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: