swin-tiny-patch4-window7-224#

Use Case and High-Level Description#

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.

Specification#

Metric

Value

Type

Classification

GFlops

9.0280

MParams

28.8173

Source framework

PyTorch*

Accuracy#

Metric

Value

Top 1

81.38%

Top 5

95.51%

Input#

Original Model#

Image, name: input, shape: 1, 3, 224, 224, format: B, C, H, W, where:

  • B - batch size

  • C - number of channels

  • H - image height

  • W - image width

Expected color order: RGB. Mean values - [123.675, 116.28, 103.53], scale values - [58.395, 57.12, 57.375].

Converted Model#

Image, name: input, shape: 1, 3, 224, 224, format: B, C, H, W, where:

  • B - batch size

  • C - number of channels

  • H - image height

  • W - image width

Expected color order: BGR.

Output#

Original Model#

Object classifier according to ImageNet classes, name: probs, shape: 1, 1000, output data format is B, C, where:

  • B - batch size

  • C - predicted probabilities for each class in [0, 1] range

Converted Model#

Object classifier according to ImageNet classes, name: probs, shape: 1, 1000, output data format is B, C, where:

  • B - batch size

  • C - predicted probabilities for each class in [0, 1] range

Download a Model and Convert it into OpenVINO™ IR Format#

You can download models and if necessary convert them into OpenVINO™ IR format using the Model Downloader and other automation tools as shown in the examples below.

An example of using the Model Downloader:

omz_downloader --name <model_name>

An example of using the Model Converter:

omz_converter --name <model_name>

Demo usage#

The model can be used in the following demos provided by the Open Model Zoo to show its capabilities: