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.

## Specification

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

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

An example of using the Model Converter:

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