# alexnet¶

## Use Case and High-Level Description¶

The alexnet model is designed to perform image classification. Just like other common classification models, the alexnet model has been pre-trained on the ImageNet image database. For details about this model, check out the paper.

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 alexnet is the usual object classifier output for the 1000 different classifications matching those in the ImageNet database.

Metric

Value

Type

Classification

GFLOPs

1.5

MParams

60.965

Source framework

Caffe*

Metric

Value

Top 1

56.598%

Top 5

79.812%

## 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 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>