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mx-maskrcnn-master

于 2020-06-17 发布
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下载积分: 1 下载次数: 5

代码说明:

说明:  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)

文件列表:

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
mx-maskrcnn-master\data\cityscape\imglists, 0 , 2018-02-28
mx-maskrcnn-master\data\cityscape\imglists\test.lst, 200205 , 2018-02-28
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
mx-maskrcnn-master\incubator-mxnet, 0 , 2018-02-28
mx-maskrcnn-master\rcnn, 0 , 2018-02-28
mx-maskrcnn-master\rcnn\CXX_OP, 0 , 2018-02-28
mx-maskrcnn-master\rcnn\CXX_OP\roi_align-inl.h, 8596 , 2018-02-28
mx-maskrcnn-master\rcnn\CXX_OP\roi_align.cc, 2824 , 2018-02-28
mx-maskrcnn-master\rcnn\CXX_OP\roi_align.cu, 12308 , 2018-02-28
mx-maskrcnn-master\rcnn\CXX_OP\roi_align_v1-inl.h, 15877 , 2018-02-28
mx-maskrcnn-master\rcnn\CXX_OP\roi_align_v1.cc, 3090 , 2018-02-28
mx-maskrcnn-master\rcnn\CXX_OP\roi_align_v1.cu, 446 , 2018-02-28
mx-maskrcnn-master\rcnn\PY_OP, 0 , 2018-02-28
mx-maskrcnn-master\rcnn\PY_OP\__init__.py, 0 , 2018-02-28
mx-maskrcnn-master\rcnn\PY_OP\fpn_roi_pooling.py, 4584 , 2018-02-28
mx-maskrcnn-master\rcnn\PY_OP\mask_output.py, 1971 , 2018-02-28
mx-maskrcnn-master\rcnn\PY_OP\mask_roi.py, 2240 , 2018-02-28
mx-maskrcnn-master\rcnn\PY_OP\proposal_fpn.py, 8149 , 2018-02-28
mx-maskrcnn-master\rcnn\__init__.py, 0 , 2018-02-28
mx-maskrcnn-master\rcnn\config.py, 5104 , 2018-02-28
mx-maskrcnn-master\rcnn\core, 0 , 2018-02-28
mx-maskrcnn-master\rcnn\core\__init__.py, 0 , 2018-02-28
mx-maskrcnn-master\rcnn\core\callback.py, 1710 , 2018-02-28
mx-maskrcnn-master\rcnn\core\loader.py, 24515 , 2018-02-28
mx-maskrcnn-master\rcnn\core\metric.py, 9044 , 2018-02-28
mx-maskrcnn-master\rcnn\core\module.py, 8588 , 2018-02-28
mx-maskrcnn-master\rcnn\core\solver.py, 3136 , 2018-02-28
mx-maskrcnn-master\rcnn\core\tester.py, 13716 , 2018-02-28
mx-maskrcnn-master\rcnn\cython, 0 , 2018-02-28
mx-maskrcnn-master\rcnn\cython\.gitignore, 15 , 2018-02-28
mx-maskrcnn-master\rcnn\cython\__init__.py, 0 , 2018-02-28
mx-maskrcnn-master\rcnn\cython\anchors.pyx, 1185 , 2018-02-28
mx-maskrcnn-master\rcnn\cython\bbox.pyx, 1763 , 2018-02-28
mx-maskrcnn-master\rcnn\cython\cpu_nms.pyx, 2241 , 2018-02-28
mx-maskrcnn-master\rcnn\cython\gpu_nms.hpp, 146 , 2018-02-28
mx-maskrcnn-master\rcnn\cython\gpu_nms.pyx, 1110 , 2018-02-28
mx-maskrcnn-master\rcnn\cython\nms_kernel.cu, 5064 , 2018-02-28
mx-maskrcnn-master\rcnn\cython\setup.py, 5515 , 2018-02-28
mx-maskrcnn-master\rcnn\dataset, 0 , 2018-02-28
mx-maskrcnn-master\rcnn\dataset\__init__.py, 53 , 2018-02-28
mx-maskrcnn-master\rcnn\dataset\cityscape.py, 12991 , 2018-02-28
mx-maskrcnn-master\rcnn\dataset\ds_utils.py, 442 , 2018-02-28
mx-maskrcnn-master\rcnn\dataset\imdb.py, 13205 , 2018-02-28
mx-maskrcnn-master\rcnn\io, 0 , 2018-02-28
mx-maskrcnn-master\rcnn\io\__init__.py, 0 , 2018-02-28
mx-maskrcnn-master\rcnn\io\image.py, 5850 , 2018-02-28
mx-maskrcnn-master\rcnn\io\rcnn.py, 19628 , 2018-02-28
mx-maskrcnn-master\rcnn\io\rpn.py, 10379 , 2018-02-28
mx-maskrcnn-master\rcnn\io\threaded_loader.py, 20199 , 2018-02-28
mx-maskrcnn-master\rcnn\processing, 0 , 2018-02-28
mx-maskrcnn-master\rcnn\processing\__init__.py, 0 , 2018-02-28
mx-maskrcnn-master\rcnn\processing\assign_levels.py, 1221 , 2018-02-28
mx-maskrcnn-master\rcnn\processing\bbox_regression.py, 9983 , 2018-02-28
mx-maskrcnn-master\rcnn\processing\bbox_transform.py, 5023 , 2018-02-28
mx-maskrcnn-master\rcnn\processing\generate_anchor.py, 2443 , 2018-02-28
mx-maskrcnn-master\rcnn\processing\nms.py, 1414 , 2018-02-28
mx-maskrcnn-master\rcnn\pycocotools, 0 , 2018-02-28
mx-maskrcnn-master\rcnn\pycocotools\UPSTREAM_REV, 80 , 2018-02-28
mx-maskrcnn-master\rcnn\pycocotools\__init__.py, 21 , 2018-02-28
mx-maskrcnn-master\rcnn\pycocotools\_mask.pyx, 11430 , 2018-02-28
mx-maskrcnn-master\rcnn\pycocotools\coco.py, 18296 , 2018-02-28
mx-maskrcnn-master\rcnn\pycocotools\cocoeval.py, 23849 , 2018-02-28
mx-maskrcnn-master\rcnn\pycocotools\mask.py, 4570 , 2018-02-28
mx-maskrcnn-master\rcnn\pycocotools\maskApi.c, 8249 , 2018-02-28
mx-maskrcnn-master\rcnn\pycocotools\maskApi.h, 2176 , 2018-02-28
mx-maskrcnn-master\rcnn\pycocotools\setup.py, 579 , 2018-02-28
mx-maskrcnn-master\rcnn\symbol, 0 , 2018-02-28
mx-maskrcnn-master\rcnn\symbol\__init__.py, 30 , 2018-02-28
mx-maskrcnn-master\rcnn\symbol\symbol_mask_fpn.py, 33269 , 2018-02-28
mx-maskrcnn-master\rcnn\tools, 0 , 2018-02-28
mx-maskrcnn-master\rcnn\tools\__init__.py, 0 , 2018-02-28
mx-maskrcnn-master\rcnn\tools\demo_maskrcnn.py, 4732 , 2018-02-28
mx-maskrcnn-master\rcnn\tools\demo_single_image.py, 6421 , 2018-02-28
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
mx-maskrcnn-master\rcnn\utils\__init__.py, 0 , 2018-02-28
mx-maskrcnn-master\rcnn\utils\combine_model.py, 709 , 2018-02-28
mx-maskrcnn-master\rcnn\utils\load_data.py, 1718 , 2018-02-28
mx-maskrcnn-master\rcnn\utils\load_model.py, 1999 , 2018-02-28
mx-maskrcnn-master\rcnn\utils\save_model.py, 762 , 2018-02-28
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

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发表评论

0 个回复

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    2021-04-22 21:08:48下载
    积分:1
  • ATOCP
    此程序为摄影测量的一个小环节,主要功能是求解影像畸变差,可根据相机内参数求解,简单实用(This program is a small part of photogrammetry, the main function is to solve the differential image distortion can be solved according to the camera parameters, simple and practical)
    2013-11-08 14:48:01下载
    积分:1
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    2008-03-10 21:08:07下载
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    积分:1
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    2018-10-18 15:41:14下载
    积分:1
  • instfreq
    亲自测试 完全可以运行且有效,求瞬时频率,各个版本的matlab 都ok(Test yourself Can be run completely and effectively)
    2017-11-05 16:10:35下载
    积分:1
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    对有噪声的图像进行图像复原,通过迭代算法实现此功能(Image restoration for noisy images, this function is implemented by an iterative algorithm)
    2020-06-18 18:00:01下载
    积分:1
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    2010-03-30 22:10:00下载
    积分:1
  • byjc
    说明:  对于一些图像来说,常用的边缘检测算法有时候无法设立合适的阈值将它们的梯度较小的模糊边缘检测出来。为了解决这个问题,有两种解决方法:将图像方差标准化,拉大模糊边缘的梯度值,或通过设置sigmoid函数,将像素所在区域的信息传递到梯度值中去,对其进行调整,就能找到合适阈值,有效地将模糊边缘提取出来。本程序把这两种算法实现并与与传统算法进行了比较。(For some images, the commonly used edge detection algorithm sometimes can not be an appropriate threshold for the gradient of their smaller fuzzy edge detected. To solve this problem, there are two solutions: the image variance standardization, widening the fuzzy edge of the gradient value, or by setting the sigmoid function pixel region to disseminate the message to the gradient values to adjust, on the can find a suitable threshold, effectively extracted fuzzy edge. This procedure of these two algorithms with the traditional algorithm.)
    2008-10-26 18:49:00下载
    积分:1
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