Use Case and High-Level Description

The ssd_resnet50_v1_fpn_coco model is a SSD FPN object detection architecture based on ResNet-50. The model has been trained from the Common Objects in Context (COCO) image dataset. For details see the repository and paper.



Metric Value
Type Detection
GFLOPs 178.6807
MParams 56.9326
Source framework TensorFlow*


Metric Value
coco_precision 38.4557%



Original model

Image, name - image_tensor, shape - [1x640x640x3], format -[BxHxWxC] where:

Expected color order - RGB.

Converted model

Image, name - image_tensor, shape - [1x3x640x640], format is [BxCxHxW] where:

Expected color order - BGR.


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 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 - detection_out, shape - [1x1xNx7], 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.