ssdlite_mobilenet_v2

Use Case and High-Level Description

The ssdlite_mobilenet_v2 model is used for object detection. For details, see the paper, MobileNetV2: Inverted Residuals and Linear Bottlenecks.

Specification

Metric

Value

Type

Detection

GFLOPs

1.525

MParams

4.475

Source framework

TensorFlow*

Accuracy

Metric

Value

coco_precision

24.2946%

Input

Original Model

Image, name: image_tensor, shape: 1, 300, 300, 3, format: B, H, W, C, where:

  • B - batch size

  • H - image height

  • W - image width

  • C - number of channels

Expected color order: RGB.

Converted Model

Image, name: image_tensor, shape: 1, 300, 300, 3, format: B, H, W, C, where:

  • B - batch size

  • H - image height

  • W - image width

  • C - number of channels

Expected color order: BGR.

Output

Original Model

  1. Classifier, name: detection_classes. Contains predicted bounding-boxes classes in a range [1, 91]. The model was trained on Common Objects in Context (COCO) dataset version with 91 categories of object, 0 class is for background. Mapping to class names provided in <omz_dir>/data/dataset_classes/coco_91cl_bkgr.txt file.

  2. Probability, name: detection_scores. Contains probability of detected bounding boxes.

  3. Detection box, name: detection_boxes. Contains detection boxes coordinates in format [y_min, x_min, y_max, x_max], where (x_min, y_min) are coordinates of the top left corner, (x_max, y_max) are coordinates of the right bottom corner. Coordinates are rescaled to input image size.

  4. Detections number, name: num_detections. Contains the number of predicted detection boxes.

Converted Model

The array of summary detection information, name: DetectionOutput, 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 in range [1, 91], mapping to class names provided in <omz_dir>/data/dataset_classes/coco_91cl_bkgr.txt file

  • conf - confidence for the predicted class

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

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

Download a Model and Convert it into OpenVINO™ IR Format

You can download models and if necessary convert them into OpenVINO™ IR format using the Model Downloader and other automation tools as shown in the examples below.

An example of using the Model Downloader:

omz_downloader --name <model_name>

An example of using the Model Converter:

omz_converter --name <model_name>

Demo usage

The model can be used in the following demos provided by the Open Model Zoo to show its capabilities: