vit_b_16¶
- torchvision.models.vit_b_16(*, weights: Optional[ViT_B_16_Weights] = None, progress: bool = True, **kwargs: Any) VisionTransformer [source]¶
Constructs a vit_b_16 architecture from An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.
- Parameters:
weights (
ViT_B_16_Weights
, optional) – The pretrained weights to use. SeeViT_B_16_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.vision_transformer.VisionTransformer
base class. Please refer to the source code for more details about this class.
- class torchvision.models.ViT_B_16_Weights(value)[source]¶
The model builder above accepts the following values as the
weights
parameter.ViT_B_16_Weights.DEFAULT
is equivalent toViT_B_16_Weights.IMAGENET1K_V1
. You can also use strings, e.g.weights='DEFAULT'
orweights='IMAGENET1K_V1'
.ViT_B_16_Weights.IMAGENET1K_V1:
These weights were trained from scratch by using a modified version of DeIT’s training recipe. Also available as
ViT_B_16_Weights.DEFAULT
.acc@1 (on ImageNet-1K)
81.072
acc@5 (on ImageNet-1K)
95.318
categories
tench, goldfish, great white shark, … (997 omitted)
num_params
86567656
min_size
height=224, width=224
recipe
GFLOPS
17.56
File size
330.3 MB
The inference transforms are available at
ViT_B_16_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]
.ViT_B_16_Weights.IMAGENET1K_SWAG_E2E_V1:
These weights are learnt via transfer learning by end-to-end fine-tuning the original SWAG weights on ImageNet-1K data.
acc@1 (on ImageNet-1K)
85.304
acc@5 (on ImageNet-1K)
97.65
categories
tench, goldfish, great white shark, … (997 omitted)
recipe
license
num_params
86859496
min_size
height=384, width=384
GFLOPS
55.48
File size
331.4 MB
The inference transforms are available at
ViT_B_16_Weights.IMAGENET1K_SWAG_E2E_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=[384]
usinginterpolation=InterpolationMode.BICUBIC
, followed by a central crop ofcrop_size=[384]
. 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]
.ViT_B_16_Weights.IMAGENET1K_SWAG_LINEAR_V1:
These weights are composed of the original frozen SWAG trunk weights and a linear classifier learnt on top of them trained on ImageNet-1K data.
acc@1 (on ImageNet-1K)
81.886
acc@5 (on ImageNet-1K)
96.18
categories
tench, goldfish, great white shark, … (997 omitted)
recipe
license
num_params
86567656
min_size
height=224, width=224
GFLOPS
17.56
File size
330.3 MB
The inference transforms are available at
ViT_B_16_Weights.IMAGENET1K_SWAG_LINEAR_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=[224]
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]
.