MaskRCNN TF2 checkpoint (AMP)

Model Overview

Mask R-CNN is a convolution based network for object instance segmentation. This implementation provides 1.3x faster training while maintaining target accuracy.

Model Architecture

Mask R-CNN builds on top of Faster R-CNN adding a mask head for the task of image segmentation.

The architecture consists of the following:

• ResNet-50 backbone with Feature Pyramid Network (FPN)
• Region proposal network (RPN) head
• RoI Align
• Bounding and classification box head
• Mask head

Figure 1. Diagram of Mask R-CNN framework from original paper

Training

This model was trained using script available on NGC and in GitHub repo.

Dataset

The following datasets were used to train this model:

• COCO 2017 - Dataset for large-scale object detection, segmentation and captioning.

Performance

Performance numbers for this model are available in NGC.

References

• Original paper
• NVIDIA model implementation in NGC
• NVIDIA model implementation on GitHub

License

This model was trained using open-source software available in Deep Learning Examples repository. For terms of use, please refer to the license of the script and the datasets the model was derived from.