ssd_mobilenet_v2_coco

Use Case and High-Level Description

The ssd_mobilenet_v2_coco model is a Single-Shot multibox Detection (SSD) network intended to perform object detection. The model has been trained from the Common Objects in Context (COCO) image dataset.

The model input is a blob that consists of a single image of 1, 3, 300, 300 in RGB order.

The model output is a typical vector containing the tracked object data, as previously described. Note that the class_id data is now significant and should be used to determine the classification for any detected object.

Specification

Metric Value
Type Detection
GFLOPs 3.775
MParams 16.818
Source framework TensorFlow*

Accuracy

Metric Value
coco_precision 24.9452%

Input

Note that original model expects image in RGB format, converted model - in BGR format.

Original model

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

  • B - batch size
  • H - height
  • W - width
  • C - channel

Channel order is RGB.

Converted model

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

  • B - batch size
  • H - height
  • W - width
  • C - channel

Channel order is BGR.

Output

NOTE output format changes after Model Optimizer conversion. To find detailed explanation of changes, go to Model Optimizer development guide

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 format [y_min, x_min, y_max, x_max], where (x_min, y_min) are coordinates top left corner, (x_max, y_max) are coordinates 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 - 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 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 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])

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>

Legal Information

The original model is distributed under the Apache License, Version 2.0. A copy of the license is provided in APACHE-2.0-TF-Models.txt.