wide_resnet101_2¶

torchvision.models.
wide_resnet101_2
(*, weights: Optional[torchvision.models.resnet.Wide_ResNet101_2_Weights] = None, progress: bool = True, **kwargs: Any) → torchvision.models.resnet.ResNet[source]¶ Wide ResNet1012 model from Wide Residual Networks.
The model is the same as ResNet except for the bottleneck number of channels which is twice larger in every block. The number of channels in outer 1x1 convolutions is the same, e.g. last block in ResNet101 has 20485122048 channels, and in Wide ResNet1012 has 204810242048.
 Parameters
weights (
Wide_ResNet101_2_Weights
, optional) – The pretrained weights to use. SeeWide_ResNet101_2_Weights
below for more details, and possible values. By default, no pretrained 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.resnet.ResNet
base class. Please refer to the source code for more details about this class.

class
torchvision.models.
Wide_ResNet101_2_Weights
(value)[source]¶ The model builder above accepts the following values as the
weights
parameter.Wide_ResNet101_2_Weights.DEFAULT
is equivalent toWide_ResNet101_2_Weights.IMAGENET1K_V2
. You can also use strings, e.g.weights='DEFAULT'
orweights='IMAGENET1K_V1'
.Wide_ResNet101_2_Weights.IMAGENET1K_V1:
These weights reproduce closely the results of the paper using a simple training recipe.
acc@1 (on ImageNet1K)
78.848
acc@5 (on ImageNet1K)
94.284
min_size
height=1, width=1
categories
tench, goldfish, great white shark, … (997 omitted)
num_params
126886696
recipe
The inference transforms are available at
Wide_ResNet101_2_Weights.IMAGENET1K_V1.transforms
and perform the following preprocessing operations: AcceptsPIL.Image
, batched(B, C, H, W)
and single(C, H, W)
imagetorch.Tensor
objects. The images are resized toresize_size=[256]
usinginterpolation=InterpolationMode.BILINEAR
, followed by a central crop ofcrop_size=[224]
. Finally the values are first rescaled to[0.0, 1.0]
and then normalized usingmean=[0.485, 0.456, 0.406]
andstd=[0.229, 0.224, 0.225]
.Wide_ResNet101_2_Weights.IMAGENET1K_V2:
These weights improve upon the results of the original paper by using TorchVision’s new training recipe. Also available as
Wide_ResNet101_2_Weights.DEFAULT
.acc@1 (on ImageNet1K)
82.51
acc@5 (on ImageNet1K)
96.02
min_size
height=1, width=1
categories
tench, goldfish, great white shark, … (997 omitted)
num_params
126886696
recipe
The inference transforms are available at
Wide_ResNet101_2_Weights.IMAGENET1K_V2.transforms
and perform the following preprocessing operations: AcceptsPIL.Image
, batched(B, C, H, W)
and single(C, H, W)
imagetorch.Tensor
objects. The images are resized toresize_size=[232]
usinginterpolation=InterpolationMode.BILINEAR
, followed by a central crop ofcrop_size=[224]
. Finally the values are first rescaled to[0.0, 1.0]
and then normalized usingmean=[0.485, 0.456, 0.406]
andstd=[0.229, 0.224, 0.225]
.