# squeezenet1.1¶

## Use Case and High-Level Description¶

The squeezenet1.1 updated version of the SqueezeNet topology. It is designed to perform image classification. It requires 2.4x less computation than SqueezeNet v1.0 without diminishing accuracy. The SqueezeNet models have been pre-trained on the ImageNet image database. For details about this family of models, check out the repository.

The model input is a blob that consists of a single image of 1, 3, 227, 227 in BGR order. The BGR mean values need to be subtracted as follows: [104, 117, 123] before passing the image blob into the network.

The model output for squeezenet1.1 is the typical object classifier output for the 1000 different classifications matching those in the ImageNet database.

Metric

Value

Type

Classification

GFLOPs

0.785

MParams

1.236

Source framework

Caffe*

Metric

Value

Top 1

58.382%

Top 5

81%

## Input¶

### Original model¶

Image, name - data, shape - 1, 3, 227, 227, format is B, C, H, W, where:

• B - batch size

• C - channel

• H - height

• W - width

Channel order is BGR. Mean values - [104, 117, 123]

### Converted model¶

Image, name - data, shape - 1, 3, 227, 227, 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 - prob, 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 - prob, 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: