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.

## Specification

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])

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 Converter:

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