## Use Case and High-Level Description¶

Inception v3 is image classification model pre-trained on ImageNet dataset. This PyTorch* implementation of architecture described in the paper “Rethinking the Inception Architecture for Computer Vision” in TorchVision package (see here).

The model input is a blob that consists of a single image of 1, 3, 299, 299 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

11.469

MParams

23.817

Source framework

PyTorch*

Metric

Value

Top 1

77.69%

Top 5

93.7%

## Input¶

### Original model¶

Image, name - data, shape - 1, 3, 299, 299, format - B, C, H, W, where:

• B - batch size

• C - number of channels

• H - image height

• W - image width

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

### Converted model¶

Image, name - data, shape - 1, 3, 299, 299, format - B, C, H, W, where:

• B - batch size

• C - number of channels

• H - image height

• W - image width

Expected color order - BGR.

## Output¶

Object classifier according to ImageNet classes, name - prob, shape - 1, 1000 in B, C format, where:

• B - batch size

• C - vector of probabilities for each class in [0, 1] range

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.

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

An example of using the Model Converter:

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

## Use Case and High-Level Description¶

Inception v3 is image classification model pre-trained on ImageNet dataset. This PyTorch* implementation of architecture described in the paper “Rethinking the Inception Architecture for Computer Vision” in TorchVision package (see here).

The model input is a blob that consists of a single image of 1, 3, 299, 299 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

11.469

MParams

23.817

Source framework

PyTorch*

Metric

Value

Top 1

77.69%

Top 5

93.7%

## Input¶

### Original model¶

Image, name - data, shape - 1, 3, 299, 299, format - B, C, H, W, where:

• B - batch size

• C - number of channels

• H - image height

• W - image width

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

### Converted model¶

Image, name - data, shape - 1, 3, 299, 299, format - B, C, H, W, where:

• B - batch size

• C - number of channels

• H - image height

• W - image width

Expected color order - BGR.

## Output¶

Object classifier according to ImageNet classes, name - prob, shape - 1, 1000 in B, C format, where:

• B - batch size

• C - vector of probabilities for each class in [0, 1] range

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.

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

An example of using the Model Converter:

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

## Legal Information¶

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