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mask_rcnn's Introduction

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mask rcnn具体原理见Matterport Mask RCNN

具体修改部分

增加C1特征层至mrcnn分支中，以应对小物体如车辆的检测

修改mrcnnfe分类损失函数为focal loss损失,解决训练样本分布不均衡问题

修改训练数据生成部分代码，使用keras.utils.Sequence代替python迭代器，更安全，避免加载相同数据

效果

汽车汽车

飞机

港口

网球场

数据集（DOTA-v1.5)

下载地址

请自行转换格式

使用该项目

请在train.py中配置相关参数，如何配置请参考(https://github.com/matterport/Mask_RCNN/blob/master/samples/shapes/train_shapes.ipynb)

请按格式配置数据集，参考multiobjectdataset下的文件

运行train.py

focal loss使用

focal loss下降缓慢，建议先使用交叉熵损失，后使用focal loss

默认使用交叉熵损失，若要使用focal loss,请取消注释model.py的1117行并修改对应的alpha值，取消注释1121行，注释1114行

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