vgg16¶
- torchvision.models.vgg16(*, weights: Optional[VGG16_Weights] = None, progress: bool = True, **kwargs: Any) VGG [source]¶
VGG-16 from Very Deep Convolutional Networks for Large-Scale Image Recognition.
- Parameters:
weights (
VGG16_Weights
, optional) – The pretrained weights to use. SeeVGG16_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.vgg.VGG
base class. Please refer to the source code for more details about this class.
- class torchvision.models.VGG16_Weights(value)[source]¶
The model builder above accepts the following values as the
weights
parameter.VGG16_Weights.DEFAULT
is equivalent toVGG16_Weights.IMAGENET1K_V1
. You can also use strings, e.g.weights='DEFAULT'
orweights='IMAGENET1K_V1'
.VGG16_Weights.IMAGENET1K_V1:
These weights were trained from scratch by using a simplified training recipe. Also available as
VGG16_Weights.DEFAULT
.acc@1 (on ImageNet-1K)
71.592
acc@5 (on ImageNet-1K)
90.382
min_size
height=32, width=32
categories
tench, goldfish, great white shark, … (997 omitted)
recipe
num_params
138357544
The inference transforms are available at
VGG16_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]
.VGG16_Weights.IMAGENET1K_FEATURES:
These weights can’t be used for classification because they are missing values in the classifier module. Only the features module has valid values and can be used for feature extraction. The weights were trained using the original input standardization method as described in the paper.
acc@1 (on ImageNet-1K)
nan
acc@5 (on ImageNet-1K)
nan
min_size
height=32, width=32
categories
None
recipe
num_params
138357544
The inference transforms are available at
VGG16_Weights.IMAGENET1K_FEATURES.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=[0.48235, 0.45882, 0.40784]
andstd=[0.00392156862745098, 0.00392156862745098, 0.00392156862745098]
.