instance-segmentation-security-0049

Use case and High-level description

This model is an instance segmentation network for 80 classes of objects. It is a Mask-RCNN-like model with ResNet50 backbone, Feature Pyramid Networks block for feature maps refinement and relatively light segmentation head.

Example

instance-segmentation-security-0049.png

Specification

Metric Value
MS COCO val2017 box AP (max short side 320, max long side 480) 30.4%
MS COCO val2017 mask AP (max short side 320, max long side 480) 26.8%
MS COCO val2017 box AP (max height 320, max width 480) 29.8%
MS COCO val2017 mask AP (max height 320, max width 480) 26.3%
Max objects to detect 100
GFlops 56.433
MParams 44.920
Source framework PyTorch*

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

Performance

Inputs

  1. name: im_data , shape: [1x3x320x480] - An input image in the format [1xCxHxW]. The expected channel order is BGR.
  1. name: im_info, shape: [1x3] - Image information: processed image height, processed image width and processed image scale w.r.t. the original image resolution.

Outputs

  1. name: classes, shape: [100, ] - Contiguous integer class ID for every detected object, '0' for background, i.e. no object.
  1. name: scores: shape: [100, ] - Detection confidence scores in range [0, 1] for every object.
  1. name: boxes, shape: [100, 4] - Bounding boxes around every detected objects in (top_left_x, top_left_y, bottom_right_x, bottom_right_y) format.
  1. name: raw_masks, shape: [100, 81, 28, 28] - Segmentation heatmaps for all classes for every output bounding box.

Legal Information

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