Use Case and High-Level Description

The ssd_mobilenet_v1_coco model is a Single-Shot multibox Detection (SSD) network intended to perform object detection. The difference bewteen this model and the mobilenet-ssd is that there the mobilenet-ssd can only detect face, the ssd_mobilenet_v1_coco model can detect objects.



Metric Value
Type Detection
GFLOPs 2.494
MParams 6.807
Source framework TensorFlow*


Metric Value
coco_precision 23.3212%



Original model

Image, name - image_tensor, shape - [1x300x300x3], format [BxHxWxC], where:

Expected color order - RGB.

Converted model

Image, name - image_tensor, shape - [1x3x300x300], format [BxCxHxW], where:

Expected color order: BGR.


Original model

  1. Classifier, name - detection_classes, contains predicted bounding boxes classes in range [1, 91]. The model was trained on Microsoft* COCO dataset version with 90 categories of object.
  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 - DetectionOutput, shape - [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:

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-TensorFlow.txt.