# levit-128s¶

## Use Case and High-Level Description¶

The levit-128s model is one of the LeViT models family: a hybrid neural network for fast inference image classification. The model is pre-trained on the ImageNet dataset. LeViT-128s model is a small LeViT variant that has 128 channels on input of the transformer stage and 2, 3 and 4 number of pairs of Attention and MLP blocks at 1, 2 and 3 model stages respectively.

The model input is a blob that consists of a single image of 1, 3, 224, 224 in RGB order.

The model output is typical object classifier for the 1000 different classifications matching with those in the ImageNet database.

For details see repository and paper.

Metric

Value

Type

Classification

GFLOPs

0.6177

MParams

8.2199

Source framework

PyTorch*

Metric

Value

Top 1

76.54%

Top 5

92.85%

## Input¶

### Original model¶

Image, name - image, shape - 1, 3, 224, 224, format is B, C, H, W, where:

• B - batch size

• C - channel

• H - height

• W - width

Channel order is RGB. Mean values - [123.675,116.28,103.53], scale values - [58.395, 57.12, 57.375].

### Converted model¶

Image, name - image, shape - 1, 3, 224, 224, format is B, C, H, W, where:

• B - batch size

• C - channel

• H - height

• W - width

Channel order is 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 logits format

### 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 logits 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.

omz_downloader --name <model_name>
omz_converter --name <model_name>