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instance-segmentation-security-1039

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 EfficientNet-B2 backbone, light-weight FPN, RPN, detection and segmentation heads.

Example

Specification

Metric

Value

COCO val2017 box AP

32.9%

COCO val2017 mask AP

28.6%

Max objects to detect

100

GFlops

13.9672

MParams

10.5674

Source framework

PyTorch*

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

Inputs

Image, name: image, shape: 1, 3, 480, 480 in the format 1, C, H, W, where:

  • C - number of channels

  • H - image height

  • W - image width

The expected channel order is BGR

Outputs

Model has outputs with dynamic shapes.

  1. Name: labels, shape: -1 - Contiguous integer class ID for every detected object.

  2. Name: boxes, shape: -1, 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: -1, 14, 14 - Segmentation heatmaps for every output bounding box.

Training Pipeline

The OpenVINO Training Extensions provide a training pipeline, allowing to fine-tune the model on custom dataset.

Demo usage

The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:

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