# octave-densenet-121-0.125¶

## Use Case and High-Level Description¶

The octave-densenet-121-0.125 model is a modification of  <https://arxiv.org/abs/1608.06993>__ with Octave convolutions from Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution with alpha=0.125. Like the original model, this model is designed for image classification. For details about family of Octave Convolution models, check out the repository.

The model input is a blob that consists of a single image of 1, 3, 224, 224 in RGB order. Before passing the image blob into the network, subtract RGB mean values as follows: [124, 117, 104]. In addition, values must be divided by 0.0167.

The model output for octave-densenet-121-0.125 is a typical object-classifier output for 1000 different classifications matching those in the ImageNet database.

Metric

Value

Type

Classification

GFLOPs

4.883

MParams

7.977

Source framework

MXNet*

Metric

Value

Top 1

76.066%

Top 5

93.044%

## Input¶

### Original Model¶

Image, name: data, shape: 1, 3, 224, 224, format: B, C, H, W, where:

• B - batch size

• C - channel

• H - height

• W - width

Channel order is RGB. Mean values: [124, 117, 104], scale value: 59.880239521.

### Converted Model¶

Image, name: data, shape: 1, 3, 224, 224, format: 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>