the paper using a simplified training recipe. Also available as
acc@1 (on ImageNet-1K)
acc@5 (on ImageNet-1K)
tench, goldfish, great white shark, … (997 omitted)
The inference transforms are available at
AlexNet_Weights.IMAGENET1K_V1.transformsand perform the following preprocessing operations: Accepts
(B, C, H, W)and single
(C, H, W)image
torch.Tensorobjects. The images are resized to
interpolation=InterpolationMode.BILINEAR, followed by a central crop of
crop_size=. Finally the values are first rescaled to
[0.0, 1.0]and then normalized using
mean=[0.485, 0.456, 0.406]and
std=[0.229, 0.224, 0.225].