instance-segmentation-security-0002

Use Case and High-Level Description

This model is an instance segmentation network for 80 classes of objects. It is a Mask R-CNN with ResNet50 backbone, FPN, RPN, detection and segmentation heads.

Example

Specification

Metric Value
MS COCO val2017 box AP (max short side 768, max long side 1024) 40.8%
MS COCO val2017 mask AP (max short side 768, max long side 1024) 36.9%
MS COCO val2017 box AP (max height 768, max width 1024) 39.86%
MS COCO val2017 mask AP (max height 768, max width 1024) 36.44%
Max objects to detect 100
GFlops 423.0842
MParams 48.3732
Source framework PyTorch*

Average Precision (AP) is defined and measured according to standard MS COCO evaluation procedure.

Inputs

  1. name: image , shape: [1x3x768x1024] - An input image in the format [1xCxHxW]. The expected channel order is BGR.

Outputs

  1. name: labels, shape: [100] - Contiguous integer class ID for every detected object.
  2. name: boxes, shape: [100, 5] - Bounding boxes around every detected objects in (top_left_x, top_left_y, bottom_right_x, bottom_right_y) format and its confidence score in range [0, 1].
  3. name: masks, shape: [100, 28, 28] - Segmentation heatmaps for every output bounding box.

Legal Information

[*] Other names and brands may be claimed as the property of others.