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.

Example

Specification

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

Accuracy

Metric	Value
coco_precision	38.4557%

Performance

Input

Original model

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

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

Expected color order - RGB.

Converted model

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

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

Expected color order - 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 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:

• image_id - ID of the image in the batch
• label - predicted class ID
• 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])

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.