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zimmerrol · Apr 10, 2019 · eea0373 · eea0373
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 # Faster Training of Mask R-CNN by Focusing on Instance Boundaries
 
+![Instance Mask Visualizations](instance_mask_visualizations.jpg)
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 This is an implementation of the improved training scheme [Faster Training of Mask R-CNN by Focusing on Instance Boundaries](https://arxiv.org/abs/1809.07069) on Python 3, Keras, and TensorFlow. The code is an extension of the existing implementation of [Mask R-CNN by Matterport](https://github.com/matterport/Mask_RCNN). It can be seen as a fork of the original repository based on [commit cbff80f](https://github.com/matterport/Mask_RCNN/commit/cbff80f3e3f653a9eeee43d0d383a0385aba546b). The model generates bounding boxes and segmentation masks for each instance of an object in the image. It's based on Feature Pyramid Network (FPN) and a ResNet101 backbone. The training speed has been increased by introducing an auxiliary objective.
 
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+![Instance Mask Visualizations](edge_agreement_head.png)
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 If you like this work and want to use this in your work or research, please cite:
 
     @article{DBLP:journals/corr/abs-1809-07069,

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