vgg16¶

Use Case and High-Level Description¶

The vgg16 model is one of the vgg models designed to perform image classification in Caffe*format.

The model input is a blob that consists of a single image of 1, 3, 224, 224 in BGR order. The BGR mean values need to be subtracted as follows: [103.939, 116.779, 123.68] before passing the image blob into the network.

The model output for vgg16 is the typical object classifier output for the 1000 different classifications matching those in the ImageNet database.

Metric

Value

Type

Classification

GFLOPs

30.974

MParams

138.358

Source framework

Caffe*

Metric

Value

Top 1

70.968%

Top 5

89.878%

Input¶

Original mode¶

Image, name - data, 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. Mean values - [103.939, 116.779, 123.68]

Converted model¶

Image, name - data, 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 - 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

python3 <omz_dir>/tools/downloader/downloader.py --name <model_name>
python3 <omz_dir>/tools/downloader/converter.py --name <model_name>