## Use Case and High-level Description¶

This is a MobileNetV2 + SSD-based vehicle and (Chinese) license plate detector for the “Barrier” use case.

## Specification¶

Metric

Value

Mean Average Precision (mAP)

99.52%

AP vehicles

99.90%

AP plates

99.13%

Car pose

Front facing cars

Min plate width

96 pixels

Max objects to detect

200

GFlops

0.271

MParams

0.547

Source framework

TensorFlow*

Average Precision (AP) is defined as an area under the precision/recall curve. Validation dataset is BIT-Vehicle.

## Input¶

### Original Model¶

An input image, name: input, shape: 1, 256, 256, 3, format: B, H, W, C, where:

• B - batch size

• H - image height

• W - image width

• C - number of channels

Expected color order: RGB. Mean values: [127.5, 127.5, 127.5], scale factor for each channel: 127.5

### Converted Model¶

An input image, name: input, shape: 1, 3, 256, 256, format B, C, H, W, where:

• B - batch size

• C - number of channels

• H - image height

• W - image width

Expected color order is BGR.

## Output¶

### Original Model¶

The net outputs a blob with the shape: 1, 1, 200, 7 in the format 1, 1, N, 7, where N is the number of detected bounding boxes. For each detection, the description has the format: [image_id, label, conf, x_min, y_min, x_max, y_max], where:

• image_id - ID of the image in the batch

• label - predicted class ID

• conf - confidence for the predicted class

• (x_min, y_min) - coordinates of the top left bounding box corner

• (x_max, y_max) - coordinates of the bottom right bounding box corner.

### Converted Model¶

The net outputs a blob with the shape: 1, 1, 200, 7 in the format 1, 1, N, 7, where N is the number of detected bounding boxes. For each detection, the description has the format: [image_id, label, conf, x_min, y_min, x_max, y_max], where:

• image_id - ID of the image in the batch

• label - predicted class ID

• conf - confidence for the predicted class

• (x_min, y_min) - coordinates of the top left bounding box corner

• (x_max, y_max) - coordinates of the bottom right bounding box corner.

## 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:

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>