Use Case and High-Level Description

This model is an instance segmentation network for 80 classes of objects. Mask R-CNN with Oct0.5ResNet50 backbone, FPN, light-weight RPN, SERes detection head, and dual attention segmentation head.

Example

Specification

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

Performance

Inputs

1. Name: im_data, shape: [1x3x480x480] - An input image in the format [1xCxHxW]. The expected channel order is BGR.
2. Name: im_info, shape: [1x3] - Image information: processed image height, processed image width and processed image scale with respect to the original image resolution.

Outputs

1. Name: classes, shape: [100, ] - Contiguous integer class ID for every detected object, 0 for background, that is, for no object
2. Name: scores: shape: [100, ] - Detection confidence scores in the range [0, 1] for every object
3. Name: boxes, shape: [100, 4] - Bounding boxes around every detected objects in the (top_left_x, top_left_y, bottom_right_x, bottom_right_y) format
4. Name: raw_masks, shape: [100, 81, 14, 14] - Segmentation heatmaps for all classes for every output bounding box

Legal Information

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