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ssd_mobilenet_v1_fpn_coco

Use Case and High-Level Description

MobileNetV1 FPN is used for object detection. For details, see the paper.

Specification

Metric

Value

Type

Detection

GFLOPs

123.309

MParams

36.188

Source framework

TensorFlow*

Accuracy

Metric

Value

coco_precision

35.5453%

Input

Original Model

Image, name: image_tensor, shape: 1, 640, 640, 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, 640, 640, 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 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 the following 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 an 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 - ID of the predicted class

  • conf - confidence for the predicted class in range [1, 91], mapping to class names provided in <omz_dir>/data/dataset_classes/coco_91cl.txt file.

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

  • (x_max, y_max) - coordinates of the bottom right bounding box corner (coordinates stored in normalized format, in 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:

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