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mask-rcnn-edge-agreement-loss

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Reference implementation of "Faster Training of Mask R-CNN by Focusing on Instance Boundaries"

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# Faster Training of Mask R-CNN by Focusing on Instance Boundaries

![Instance Mask Visualizations](https://github.com/FlashTek/mask-rcnn-edge-agreement-loss/raw/master/instance_mask_visualizations.jpg)

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.

Adding the new auxillary task can be done using this simple network head:
<p align="center">
    <img src="https://github.com/FlashTek/mask-rcnn-edge-agreement-loss/raw/master/edge_agreement_head_small.png" alt="Architecture" height="250">
</p>

The choice of the edge detection filter influences the convergence speed up; as written in the paper, the best results were obtained using the `Sobel` filter.
<p align="center">
    <img src="https://github.com/FlashTek/mask-rcnn-edge-agreement-loss/raw/master/mask_loss.jpg" alt="Loss curves" height="350">
</p>
If you like this work and want to use this in your work or research, please cite:

@article{DBLP:journals/corr/abs-1809-07069,
    author    = {Roland S. Zimmermann and
                Julien N. Siems},
    title     = {Faster Training of Mask {R-CNN} by Focusing on Instance Boundaries},
    journal   = {CoRR},
    volume    = {abs/1809.07069},
    year      = {2018},
    url       = {http://arxiv.org/abs/1809.07069},
    archivePrefix = {arXiv},
    eprint    = {1809.07069},
    timestamp = {Fri, 05 Oct 2018 11:34:52 +0200},
    biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1809-07069},
    bibsource = {dblp computer science bibliography, https://dblp.org}
    }

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