swin_t¶
-
torchvision.models.
swin_t
(*, weights: Optional[torchvision.models.swin_transformer.Swin_T_Weights] = None, progress: bool = True, **kwargs: Any) → torchvision.models.swin_transformer.SwinTransformer[source]¶ Constructs a swin_tiny architecture from Swin Transformer: Hierarchical Vision Transformer using Shifted Windows.
- Parameters
weights (
Swin_T_Weights
, optional) – The pretrained weights to use. SeeSwin_T_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.swin_transformer.SwinTransformer
base class. Please refer to the source code for more details about this class.
-
class
torchvision.models.
Swin_T_Weights
(value)[source]¶ The model builder above accepts the following values as the
weights
parameter.Swin_T_Weights.DEFAULT
is equivalent toSwin_T_Weights.IMAGENET1K_V1
. You can also use strings, e.g.weights='DEFAULT'
orweights='IMAGENET1K_V1'
.Swin_T_Weights.IMAGENET1K_V1:
These weights reproduce closely the results of the paper using a similar training recipe. Also available as
Swin_T_Weights.DEFAULT
.acc@1 (on ImageNet-1K)
81.474
acc@5 (on ImageNet-1K)
95.776
categories
tench, goldfish, great white shark, … (997 omitted)
num_params
28288354
min_size
height=224, width=224
recipe
The inference transforms are available at
Swin_T_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=[232]
usinginterpolation=InterpolationMode.BICUBIC
, 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]
.