https://github.com/zimmerrol/mask-rcnn-edge-agreement-loss

Reference implementation of "Faster Training of Mask R-CNN by Focusing on Instance Boundaries"
https://github.com/zimmerrol/mask-rcnn-edge-agreement-loss

auxiliary-tasks instance-segmentation mask-rcnn research

Last synced: 18 days ago
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Reference implementation of "Faster Training of Mask R-CNN by Focusing on Instance Boundaries"

• Host: GitHub
• URL: https://github.com/zimmerrol/mask-rcnn-edge-agreement-loss
• Owner: zimmerrol
• License: mit
• Created: 2019-03-30T12:15:47.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2019-04-21T14:06:52.000Z (about 6 years ago)
• Last Synced: 2025-03-30T16:39:18.934Z (about 2 months ago)
• Topics: auxiliary-tasks, instance-segmentation, mask-rcnn, research
• Language: Python
• Homepage: https://arxiv.org/abs/1809.07069
• Size: 15.7 MB
• Stars: 43
• Watchers: 4
• Forks: 8
• Open Issues: 6
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# 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:



    



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.



    



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}

    }