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.

Specification

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

Download a Model and Convert it into Inference Engine Format

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 Downloader: