- torchvision.models.shufflenet_v2_x1_0(*, weights: Optional[ShuffleNet_V2_X1_0_Weights] = None, progress: bool = True, **kwargs: Any) ShuffleNetV2 [source]¶
Constructs a ShuffleNetV2 architecture with 1.0x output channels, as described in ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design.
ShuffleNet_V2_X1_0_Weights, optional) – The pretrained weights to use. See
ShuffleNet_V2_X1_0_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.shufflenetv2.ShuffleNetV2base class. Please refer to the source code for more details about this class.
- class torchvision.models.ShuffleNet_V2_X1_0_Weights(value)[source]¶
The model builder above accepts the following values as the
ShuffleNet_V2_X1_0_Weights.DEFAULTis equivalent to
ShuffleNet_V2_X1_0_Weights.IMAGENET1K_V1. You can also use strings, e.g.
These weights were trained from scratch to reproduce closely the results of the paper. 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
ShuffleNet_V2_X1_0_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].