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We first use a traditional automatic cyclone identification method to construct a trainable labeled dataset, then the Mask R-CNN was trained on this dataset. And finally trained network was again used on the same reanalysis data to identify additional cyclones.

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a-mask-r-cnn-model-for-identifying-cyclones's Introduction

Mask R-CNN for Identifying Cyclones

This is an application of Mask R-CNN . The source code is packed into a compressed package of Mask_RCNN-master_source_code.zip. More details can be obtained here, https://github.com/matterport/Mask_RCNN.

We use the Mask R-CNN model for identifying extratropical cyclones.

Train a new model starting from pre-trained COCO weights

python3 Mask_RCNN-master/cyclone/maskrcnn_cyclone.py train --weight=coco

Continue training a model that you had trained earlier

python3 Mask_RCNN-master/cyclone/maskrcnn_cyclone.py train --weight=last

Run trained Mask R-CNN model

python3 Mask_RCNN-master/cyclone/maskrcnn_cyclone.py test --image=[path] --weight=last

a-mask-r-cnn-model-for-identifying-cyclones's People

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