This model is an instance segmentation network for 80 classes of objects. It is a Cascade mask R-CNN with ResNet101 backbone and deformable convolutions, FPN, RPN, detection and segmentation heads.
| Metric | Value |
|---|---|
| MS COCO val2017 box AP (max short side 800, max long side 1344) | 45.8% |
| MS COCO val2017 mask AP (max short side 800, max long side 1344) | 39.7% |
| MS COCO val2017 box AP (max height 800, max width 1344) | 43.55% |
| MS COCO val2017 mask AP (max height 800, max width 1344) | 38.14% |
| Max objects to detect | 100 |
| GFlops | 828.6324 |
| MParams | 101.236 |
| Source framework | PyTorch* |
Average Precision (AP) is defined and measured according to standard MS COCO evaluation procedure.
- name:
image, shape: [1x3x800x1344] - An input image in the format [1xCxHxW]. The expected channel order is BGR.
- name:
labels, shape: [100] - Contiguous integer class ID for every detected object. - 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]. - name:
masks, shape: [100, 28, 28] - Segmentation heatmaps for every output bounding box.
[*] Other names and brands may be claimed as the property of others.