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mmdetection-master

于 2020-06-19 发布 文件大小:7666KB
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代码说明：

  基于mmdetection框架的深度学习目标检测框架(deep learning object detection based on mmdetection)

文件列表：

mmdetection-master, 0 , 2019-07-31
mmdetection-master\.git, 0 , 2019-07-31
mmdetection-master\.git\COMMIT_EDITMSG, 40 , 2019-07-04
mmdetection-master\.git\HEAD, 23 , 2019-07-04
mmdetection-master\.git\config, 130 , 2019-07-04
mmdetection-master\.git\description, 73 , 2019-07-04
mmdetection-master\.git\hooks, 0 , 2019-07-04
mmdetection-master\.git\hooks\applypatch-msg.sample, 478 , 2019-07-04
mmdetection-master\.git\hooks\commit-msg.sample, 896 , 2019-07-04
mmdetection-master\.git\hooks\post-update.sample, 189 , 2019-07-04
mmdetection-master\.git\hooks\pre-applypatch.sample, 424 , 2019-07-04
mmdetection-master\.git\hooks\pre-commit.sample, 1642 , 2019-07-04
mmdetection-master\.git\hooks\pre-push.sample, 1348 , 2019-07-04
mmdetection-master\.git\hooks\pre-rebase.sample, 4951 , 2019-07-04
mmdetection-master\.git\hooks\prepare-commit-msg.sample, 1239 , 2019-07-04
mmdetection-master\.git\hooks\update.sample, 3610 , 2019-07-04
mmdetection-master\.git\index, 42003 , 2019-07-31
mmdetection-master\.git\info, 0 , 2019-07-04
mmdetection-master\.git\info\exclude, 240 , 2019-07-04
mmdetection-master\.git\logs, 0 , 2019-07-04
mmdetection-master\.git\logs\HEAD, 366 , 2019-07-04
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    说明：  a）针对合成纹理图像（共有4 个合成纹理图像，见文件夹： Texture_mosaic_data）中每一个像素提取纹理特征向量； b）利用聚类技术（推荐用k‐均值聚类，可以从网上查找原码）对特征向 量空间中的点进行聚类，类别数可根据图像中的实际纹理类数确定。最后把类 属标签映射成图像形式显示（如下图，其中b、d、f、h 为相应的基准分割图像）。(A) for synthetic texture images (there are 4 synthetic texture images in total, see the folder: Texture feature vector is extracted from every pixel in texture; B) use clustering technology (K-means clustering is recommended, and the original code can be found on the Internet) to search the feature direction The number of categories can be determined according to the actual number of texture classes in the image. Last class The generic label is mapped to an image (as shown in the figure below, where B, D, F, h are the corresponding reference segmentation images).)
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