mx-maskrcnn-master

  我们提出了一个简单、灵活和通用的对象实例分割框架。我们的方法能有效检测图像中的对象，同时为每个实例生成高质量的 segmentation mask。这种被称为 Mask R-CNN 的方法通过添加用于预测 object mask 的分支来扩展 Faster R-CNN，该分支与用于边界框识别的现有分支并行。Mask R-CNN 训练简单，只需在以 5fps 运行的 Faster R-CNN 之上增加一个较小的 overhead。此外，Mask R-CNN 很容易推广到其他任务，例如它可以允许同一个框架中进行姿态估计。我们在 COCO 系列挑战的三个轨道任务中均取得了最佳成果，包括实例分割、边界对象检测和人关键点检测。没有任何 tricks，Mask R-CNN 的表现优于所有现有的单一模型取得的成绩，包括 COCO 2016 挑战赛的冠军。(Mask R-CNN code by HeKaiming)

mx-maskrcnn-master, 0 , 2018-02-28
mx-maskrcnn-master\.gitignore, 988 , 2018-02-28
mx-maskrcnn-master\.gitmodules, 103 , 2018-02-28
mx-maskrcnn-master\LICENSE, 11357 , 2018-02-28
mx-maskrcnn-master\Makefile, 221 , 2018-02-28
mx-maskrcnn-master\README.md, 5451 , 2018-02-28
mx-maskrcnn-master\data, 0 , 2018-02-28
mx-maskrcnn-master\data\cityscape, 0 , 2018-02-28
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mx-maskrcnn-master\data\cityscape\imglists\train.lst, 412545 , 2018-02-28
mx-maskrcnn-master\data\cityscape\imglists\val.lst, 67790 , 2018-02-28
mx-maskrcnn-master\demo_mask.py, 2115 , 2018-02-28
mx-maskrcnn-master\eval_maskrcnn.py, 2113 , 2018-02-28
mx-maskrcnn-master\figures, 0 , 2018-02-28
mx-maskrcnn-master\figures\maskrcnn_result.png, 900697 , 2018-02-28
mx-maskrcnn-master\figures\test.jpg, 40967 , 2018-02-28
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mx-maskrcnn-master\rcnn, 0 , 2018-02-28
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mx-maskrcnn-master\rcnn\PY_OP\mask_roi.py, 2240 , 2018-02-28
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mx-maskrcnn-master\rcnn\__init__.py, 0 , 2018-02-28
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mx-maskrcnn-master\rcnn\core, 0 , 2018-02-28
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mx-maskrcnn-master\rcnn\cython, 0 , 2018-02-28
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mx-maskrcnn-master\rcnn\tools\test_maskrcnn.py, 4730 , 2018-02-28
mx-maskrcnn-master\rcnn\tools\test_rpn.py, 4318 , 2018-02-28
mx-maskrcnn-master\rcnn\tools\train_maskrcnn.py, 9777 , 2018-02-28
mx-maskrcnn-master\rcnn\tools\train_rpn.py, 9360 , 2018-02-28
mx-maskrcnn-master\rcnn\utils, 0 , 2018-02-28
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mx-maskrcnn-master\scripts, 0 , 2018-02-28
mx-maskrcnn-master\scripts\demo.sh, 509 , 2018-02-28
mx-maskrcnn-master\scripts\demo_single_image.sh, 432 , 2018-02-28