# mobilenet-ssd¶

## Use Case and High-Level Description¶

The mobilenet-ssd model is a Single-Shot multibox Detection (SSD) network intended to perform object detection. This model is implemented using the Caffe* framework. For details about this model, check out the repository.

The model input is a blob that consists of a single image of 1, 3, 300, 300 in BGR order, also like the densenet-121 model. The BGR mean values need to be subtracted as follows: [127.5, 127.5, 127.5] before passing the image blob into the network. In addition, values must be divided by 0.007843.

The model output is a typical vector containing the tracked object data, as previously described.

Metric

Value

Type

Detection

GFLOPs

2.316

MParams

5.783

Source framework

Caffe*

## Accuracy¶

The accuracy results were obtained on test data from VOC2007 dataset.

Metric

Value

mAP

67.00%

## Input¶

### Original model¶

Image, name - prob, shape - 1, 3, 300, 300, format is B, C, H, W, where:

• B - batch size

• C - channel

• H - height

• W - width

Channel order is BGR. Mean values - [127.5, 127.5, 127.5], scale value - 127.5.

### Converted model¶

Image, name - prob, shape - 1, 3, 300, 300, format is B, C, H, W, where:

• B - batch size

• C - channel

• H - height

• W - width

Channel order is BGR

## Output¶

### Original model¶

The array of detection summary info, name - detection_out, shape - 1, 1, 100, 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 (1..20 - PASCAL VOC defined class ids). Mapping to class names provided by <omz_dir>/data/dataset_classes/voc_20cl_bkgr.txt file.

• conf - confidence for the predicted class

• (x_min, y_min) - coordinates of the top left bounding box corner (coordinates are in normalized format, in range [0, 1])

• (x_max, y_max) - coordinates of the bottom right bounding box corner (coordinates are in normalized format, in range [0, 1])

### Converted model¶

The array of detection summary info, name - detection_out, shape - 1, 1, 100, 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 (1..20 - PASCAL VOC defined class ids). Mapping to class names provided by <omz_dir>/data/dataset_classes/voc_20cl_bkgr.txt file.

• conf - confidence for the predicted class

• (x_min, y_min) - coordinates of the top left bounding box corner (coordinates are in normalized format, in range [0, 1])

• (x_max, y_max) - coordinates of the bottom right bounding box corner (coordinates are in normalized format, in range [0, 1])

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