# shufflenet-v2-x1.0¶

## Use Case and High-Level Description¶

Shufflenet V2 x1.0 is image classification model pre-trained on ImageNet dataset. This is PyTorch* implementation based on architecture described in paper “ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design” in TorchVision package (see here).

The model input is a blob that consists of a single image of 1, 3, 224, 224 in RGB order.

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

Metric

Value

Type

Classification

GFLOPs

0.2957

MParams

2.2705

Source framework

PyTorch*

Metric

Value

Top 1

69.36%

Top 5

88.32%

## Input¶

### Original model¶

Image, name - input, shape - 1, 3, 224, 224, format is B, C, H, W, where:

• B - batch size

• C - channel

• H - height

• W - width

Channel order is RGB. Mean values - [123.675, 116.28, 103.53], scale values - [58.395, 57.12, 57.375].

### Converted model¶

Image, name - input, 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 - output, 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¶

The converted model has the same parameters as the original model.

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