GitHub - karensnh/Mask-R-CNN-for-classification-of-maerl: Application of Mask R-CNN for classification of live maerl in imagery of the seabed taken using drop-down cameras. Notebooks to train the model and apply model in inference mode.

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Application of Mask R-CNN for classification of live maerl in imagery of the seabed taken using drop-down cameras. Notebooks to train the model and apply model in inference mode.

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Maerl_trainingMRCNN_augmentation_inference_share.ipynb		Maerl_trainingMRCNN_augmentation_inference_share.ipynb
Maerl_trainingMRCNN_augmentation_share.ipynb		Maerl_trainingMRCNN_augmentation_share.ipynb
maerl_tiny_share.py		maerl_tiny_share.py
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Jupyter notebooks used to train and apply a mask R-cnn model  to classify live maerl in imagery of the sea-bed obtained from drop-down cameras. 
Originally based on https://github.com/matterport/Mask_RCNN Also see this repo for more examples (in samples folder) and explanation of config parameters.
Platform used in this instance : JupyterLab on MAGEOHub (massive online GPU for Earth Observation at Plymouth Marine Lab).
This is a binary classification of live maerl/ not live maerl. 
Released under open government licence http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
Many thanks to Dan Simms (University of Cranfield) for code examples and Stephen Goult & Dan Clewley  (Plymouth Marine Laboratory/ NEODAAS) for troubleshooting suggestions and use of MAGEO.
Keen to hear about other applications - [email protected]

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Application of Mask R-CNN for classification of live maerl in imagery of the seabed taken using drop-down cameras. Notebooks to train the model and apply model in inference mode.

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