Mask R-CNN is a convolution based network for object instance segmentation. This implementation provides 1.3x faster training while maintaining target accuracy.
Mask R-CNN builds on top of FasterRCNN adding an additional mask head for the task of image segmentation.
The architecture consists of following:
- R-50 backbone with FPN
- RPN head
- RoI ALign
- Bounding and classification box head
- Mask head
The following datasets were used to train this model:
- COCO 2017 - Dataset for large-scale object detection, segmentation and captioning.
Performance numbers for this model are available in NGC.