The swin-tiny-patch4-window7-224
model is a tiny
version of the Swin Transformer image classification models pre-trained on ImageNet dataset. Swin Transformer is Hierarchical Vision Transformer whose representation is computed with shifted windows. Each patch is treated as a token with size of 4 and its feature is set as a concatenation of the raw pixel RGB values. The model has 7 patches in each window. Stages of tiny version of model have 2, 2, 6, 2 layers respectively. Number of channels of the hidden layers in the first stage for tiny variant is 96.
More details provided in the paper and repository.
Metric | Value |
---|---|
Type | Classification |
GFlops | 9.0280 |
MParams | 28.8173 |
Source framework | PyTorch* |
Metric | Value |
---|---|
Top 1 | 81.38% |
Top 5 | 95.51% |
Image, name: input
, shape: 1, 3, 224, 224
, format: B, C, H, W
, where:
B
- batch sizeC
- number of channelsH
- image heightW
- image width
Expected color order: RGB
.
Mean values - [123.675, 116.28, 103.53], scale values - [58.395, 57.12, 57.375].
Image, name: input
, shape: 1, 3, 224, 224
, format: B, C, H, W
, where:
B
- batch sizeC
- number of channelsH
- image heightW
- image width
Expected color order: BGR
.
Object classifier according to ImageNet classes, name: probs
, shape: 1, 1000
, output data format is B, C
, where:
B
- batch sizeC
- predicted probabilities for each class in [0, 1] range
Object classifier according to ImageNet classes, name: probs
, shape: 1, 1000
, output data format is B, C
, where:
B
- batch sizeC
- predicted probabilities for each class in [0, 1] range
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The original model is distributed under the
Apache License, Version 2.0.
A copy of the license is provided in <omz_dir>/models/public/licenses/APACHE-2.0-PyTorch-Image-Models.txt
.