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.
| Metric | Value |
|---|---|
| MS COCO val2017 box AP | 32.99% |
| MS COCO val2017 mask AP | 28.37% |
| Max objects to detect | 100 |
| GFlops | 30.146 |
| MParams | 26.690 |
| Source framework | PyTorch* |
Average Precision (AP) is defined and measured according to standard MS COCO evaluation procedure.
- name:
im_data, shape: [1x3x480x480] - An input image in the format [1xCxHxW]. The expected channel order is BGR. - name:
im_info, shape: [1x3] - Image information: processed image height, processed image width and processed image scale w.r.t. the original image resolution.
- name:
classes, shape: [100, ] - Contiguous integer class ID for every detected object, '0' for background, i.e. no object. - name:
scores: shape: [100, ] - Detection confidence scores in range [0, 1] for every object. - 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. - name:
raw_masks, shape: [100, 81, 14, 14] - Segmentation heatmaps for all classes for every output bounding box.
[*] Other names and brands may be claimed as the property of others.