alexnet

torchvision.models.alexnet(*, weights: Optional[AlexNet_Weights] = None, progress: bool = True, **kwargs: Any) → AlexNet

AlexNet model architecture from One weird trick for parallelizing convolutional neural networks.

Note

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.

Parameters:

• weights (AlexNet_Weights, optional) – The pretrained weights to use. See AlexNet_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.

• **kwargs – parameters passed to the torchvision.models.squeezenet.AlexNet base class. Please refer to the source code for more details about this class.

class torchvision.models.AlexNet_Weights(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)