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.



Metric Value
COCO val2017 box AP 35.0%
COCO val2017 mask AP 31.2%
Max objects to detect 100
GFlops 29.334
MParams 13.5673
Source framework PyTorch*

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


Image, name: image, shape: 1, 3, 608, 608 in the format 1, C, H, W, where:

  • C - number of channels
  • H - image height
  • W - image width

The expected channel order is BGR


  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.

Training Pipeline

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

Legal Information

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