instance-segmentation-security-0091

Use Case and High-Level Description

This model is an instance segmentation network for 80 classes of objects. It is a Cascade mask R-CNN with ResNet101 backbone and deformable convolutions, FPN, RPN, detection and segmentation heads.

Example

Specification

Metric Value
MS COCO val2017 box AP (max short side 800, max long side 1344) 45.8%
MS COCO val2017 mask AP (max short side 800, max long side 1344) 39.7%
MS COCO val2017 box AP (max height 800, max width 1344) 43.55%
MS COCO val2017 mask AP (max height 800, max width 1344) 38.14%
Max objects to detect 100
GFlops 828.6324
MParams 101.236
Source framework PyTorch*

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

Inputs

  1. name: image , shape: [1x3x800x1344] - 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.