{"id":5745,"date":"2024-04-23T13:34:49","date_gmt":"2024-04-23T04:34:49","guid":{"rendered":"https:\/\/et.kyushu-u.ac.jp\/?page_id=5745"},"modified":"2024-04-23T13:34:49","modified_gmt":"2024-04-23T04:34:49","slug":"trymatlab03","status":"publish","type":"page","link":"https:\/\/et.kyushu-u.ac.jp\/index.php\/report\/trymatlab03\/","title":{"rendered":"MATLAB\u3092\u4f7f\u3063\u3066\u307f\u307e\u305b\u3093\u304b\uff1f"},"content":{"rendered":"

\u7b2c\uff13\u56de\uff1aDeepLabv3+\u3092\u7528\u3044\u305f\u8ee2\u79fb\u5b66\u7fd2<\/h1>\n

\u8a2d\u5099\u30fb\u60c5\u5831\u6280\u8853\u5ba4\u3000AI\u30fb\u30e1\u30ab\u30c8\u30ed\u30cb\u30af\u30b9\u73ed
\n\u6728\u5ead\u3000\u6d0b\u4ecb<\/p>\n

\u7686\u69d8\u306fMATLAB\u3068\u3044\u3046\u30bd\u30d5\u30c8\u30a6\u30a7\u30a2\u3092\u3054\u5b58\u77e5\u3067\u3057\u3087\u3046\u304b\u3002MATLAB\u306f\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u958b\u767a\u3001\u30c7\u30fc\u30bf\u89e3\u6790\u3001\u53ef\u8996\u5316\u3001\u6570\u5024\u8a08\u7b97\u306e\u305f\u3081\u306e\u7d71\u5408\u958b\u767a\u74b0\u5883\u3067\u3059\u3002\u69d8\u3005\u306a\u5206\u91ce\u306b\u5bfe\u5fdc\u3057\u305fToolbox\uff08\u30a2\u30c9\u30aa\u30f3\u88fd\u54c1\uff09\u304c\u3042\u308a\u3001\u3053\u308c\u3089\u3092\u5229\u7528\u3059\u308b\u3053\u3068\u3067\u3001\u8907\u96d1\u306a\u51e6\u7406\u3092\u7c21\u6f54\u306a\u30b3\u30fc\u30c9\u3067\u5b9f\u73fe\u3067\u304d\u307e\u3059\u3002
\n\u4e5d\u5dde\u5927\u5b66\u60c5\u5831\u7d71\u62ec\u672c\u90e8\u306f\u3001\u300cMATLAB Campus-Wide License\uff08\u5305\u62ec\u30e9\u30a4\u30bb\u30f3\u30b9\u5951\u7d04\uff09\u300d\u306e\u904b\u7528\u30922023\u5e741\u67084\u65e5\u304b\u3089\u958b\u59cb\u3057\u307e\u3057\u305f\u3002\u3053\u308c\u306b\u3088\u308a\u3001MATLAB\/Simulink\u304a\u3088\u3073100\u4ee5\u4e0a\u306eToolbox\uff08\u30a2\u30c9\u30aa\u30f3\u88fd\u54c1\uff09\u306e\u4ed6\u30aa\u30f3\u30e9\u30a4\u30f3\u30c4\u30fc\u30eb\u3084\u30b5\u30fc\u30d3\u30b9\u3092\u3001\u6559\u8077\u54e1\u306f\u6709\u511f\u3067\u3059\u304c\u3001\u5b66\u751f\u306f\u7121\u511f\uff08!!\uff09\u3067\u5229\u7528\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3057\u305f\u3002\u3053\u3053\u3067\u306f\u3001\u300cMATLAB\u3067\u3053\u3046\u3044\u3046\u4e8b\u304c\u3067\u304d\u307e\u3059\u300d\u3068\u3044\u3046\u3053\u3068\u3092\u3054\u7d39\u4ecb\u3057\u3066\u3044\u304d\u305f\u3044\u3068\u601d\u3044\u307e\u3059\u3002<\/p>\n

\u4eca\u56de\u306f\u4ee5\u4e0b\u306e\u30a2\u30c9\u30aa\u30f3\u88fd\u54c1\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002
\nImage Processing Toolbox<\/span>
\nComputer Vision Toolbox<\/span>
\nDeep Learning Toolbox<\/span><\/p>\n

\u306f\u3058\u3081\u306b<\/b><\/h2>\n

\u4eca\u56de\u306f\u3001\u30bb\u30de\u30f3\u30c6\u30a3\u30c3\u30af\u30bb\u30b0\u30e1\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u306e\u305f\u3081\u306e\u30c7\u30a3\u30fc\u30d7\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u300cDeepLabv3+\u300d\u3092\u8ee2\u79fb\u5b66\u7fd2\u3055\u305b\u308b\u3053\u3068\u3067\u7a7a\u4e2d\u5199\u771f\u304b\u3089\u6c34\u90e8\u3092\u62bd\u51fa\u3059\u308b\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u4f5c\u308a\u305f\u3044\u3068\u601d\u3044\u307e\u3059\u3002<\/p>\n

\u30bb\u30de\u30f3\u30c6\u30a3\u30c3\u30af\u30bb\u30b0\u30e1\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u3068\u306f\u3001\u753b\u50cf\u5185\u306e\u753b\u7d20\u6bce\u306b\u30af\u30e9\u30b9\u5206\u985e\u3059\u308b\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3067\u3059\u3002\u4f8b\u3048\u3070\u3001\u4eba\u7269\u3068\u72ac\u304c\u5199\u3063\u305f\u753b\u50cf\u304b\u3089\u4eba\u7269\u3001\u72ac\u3001\u80cc\u666f\u3092\u5206\u5272\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u3053\u306e\u6280\u8853\u306f\u3001\u81ea\u52d5\u904b\u8ee2\u3001\u533b\u7642\u7528\u753b\u50cf\u89e3\u6790\u3001\u5de5\u696d\u691c\u67fb\u306a\u3069\u5e83\u3044\u5206\u91ce\u3067\u4f7f\u7528\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u305d\u3057\u3066\u3001DeepLabv3+\u306f\u3001\u3053\u306e\u30bb\u30de\u30f3\u30c6\u30a3\u30c3\u30af\u30bb\u30b0\u30e1\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u306e\u305f\u3081\u306e\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3068\u3057\u30662018\u5e74\u306b\u516c\u958b\u3055\u308c\u305f\u3082\u306e\u3067\u3001\u5f93\u6765\u306e\u624b\u6cd5\u3088\u308a\u9ad8\u901f\u3067\u5f37\u529b\u3060\u3068\u3046\u305f\u3063\u3066\u3044\u307e\u3059\u3002\u305f\u3060\u3057\u3001\u81ea\u5206\u304c\u671b\u3080\u30af\u30e9\u30b9\u5206\u985e\u3092\u884c\u3046\u305f\u3081\u306b\u30bc\u30ed\u304b\u3089\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u5b66\u7fd2\u3055\u305b\u308b\u5834\u5408\u3001\u5927\u91cf\u306e\u6559\u5e2b\u30c7\u30fc\u30bf\uff08\u753b\u50cf\u3068\u753b\u7d20\u6bce\u306b\u3069\u306e\u30af\u30e9\u30b9\u306b\u5c5e\u3059\u308b\u304b\u30e9\u30d9\u30eb\u4ed8\u3051\u3057\u305f\u30e9\u30d9\u30eb\u753b\u50cf\uff09\u3092\u7528\u610f\u3057\u306a\u3051\u308c\u3070\u306a\u308a\u307e\u305b\u3093\u3002<\/p>\n

\u305d\u3053\u3067\u3001\u8ee2\u79fb\u5b66\u7fd2\u3068\u3044\u3046\u624b\u6cd5\u3092\u5229\u7528\u3057\u307e\u3059\u3002\u3053\u308c\u306f\u3001\u3042\u3089\u304b\u3058\u3081\u65e2\u5b58\u306e\u6559\u5e2b\u30c7\u30fc\u30bf\u3067\u7cbe\u5ea6\u3088\u304f\u5206\u985e\u3067\u304d\u308b\u3088\u3046\u306b\u5b66\u7fd2\u3055\u305b\u305f\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u521d\u671f\u5024\u306b\u3057\u3066\u3001\u81ea\u5206\u304c\u671b\u3080\u30af\u30e9\u30b9\u5206\u985e\u7528\u306b\u518d\u5b66\u7fd2\u3055\u305b\u308b\u65b9\u6cd5\u3067\u3059\u3002\u65e2\u5b58\u306e\u6559\u5e2b\u30c7\u30fc\u30bf\u3068\u81ea\u5206\u304c\u6e96\u5099\u3059\u308b\u6559\u5e2b\u30c7\u30fc\u30bf\u306e\u5206\u985e\u30af\u30e9\u30b9\u304c\u9055\u3044\u3059\u304e\u308b\u3068\u7cbe\u5ea6\u304c\u4e0a\u304c\u3089\u306a\u3044\u3068\u3044\u3046\u554f\u984c\u304c\u3042\u308a\u307e\u3059\u304c\u3001\u518d\u5b66\u7fd2\u306b\u5fc5\u8981\u306a\u6559\u5e2b\u30c7\u30fc\u30bf\u3068\u5b66\u7fd2\u30b3\u30b9\u30c8\u304c\u5c11\u306a\u304f\u3066\u6e08\u3080\u3068\u3044\u3046\u5927\u304d\u306a\u30e1\u30ea\u30c3\u30c8\u304c\u3042\u308b\u305f\u3081\u3001\u8a66\u3057\u3066\u307f\u308b\u4fa1\u5024\u304c\u3042\u308b\u624b\u6cd5\u3060\u3068\u601d\u3044\u307e\u3059\u3002<\/p>\n

MathWorks\u306f\u3001\u3053\u306eDeepLabv3+\u3092\u4f7f\u3063\u3066PASCAL VOC\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304b\u308920\u306e\u30af\u30e9\u30b9\u3092\u691c\u51fa\u3059\u308b\u3088\u3046\u306b\u8a13\u7df4\u3055\u308c\u3066\u3044\u308b\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u63d0\u4f9b\u3057\u3066\u3044\u307e\u3059\u3002\u4eca\u56de\u306f\u3001\u3053\u308c\u3092\u56fd\u571f\u5730\u7406\u9662\u304c\u516c\u958b\u3057\u3066\u3044\u308b\u300cCNN\u306b\u3088\u308b\u5730\u7269\u62bd\u51fa\u7528\u6559\u5e2b\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u300d\u3067\u8ee2\u79fb\u5b66\u7fd2\u3055\u305b\u3001\u7a7a\u4e2d\u5199\u771f\u304b\u3089\u6c34\u90e8\u3092\u62bd\u51fa\u3059\u308b\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u4f5c\u3063\u3066\u3044\u304d\u307e\u3059\u3002<\/p>\n

\u4e8b\u524d\u5b66\u7fd2\u6e08\u307fDeepLabv3+\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9<\/b><\/h2>\n

\u4ee5\u4e0b\u306e\u30ea\u30f3\u30af\u5148\u30da\u30fc\u30b8\u306e\u300cCode\u300d\u30dc\u30bf\u30f3\u3092\u30af\u30ea\u30c3\u30af\u3059\u308b\u3068\u300cDownload ZIP\u300d\u3068\u3044\u3046\u9805\u76ee\u304c\u73fe\u308c\u307e\u3059\u306e\u3067\u3001\u305d\u308c\u3092\u30af\u30ea\u30c3\u30af\u3057\u3066\u300cpretrained-deeplabv3plus-main.zip\u300d\u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3057\u307e\u3059\uff08\u4e0b\u56f3\u53c2\u7167\uff09\u3002<\/p>\n

> DeepLabv3+ inference and training in MATLAB for Semantic Segmentation<\/a>
\n\"\"<\/a><\/p>\n

\u300cpretrained-deeplabv3plus-main.zip\u300d\u3092\u5c55\u958b\u3059\u308b\u3068\u300cpretrained-deeplabv3plus-main\u300d\u3068\u3044\u3046\u30d5\u30a9\u30eb\u30c0\u304c\u3067\u304d\u307e\u3059\u306e\u3067\u3001MATLAB\u3067\u305d\u3053\u307e\u3067\u79fb\u52d5\u3057\u307e\u3059\u3002MATLAB\u306e\u300c\u73fe\u5728\u306e\u30d5\u30a9\u30eb\u30c0\u30fc\u300d\u30bf\u30d6\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u306a\u3063\u3066\u3044\u308b\u3068\u601d\u3044\u307e\u3059\u3002
\n\"\"<\/a><\/p>\n

\u30b3\u30de\u30f3\u30c9\u30a6\u30a3\u30f3\u30c9\u30a6\u3067\u4ee5\u4e0b\u306e\u30b9\u30af\u30ea\u30d7\u30c8\u3092\u6253\u3064\u3068\u3001model\u30d5\u30a9\u30eb\u30c0\u306e\u4e0b\u306b\u300cdeepLabV3Plus-voc.mat\u300d\u30d5\u30a1\u30a4\u30eb\u304c\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3055\u308c\u307e\u3059\u3002\u3053\u306e\u30d5\u30a1\u30a4\u30eb\u3092\u81ea\u5206\u306e\u4f5c\u696d\u30d5\u30a9\u30eb\u30c0\u306b\u30b3\u30d4\u30fc\u3057\u3066\u4f7f\u3044\u307e\u3059\u3002<\/p>\n

model = helper.downloadPretrainedDeepLabv3Plus;\r\n<\/pre>\n
\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9<\/b><\/h2>\n
\u4ee5\u4e0b\u306e\u30ea\u30f3\u30af\u5148\u3067\u3001\u56fd\u571f\u5730\u7406\u9662\u304c\u63d0\u4f9b\u3059\u308b\u300cCNN\u306b\u3088\u308b\u5730\u7269\u62bd\u51fa\u7528\u6559\u5e2b\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u300d\u304c\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3067\u304d\u307e\u3059\u3002\u3053\u308c\u306f\u3001\u7a7a\u4e2d\u5199\u771f\u753b\u50cf\u3092\u5bfe\u8c61\u3068\u3057\u305f\u3001\u9053\u8def\u3001\u6c34\u90e8\u3001\u592a\u967d\u5149\u767a\u96fb\u8a2d\u5099\u306a\u3069\u306e\u5730\u7269\u3092\u30bb\u30de\u30f3\u30c6\u30a3\u30c3\u30af\u30bb\u30b0\u30e1\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u3067\u62bd\u51fa\u3059\u308b\u969b\u306b\u7528\u3044\u308b\u5b66\u7fd2\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3067\u3059\u3002\u4eca\u56de\u306f\u3053\u306e\u4e2d\u306e\u300c\u6c34\u90e8\u300d\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u3044\u307e\u3059\u3002<\/p>\n
> CNN\u306b\u3088\u308b\u5730\u7269\u62bd\u51fa\u7528\u6559\u5e2b\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\uff08\u56fd\u571f\u5730\u7406\u9662\uff09<\/a><\/p>\n
\u6c34\u90e8\u306e\u30da\u30fc\u30b8\u306b\u79fb\u52d5\u3057\u3001\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u306e\u9805\u76ee\u304b\u3089\u300cH1-No18-572.zip\u300d\u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3057\u5c55\u958b\u3057\u307e\u3059\u3002\u5c55\u958b\u3055\u308c\u305f\u300cH1-No18-572\u300d\u30d5\u30a9\u30eb\u30c0\u306e\u4e0b\u306b\u306f\u300corg\u300d\u3001\u300cval\u300d\u3068\u3044\u3046\u30d5\u30a9\u30eb\u30c0\u304c\u3042\u308a\u307e\u3059\u3002\u300corg\u300d\u30d5\u30a9\u30eb\u30c0\u306b\u306f\u7a7a\u4e2d\u5199\u771f\u753b\u50cf\u3001\u300cval\u300d\u30d5\u30a9\u30eb\u30c0\u306b\u306f\u6c34\u90e8\u3092\u9752\u8272\uff08RGB:#0000FF\uff09\u3067\u5857\u3063\u305f\u30e9\u30d9\u30eb\u753b\u50cf\u304c\u4fdd\u5b58\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n
\"\"<\/a><\/p>\n
\u8ee2\u79fb\u5b66\u7fd2\u306b\u3088\u308b\u6c34\u90e8\u3092\u62bd\u51fa\u3059\u308b\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u4f5c\u6210<\/b><\/h2>\n
\u305d\u308c\u3067\u306f\u3001\u6c34\u90e8\u3092\u62bd\u51fa\u3059\u308b\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u4f5c\u3063\u3066\u3044\u304d\u307e\u3059\u3002\u4eca\u56de\u306e\u4f8b\u3067\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u4f5c\u696d\u30d5\u30a9\u30eb\u30c0\u69cb\u6210\u306b\u3057\u3066\u3044\u307e\u3059\u3002\u300cdeepLabV3Plus-voc.mat\u300d\u306f\u4e8b\u524d\u5b66\u7fd2\u6e08\u307f\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3001\u300cimagefiles\u300d\u3068\u3044\u3046\u30d5\u30a9\u30eb\u30c0\u4ee5\u4e0b\u306b\u6c34\u90e8\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u300cH1-No18-572\u300d\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n
\"\"<\/a><\/p>\n
\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u6e96\u5099<\/b><\/h3>\n
\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306f\u753b\u50cf\u3068\u30e9\u30d9\u30eb\u753b\u50cf\u306e\u30da\u30a2\u3067\u69cb\u6210\u3055\u308c\u308b\u306e\u3067\u3059\u304c\u3001\u305d\u308c\u305e\u308c\u306e\u753b\u50cf\u30b5\u30a4\u30ba\u304c\u63c3\u3063\u3066\u3044\u306a\u3044\u3068\u3044\u3051\u307e\u305b\u3093\u3002\u4eca\u56de\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3057\u305f\u6c34\u90e8\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306f\u3001\u30e9\u30d9\u30eb\u753b\u50cf\uff08val\u30d5\u30a9\u30eb\u30c0\u5185\uff09\u306e\u300c554.png\u300d\u3060\u3051\u30b5\u30a4\u30ba\u304c574\u00d7574\u30d4\u30af\u30bb\u30eb\u306b\u306a\u3063\u3066\u3044\u307e\u3059\uff08\u4ed6\u306e\u753b\u50cf\u306f\u3059\u3079\u3066572\u00d7572\u30d4\u30af\u30bb\u30eb\uff09\u3002\u3067\u3059\u306e\u3067\u3001\u753b\u50cf\u3068\u30e9\u30d9\u30eb\u753b\u50cf\u306e\u300c554.png\u300d\u306f\u524a\u9664\u3057\u307e\u3059\u3002\u305d\u306e\u5f8c\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u30ea\u30b9\u30c8\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002<\/p>\n
% \u30c7\u30fc\u30bf\u30d5\u30a9\u30eb\u30c0\u306e\u30d1\u30b9\u3092\u6307\u5b9a\r\ndataFolderPath = fullfile('.\/', 'imagefiles', 'H1-No18-572');\r\n\r\n% \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u30ea\u30b9\u30c8\u3092\u4f5c\u6210\r\nimageFiles = dir(fullfile(dataFolderPath, 'org', '*.png')); % \u7a7a\u4e2d\u5199\u771f\u753b\u50cf\r\nvalFiles = dir(fullfile(dataFolderPath, 'val', '*.png')); % \u30e9\u30d9\u30eb\u753b\u50cf\r\n<\/pre>\n
\u30e9\u30d9\u30eb\u753b\u50cf\u306e\u4fee\u6b63<\/b><\/h3>\n
\u6c34\u90e8\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u30e9\u30d9\u30eb\u753b\u50cf\u306f\u6c34\u90e8\u3092\u9752\u8272\u3067\u5857\u3063\u3066\u3044\u307e\u3059\u304c\u3001\u305d\u308c\u4ee5\u5916\u306e\u90e8\u5206\u306f\u5199\u771f\u306e\u307e\u307e\u3067\u3059\u3002\u6c34\u90e8\u4ee5\u5916\u306f\u300c\u305d\u306e\u4ed6\u300d\u3068\u3044\u3046\u30af\u30e9\u30b9\u306b\u3057\u305f\u3044\u306e\u3067\u3001\u6c34\u90e8\u4ee5\u5916\u3092\u9ed2\u8272\uff08RGB:#000000\uff09\u3067\u5857\u3063\u305f\u753b\u50cf\u3092\u65b0\u305f\u306b\u4f5c\u6210\u3057\u3001\u300clabel\u300d\u3068\u3044\u3046\u30d5\u30a9\u30eb\u30c0\u306b\u4fdd\u5b58\u3057\u307e\u3059\u3002<\/p>\n
% \u300clabel\u300d\u30d5\u30a9\u30eb\u30c0\u304c\u306a\u3051\u308c\u3070\u4f5c\u6210\u3059\u308b\r\nif exist(fullfile(dataFolderPath, 'label'), \"dir\") ~=7\r\n    mkdir(fullfile(dataFolderPath, 'label'))\r\nend\r\n\r\nfor i=1:length(valFiles)\r\n    % \u30aa\u30ea\u30b8\u30ca\u30eb\u30e9\u30d9\u30eb\u753b\u50cf\u3092\u8aad\u307f\u8fbc\u3080\r\n    tmpimg = imread(fullfile(valFiles(i).folder, valFiles(i).name));\r\n    % \u6c34\u90e8\u4ee5\u5916\u306e\u753b\u7d20\u3092\u53d6\u5f97\r\n    mask = ~all(tmpimg == reshape([0 0 255], [1 1 3]), 3);\r\n    % \u6c34\u90e8\u4ee5\u5916\u3092\u9ed2\u306b\u3059\u308b\r\n    tmpimg(repmat(mask, [1 1 3])) = 0;\r\n    % 'label'\u3068\u3044\u3046\u540d\u524d\u306e\u30d5\u30a9\u30eb\u30c0\u306b\u65b0\u305f\u306b\u4f5c\u3063\u305f\u30e9\u30d9\u30eb\u753b\u50cf\u3092\u4fdd\u5b58\r\n    imwrite(tmpimg, fullfile(dataFolderPath, 'label', valFiles(i).name));\r\nend\r\n<\/pre>\n
\u30c7\u30fc\u30bf\u30b9\u30c8\u30a2\u3092\u4f5c\u6210\u3059\u308b<\/b><\/h3>\n
\u6a5f\u68b0\u5b66\u7fd2\u306a\u3069\u3067\u5927\u91cf\u306e\u30c7\u30fc\u30bf\u3092\u6271\u3046\u5834\u5408\u3001\u5168\u30c7\u30fc\u30bf\u3092\u30e1\u30e2\u30ea\u306b\u8aad\u307f\u8fbc\u3082\u3046\u3068\u3059\u308b\u3068\u30e1\u30e2\u30ea\u306b\u53ce\u307e\u3089\u306a\u3044\u6050\u308c\u304c\u3042\u308a\u307e\u3059\u3002\u305d\u3053\u3067\u3001\u30d5\u30a1\u30a4\u30eb\u60c5\u5831\u306a\u3069\u3092\u683c\u7d0d\u3057\u305f\u30c7\u30fc\u30bf\u30b9\u30c8\u30a2\u3068\u3044\u3046\u30ea\u30dd\u30b8\u30c8\u30ea\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002\u3053\u308c\u3092\u4f7f\u3046\u3053\u3068\u3067\u30e1\u30e2\u30ea\u306b\u53ce\u307e\u308b\u30b5\u30a4\u30ba\u3067\u8aad\u307f\u8fbc\u307f\u3001\u5b66\u7fd2\u3055\u305b\u308b\u3068\u3044\u3046\u3053\u3068\u304c\u7c21\u5358\u306b\u3067\u304d\u307e\u3059\u3002<\/p>\n
\u753b\u50cf\u30c7\u30fc\u30bf\u306e\u30c7\u30fc\u30bf\u30b9\u30c8\u30a2\u306e\u4f5c\u6210\u306b\u306fimageDatastore\u95a2\u6570\u3092\u4f7f\u3044\u307e\u3059\u3002\u5404\u753b\u50cf\u30c7\u30fc\u30bf\u306e\u30d5\u30a1\u30a4\u30eb\u30d1\u30b9\u3092\u683c\u7d0d\u3057\u305fcell\u914d\u5217\u3092\u5f15\u6570\u3068\u3057\u3066\u4e0e\u3048\u307e\u3059\u3002\u30e9\u30d9\u30eb\u753b\u50cf\u306e\u30c7\u30fc\u30bf\u30b9\u30c8\u30a2\u306e\u4f5c\u6210\u306b\u306fPixelLabelDatastore\u95a2\u6570\u3092\u4f7f\u3044\u307e\u3059\u3002\u5404\u30e9\u30d9\u30eb\u753b\u50cf\u30c7\u30fc\u30bf\u306e\u30d5\u30a1\u30a4\u30eb\u30d1\u30b9\u3092\u683c\u7d0d\u3057\u305fcell\u914d\u5217\u3068\u30af\u30e9\u30b9\u540d\u3001\u30af\u30e9\u30b9\u540d\u3068\u95a2\u9023\u4ed8\u3051\u308b\u5024\u3092\u5f15\u6570\u3068\u3057\u3066\u4e0e\u3048\u307e\u3059\u3002\u4eca\u56de\u306e\u4f8b\u3067\u306f\u3001\u300cWater\u300d\uff08\u6c34\u90e8\uff09\u3068\u300cOther\u300d\uff08\u305d\u306e\u4ed6\uff09\u306e\uff12\u3064\u306e\u30af\u30e9\u30b9\u3092\u5b66\u7fd2\u3055\u305b\u307e\u3059\u3002\u6c34\u90e8\u306f\u9752\u8272\u3001\u305d\u306e\u4ed6\u306f\u9ed2\u8272\u3067\u5857\u3063\u3066\u3044\u307e\u3059\u306e\u3067\u3001\u305d\u306e\u8272\u306b\u5bfe\u5fdc\u3057\u305fRGB\u5024[0 0 255]\u3001[0 0 0]\u3092\u8a2d\u5b9a\u3057\u307e\u3059\u3002<\/p>\n
% \u30e9\u30d9\u30eb\u753b\u50cf\u306e\u30ea\u30b9\u30c8\u3092\u4f5c\u6210\r\nlabelFiles = dir(fullfile(dataFolderPath, 'label', '*.png'));\r\nnumImg = length(labelFiles);\r\n\r\n% \u753b\u50cf\u3068\u30e9\u30d9\u30eb\u753b\u50cf\u307e\u3067\u306e\u30d1\u30b9\u3092\u683c\u7d0d\u3057\u305f\u30bb\u30eb\u914d\u5217\u3092\u4f5c\u6210\u3059\u308b\r\nimageFilepaths = cell(numImg, 1);\r\nlabelFilepaths = cell(numImg, 1);\r\nfor i=1:numImg\r\n    imageFilepaths{i} = fullfile(imageFiles(i).folder, imageFiles(i).name);\r\n    labelFilepaths{i} = fullfile(labelFiles(i).folder, labelFiles(i).name);\r\nend\r\n\r\nclasses = [\"Water\" \"Other\"]; % \u30af\u30e9\u30b9\u540d\u306e\u8a2d\u5b9a\r\npixelLabelID = {[0 0 255] [0 0 0]}; % \u30af\u30e9\u30b9\u306b\u5bfe\u5fdc\u3059\u308b\u30e9\u30d9\u30eb\u5024\u306e\u8a2d\u5b9a\r\n\r\n% \u753b\u50cf\u30c7\u30fc\u30bf\u30b9\u30c8\u30a2\u306e\u4f5c\u6210\r\nimds = imageDatastore(imageFilepaths);\r\n% \u30e9\u30d9\u30eb\u753b\u50cf\u30c7\u30fc\u30bf\u30b9\u30c8\u30a2\u306e\u4f5c\u6210\r\npxds = pixelLabelDatastore(labelFilepaths, classes, pixelLabelID);\r\n<\/pre>\n
\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u89e3\u6790<\/b><\/h3>\n
countEachLabel\u95a2\u6570\u3092\u4f7f\u3046\u3068\u30e9\u30d9\u30eb\u753b\u50cf\u306b\u542b\u307e\u308c\u308b\u30af\u30e9\u30b9\u3054\u3068\u306e\u30d4\u30af\u30bb\u30eb\u6570\u3092\u30ab\u30a6\u30f3\u30c8\u3067\u304d\u307e\u3059\u3002\u51fa\u529b\u306eName\u306f\u30af\u30e9\u30b9\u540d\u3001PixelCount\u306f\u305d\u306e\u30af\u30e9\u30b9\u306e\u7dcf\u30d4\u30af\u30bb\u30eb\u6570\u3001ImagePixelCount\u306f\u305d\u306e\u30af\u30e9\u30b9\u3092\u542b\u3080\u753b\u50cf\u306e\u7dcf\u30d4\u30af\u30bb\u30eb\u6570\u3092\u8868\u3057\u307e\u3059\u3002PixelCount\u3092\u898b\u308b\u3068\u3001\u6c34\u90e8\u3088\u308a\u305d\u306e\u4ed6\u304c4.4\u500d\u7a0b\u5ea6\u591a\u3044\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002\u30af\u30e9\u30b9\u306b\u504f\u308a\u304c\u3042\u308b\u3068\u5b66\u7fd2\u7cbe\u5ea6\u306b\u60aa\u5f71\u97ff\u3092\u4e0e\u3048\u307e\u3059\u306e\u3067\u3001\u5206\u985e\u5c64\u306b\u91cd\u307f\u4ed8\u3051\u3057\u3066\u504f\u308a\u306e\u5f71\u97ff\u3092\u4f4e\u6e1b\u3057\u307e\u3059\uff08\u5f8c\u8ff0\uff09\u3002ImagePixelCount\u304c\u7570\u306a\u308b\u306e\u306f\u3001\u6c34\u90e8\u3060\u3051\u306e\u753b\u50cf\u3084\u6c34\u90e8\u304c\u307e\u3063\u305f\u304f\u5b58\u5728\u3057\u306a\u3044\u753b\u50cf\u304c\u3042\u308b\u305f\u3081\u3067\u3059\u3002<\/p>\n
tbl = countEachLabel(pxds)\r\n<\/pre>\n
tbl =\r\n  2\u00d73 table\r\n      Name       PixelCount    ImagePixelCount\r\n    _________    __________    _______________\r\n    {'Water'}    7.5727e+07      2.2608e+08   \r\n    {'Other'}    3.3293e+08      3.9262e+08   \r\n<\/pre>\n
\u30c7\u30fc\u30bf\u30b9\u30c8\u30a2\u3092\u5b66\u7fd2\u3001\u691c\u8a3c\u3001\u30c6\u30b9\u30c8\u7528\u30c7\u30fc\u30bf\u30b9\u30c8\u30a2\u306b\u5206\u5272<\/b><\/h3>\n
\u5b66\u7fd2\u3001\u691c\u8a3c\u3001\u30c6\u30b9\u30c8\u7528\u306b\u30c7\u30fc\u30bf\u30b9\u30c8\u30a2\u309260%\u300120%\u300120%\u306e\u5272\u5408\u3067\u5206\u5272\u3057\u307e\u3059\u3002\u307e\u305adividerand\u95a2\u6570\u3092\u4f7f\u3044\u30011\u304b\u3089\u5168\u30c7\u30fc\u30bf\u6570\u307e\u3067\u306e\u6570\u5024\u309260%\u300120%\u300120%\u306e\u5272\u5408\u3067\u30e9\u30f3\u30c0\u30e0\u306b\u5206\u5272\u3057\u307e\u3059\u3002\u3053\u306e\u5206\u5272\u3055\u308c\u305f\u6570\u5024\u3092\u30d5\u30a1\u30a4\u30eb\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u3068\u3057\u3001\u5168\u30c7\u30fc\u30bf\u30b9\u30c8\u30a2\u304b\u3089\u305d\u308c\u305e\u308c\u306e\u30c7\u30fc\u30bf\u30b9\u30c8\u30a2\u3078\u62bd\u51fa\u3057\u307e\u3059\u3002<\/p>\n
% \u5404\u30c7\u30fc\u30bf\u30b9\u30c8\u30a2\u7528\u306b\u30e9\u30f3\u30c0\u30e0\u306a\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u306e\u4f5c\u6210\r\n[IndTrain, IndVal, IndTest] = dividerand(length(imds.Files), 0.6, 0.2, 0.2);\r\n\r\n% \u30c7\u30fc\u30bf\u30b9\u30c8\u30a2\u306e\u4f5c\u6210\r\nimdsTrain = imageDatastore(imds.Files(IndTrain));\r\npxdsTrain = pixelLabelDatastore(pxds.Files(IndTrain), classes, pixelLabelID);\r\nimdsVal = imageDatastore(imds.Files(IndVal));\r\npxdsVal = pixelLabelDatastore(pxds.Files(IndVal), classes, pixelLabelID);\r\nimdsTest = imageDatastore(imds.Files(IndTest));\r\npxdsTest = pixelLabelDatastore(pxds.Files(IndTest), classes, pixelLabelID);\r\n<\/pre>\n
\u30c7\u30fc\u30bf\u6570\u3092\u78ba\u8a8d\u3059\u308b\u3068\u3001\u5b66\u7fd2\u30c7\u30fc\u30bf\u6570\uff08numTrainingImages\uff09\u304c749\u3001\u691c\u8a3c\u30c7\u30fc\u30bf\u6570\uff08numValImages\uff09\u304c250\u3001\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\u6570\uff08numTestingImages\uff09\u304c250\u3068\u306a\u3063\u3066\u3044\u307e\u3059\u3002<\/p>\n
\r\n% \u30c7\u30fc\u30bf\u6570\u306e\u78ba\u8a8d\r\nnumTrainingImages = numel(imdsTrain.Files)\r\nnumValImages = numel(imdsVal.Files)\r\nnumTestingImages = numel(imdsTest.Files)\r\n<\/pre>\n
numTrainingImages = 749\r\nnumValImages = 250\r\nnumTestingImages = 250\r\n<\/pre>\n
\u4ee5\u4e0a\u3067\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u6e96\u5099\u306f\u7d42\u308f\u308a\u307e\u3057\u305f\u3002\u6b21\u306f\u8ee2\u79fb\u5b66\u7fd2\u306e\u305f\u3081\u306b\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u4fee\u6b63\u3092\u884c\u3044\u307e\u3059\u3002<\/p>\n
\u4e8b\u524d\u5b66\u7fd2\u6e08\u307f\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u4fee\u6b63<\/b><\/h3>\n
\u307e\u305a\u3001\u4e8b\u524d\u5b66\u7fd2\u6e08\u307f\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u8aad\u307f\u8fbc\u307f\u3001\u305d\u3053\u304b\u3089\u30b0\u30e9\u30d5\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3092\u62bd\u51fa\u3057\u307e\u3059\u3002<\/p>\n
load('deepLabV3Plus-voc.mat');\r\nlgraph = layerGraph(net);\r\n<\/pre>\n
\u5909\u6570lgraph\u304c\u30b0\u30e9\u30d5\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3067\u3059\u3002\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u69cb\u6210\u3059\u308b\u5404\u5c64\u306e\u60c5\u5831\u3084\u5c64\u540c\u58eb\u306e\u63a5\u7d9a\u60c5\u5831\u304c\u542b\u307e\u308c\u3066\u304a\u308a\u3001\u3053\u308c\u3092\u7de8\u96c6\u3059\u308b\u3053\u3068\u3067\u81ea\u5206\u306e\u671b\u3080\u5f62\u306b\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u4fee\u6b63\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n
analyzeNetwork\u95a2\u6570\u306f\u4e0b\u56f3\u306e\u3088\u3046\u306b\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u53ef\u8996\u5316\u3084\u89e3\u6790\u304c\u3067\u304d\u3001\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306b\u554f\u984c\u304c\u3042\u308c\u3070\u3001\u30a8\u30e9\u30fc\u3092\u51fa\u529b\u3057\u3066\u304f\u308c\u307e\u3059\u3002\u51fa\u529b\u3092\u898b\u308b\u3068\u3001376\u5c64\u306e\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3067\u30d1\u30e9\u30e1\u30fc\u30bf\u6570\u306f58.8M\u3001\u5165\u529b\u5c64\u306e\u30bf\u30a4\u30d7\u306f\u753b\u50cf\u3067\u3001\u305d\u306e\u30b5\u30a4\u30ba\u306f513\u00d7513\u00d73\u3067\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/p>\n
analyzeNetwork(lgraph);<\/pre>\n
\"\"<\/a><\/p>\n
\u305d\u308c\u3067\u306f\u3001\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u4fee\u6b63\u3092\u884c\u3044\u307e\u3059\u3002\u307e\u305a\u3001\u6700\u5f8c\u306e\u7573\u307f\u8fbc\u307f\u5c64\u3092\u6c34\u90e8\u3001\u305d\u306e\u4ed6\u306e\uff12\u30af\u30e9\u30b9\u306b\u5bfe\u5fdc\u3055\u305b\u307e\u3059\u3002lgraph\u3084analyzeNetwork\u95a2\u6570\u3067\u6700\u5f8c\u306e\u7573\u307f\u8fbc\u307f\u5c64\u3092\u8abf\u3079\u308b\u3068\u3001’node_398’\u3068\u3044\u3046\u540d\u524d\u3067\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3057\u305f\u3002\u305d\u306e\u5c64\u3092\u65b0\u3057\u304f\u4f5c\u3063\u305f\u7573\u307f\u8fbc\u307f\u5c64\u306b\u7f6e\u304d\u63db\u3048\u307e\u3059\u3002<\/p>\n
numClasses = numel(classes); % \u65b0\u3057\u3044\u30af\u30e9\u30b9\u6570\u306e\u53d6\u5f97\r\n\r\n% \u65b0\u3057\u3044\u7573\u307f\u8fbc\u307f\u5c64\u3092\u4f5c\u6210\u3059\u308b\r\nconvLayer = convolution2dLayer([1 1], numClasses,'Name', 'node_398');\r\n% \u7573\u307f\u8fbc\u307f\u5c64\u3092\u7f6e\u304d\u63db\u3048\u308b\r\nlgraph = replaceLayer(lgraph,\"node_398\",convLayer);\r\n<\/pre>\n
\u6b21\u306b\u3001\u30d4\u30af\u30bb\u30eb\u5206\u985e\u5c64\u3092\uff12\u30af\u30e9\u30b9\u7528\u306b\u4fee\u6b63\u3057\u307e\u3059\u3002\u3053\u306e\u3068\u304d\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u304a\u3051\u308b\u30af\u30e9\u30b9\u306e\u504f\u308a\u306e\u5f71\u97ff\u3092\u5c11\u306a\u304f\u3059\u308b\u305f\u3081\u306b\u30af\u30e9\u30b9\u3054\u3068\u306b\u91cd\u307f\u4ed8\u3051\u3092\u3057\u307e\u3059\u3002<\/p>\n
\r\n% \u5404\u30af\u30e9\u30b9\u306b\u4e0e\u3048\u308b\u91cd\u307f\u3092\u8a08\u7b97\u3059\u308b\r\nimageFreq = tbl.PixelCount .\/ sum(tbl.PixelCount);\r\nclassWeights = median(imageFreq) .\/ imageFreq;\r\n\r\n% \u30d4\u30af\u30bb\u30eb\u5206\u985e\u5c64\u3092\u3001\u91cd\u307f\u4ed8\u3051\u3057\u305f\u65b0\u3057\u3044\u5206\u985e\u5c64\u3067\u7f6e\u304d\u63db\u3048\u308b\r\npxLayer = pixelClassificationLayer('Name','labels','Classes',tbl.Name,'ClassWeights',classWeights);\r\nlgraph = replaceLayer(lgraph,\"labels\",pxLayer);\r\n<\/pre>\n
\u4fee\u6b63\u5f8c\u306fanalyzeNetwork\u95a2\u6570\u3067\u671b\u3093\u3060\u3068\u304a\u308a\u306b\u4fee\u6b63\u3067\u304d\u3066\u3044\u308b\u304b\u78ba\u8a8d\u3057\u307e\u3057\u3087\u3046\u3002\u7d9a\u3044\u3066\u3001\u5b66\u7fd2\u30aa\u30d7\u30b7\u30e7\u30f3\u306e\u8a2d\u5b9a\u3092\u3057\u307e\u3059\u3002<\/p>\n
\u5b66\u7fd2\u30aa\u30d7\u30b7\u30e7\u30f3\u306e\u8a2d\u5b9a<\/b><\/h3>\n
\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u5165\u529b\u5c64\u306e\u30b5\u30a4\u30ba\u306f513\u00d7513\u00d73\u3067\u3059\u304c\u3001\u6c34\u90e8\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u753b\u50cf\u30b5\u30a4\u30ba\u306f572\u00d7572\u00d73\u3067\u3059\u306e\u3067\u3001\u3053\u306e\u307e\u307e\u4e0e\u3048\u308b\u3068\u30a8\u30e9\u30fc\u306b\u306a\u308a\u307e\u3059\u3002\u305d\u3053\u3067\u3001randomPatchExtractionDatastore\u95a2\u6570\u3092\u4f7f\u3063\u3066\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u30e9\u30f3\u30c0\u30e0\u306a\u4f4d\u7f6e\u3067513\u00d7513\u00d73\u30b5\u30a4\u30ba\u306e\u753b\u50cf\u3092\u62bd\u51fa\u3059\u308b\u305f\u3081\u306e\u30c7\u30fc\u30bf\u30b9\u30c8\u30a2\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002\u5f15\u6570\u306b\u753b\u50cf\u3068\u30e9\u30d9\u30eb\u753b\u50cf\u306e\u30c7\u30fc\u30bf\u30b9\u30c8\u30a2\u3001\u5207\u308a\u53d6\u308b\u30b5\u30a4\u30ba\uff08[513 513]\uff09\u3001\u62bd\u51fa\u3059\u308b\u753b\u50cf\u6570\uff08’PatchesPerImage’\uff09\u3092\u4e0e\u3048\u307e\u3059\u3002’PatchesPerImage’\u3092\u5927\u304d\u304f\u3059\u308b\u3068\uff11\u679a\u306e\u753b\u50cf\u304b\u3089\u62bd\u51fa\u3059\u308b\u753b\u50cf\u6570\u304c\u5897\u3084\u305b\u307e\u3059\u306e\u3067\u3001\u4f8b\u3048\u3070\u3001\u5927\u304d\u306a\u753b\u50cf\u304b\u3089\u30e9\u30f3\u30c0\u30e0\u306a\u4f4d\u7f6e\u3067\u5207\u308a\u53d6\u3063\u305f\u5c0f\u3055\u306a\u753b\u50cf\u3092\u305f\u304f\u3055\u3093\u4f5c\u308b\u3068\u3044\u3063\u305f\u4f7f\u3044\u65b9\u304c\u3067\u304d\u307e\u3059\u3002\u307e\u305f\u3001’DataAugmentation’\u30aa\u30d7\u30b7\u30e7\u30f3\u3092\u4f7f\u3048\u3070\u3001\u30e9\u30f3\u30c0\u30e0\u306a\u753b\u50cf\u306e\u56de\u8ee2\u3084\u53cd\u8ee2\u3068\u3044\u3063\u305f\u524d\u51e6\u7406\u3092\u9069\u7528\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002\u3053\u308c\u306f\u3001\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u904e\u9069\u5408\u306e\u9632\u6b62\u306b\u5f79\u7acb\u3061\u307e\u3059\u3002\u8a73\u3057\u304f\u306f\u4ee5\u4e0b\u306e\u30ea\u30f3\u30af\u3092\u53c2\u7167\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n
> randomPatchExtractionDatastore<\/a><\/p>\n
dsTrain = randomPatchExtractionDatastore(imdsTrain,pxdsTrain, [513 513], 'PatchesPerImage',1);\r\ndsVal = randomPatchExtractionDatastore(imdsVal,pxdsVal,[513 513],'PatchesPerImage',1);\r\n<\/pre>\n
\u5b66\u7fd2\u30aa\u30d7\u30b7\u30e7\u30f3\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u8a2d\u5b9a\u3057\u307e\u3057\u305f\u3002\u4eca\u56de\u3001\u30d5\u30a1\u30a4\u30f3\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u3068\u3044\u3046\u624b\u6cd5\u3092\u4f7f\u3044\u307e\u3059\u3002\u3053\u308c\u306f\u4e8b\u524d\u5b66\u7fd2\u6e08\u307f\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u521d\u671f\u5024\u3068\u3057\u3066\u3001\u65b0\u3057\u3044\u30af\u30e9\u30b9\u306b\u5bfe\u5fdc\u3059\u308b\u3088\u3046\u306b\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u5fae\u8abf\u6574\u3059\u308b\u3082\u306e\u3067\u3059\u3002\u521d\u671f\u5b66\u7fd2\u7387\uff08’InitialLearnRate’\uff09\u3092\u5c0f\u3055\u304f\u3059\u308b\u3053\u3068\u3067\u5b9f\u73fe\u3057\u307e\u3059\u3002\u307e\u305f\u3001’LearnRateDropPeriod’\u30925\u3001’LearnRateDropFactor’\u30920.5\u306b\u8a2d\u5b9a\u3057\u30015\u30a8\u30dd\u30c3\u30af\u7d4c\u904e\u3059\u308b\u3054\u3068\u306b\u5b66\u7fd2\u7387\u30920.5\u500d\u3059\u308b\u3088\u3046\u306b\u3057\u307e\u3057\u305f\u3002\u3053\u308c\u306f\u3001\u5b66\u7fd2\u304c\u9032\u3080\u307b\u3069\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u5909\u5316\u304c\u5c11\u306a\u304f\u306a\u308b\u3088\u3046\u306b\u3059\u308b\u305f\u3081\u3067\u3059\u3002<\/p>\n
\u6700\u5927\u30a8\u30dd\u30c3\u30af\u6570\uff08’MaxEpochs’\uff09\u306f10\u3001\u51fa\u529b\u3059\u308b\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\uff08’OutputNetwork’\uff09\u306f\u5b66\u7fd2\u7d42\u4e86\u6642\u70b9\u3067\u6700\u5c0f\u306e\u691c\u8a3c\u640d\u5931\u3092\u51fa\u3057\u305f\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306b\u3057\u3066\u3044\u307e\u3059\u3002\u305d\u306e\u4ed6\u3001\u3044\u304f\u3064\u304b\u8a2d\u5b9a\u3092\u3057\u3066\u3044\u307e\u3059\u304c\u3001\u5b66\u7fd2\u30aa\u30d7\u30b7\u30e7\u30f3\u306e\u8a73\u7d30\u306f\u4ee5\u4e0b\u306e\u30ea\u30f3\u30af\u3092\u53c2\u8003\u306b\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n
> trainingoptions<\/a><\/p>\n
options = trainingOptions('sgdm', ...\r\n    'LearnRateSchedule','piecewise',...\r\n    'LearnRateDropPeriod',5,...\r\n    'LearnRateDropFactor',0.5,...\r\n    'Momentum',0.9, ...\r\n    'InitialLearnRate',0.001, ... % \u521d\u671f\u5b66\u7fd2\u7387\u3092\u5c0f\u3055\u304f\u3059\u308b\r\n    'L2Regularization',0.005, ...\r\n    'ValidationData',dsVal, ...\r\n    'MaxEpochs',10, ...\r\n    'MiniBatchSize',6, ... % \u30e1\u30e2\u30ea\u30a8\u30e9\u30fc\u304c\u51fa\u308b\u3068\u304d\u306f\u30b5\u30a4\u30ba\u3092\u6e1b\u3089\u3059\r\n    'Shuffle','every-epoch', ...\r\n    'CheckpointPath', tempdir, ...\r\n    'VerboseFrequency',50, ...\r\n    'ValidationFrequency',50, ...\r\n    'Plots','training-progress', ...\r\n    'OutputNetwork','best-validation-loss', ...\r\n    'ExecutionEnvironment','gpu' ...\r\n    );\r\n<\/pre>\n
\u5b66\u7fd2\u958b\u59cb<\/b><\/h3>\n
\u305d\u308c\u3067\u306f\u3001\u5b66\u7fd2\u3055\u305b\u3066\u307f\u307e\u3057\u3087\u3046\u3002\u4ee5\u4e0b\u306e\u30b3\u30de\u30f3\u30c9\u3092\u5b9f\u884c\u3059\u308b\u3068\u3001\u4e0b\u56f3\u306e\u3088\u3046\u306a\u30a6\u30a4\u30f3\u30c9\u30a6\u304c\u958b\u304d\u3001\u5b66\u7fd2\u306e\u9032\u6357\u72b6\u6cc1\u3092\u8868\u793a\u3057\u307e\u3059\u300210GB\u306e\u30e1\u30e2\u30ea\u3092\u642d\u8f09\u3057\u305fNVIDIA GeForce RTX 3080\u3067\u8a08\u7b97\u3055\u305b\u307e\u3057\u305f\u304c\u3001\u7d0430\u5206\u304b\u304b\u308a\u307e\u3057\u305f\u3002GPU\u30e1\u30e2\u30ea\u304c\u3053\u308c\u3088\u308a\u3082\u5c11\u306a\u3044\u5834\u5408\u306f\u30e1\u30e2\u30ea\u4e0d\u8db3\u304c\u767a\u751f\u3059\u308b\u3068\u601d\u3044\u307e\u3059\u3002\u305d\u306e\u6642\u306f\u5b66\u7fd2\u30aa\u30d7\u30b7\u30e7\u30f3\u306e’MiniBatchSize’\u3092\u5c0f\u3055\u304f\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u305d\u308c\u3067\u3082\u4e0d\u8db3\u3059\u308b\u5834\u5408\u306f\u3001\u5c64\u6570\u306e\u5c11\u306a\u3044\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3078\u306e\u5909\u66f4\u3084\u3001\u5165\u529b\u5c64\u306e\u30b5\u30a4\u30ba\u7e2e\u5c0f\u3092\u691c\u8a0e\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n
[net, info] = trainNetwork(dsTrain,lgraph,options);\r\n<\/pre>\n
\"\"<\/a><\/p>\n
\u30c6\u30b9\u30c8\u30a4\u30e1\u30fc\u30b8\u3092\u4f7f\u3063\u305f\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u8a55\u4fa1<\/b><\/h3>\n
\u6700\u5f8c\u306b\u3001\u30c6\u30b9\u30c8\u30a4\u30e1\u30fc\u30b8\u3092\u4f7f\u3063\u3066\u3001\u30d5\u30a1\u30a4\u30f3\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u3055\u308c\u305f\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u30c6\u30b9\u30c8\u3092\u3057\u307e\u3059\u3002pxdsResults\u306b\u30bb\u30de\u30f3\u30c6\u30a3\u30c3\u30af\u30bb\u30b0\u30e1\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u306e\u7d50\u679c\u304c\u51fa\u529b\u3055\u308c\u307e\u3059\u3002\u305d\u306e\u7d50\u679c\u3068\u6b63\u89e3\u30e9\u30d9\u30eb\u3092\u6bd4\u8f03\u3059\u308b\u306e\u304cevaluateSemanticSegmentation\u95a2\u6570\u3067\u3059\u3002metrics\u306b\u306fConfusionMatrix\u3001NormalizedConfusionMatrix\u3001DataSetMetrics\u3001ClassMetrics\u3001ImageMetrics\u306e\uff15\u3064\u306e\u8a55\u4fa1\u7d50\u679c\u304c\u51fa\u529b\u3055\u308c\u307e\u3059\u3002\u305d\u308c\u305e\u308c\u306e\u8a55\u4fa1\u9805\u76ee\u306b\u3064\u3044\u3066\u306f\u4ee5\u4e0b\u306e\u30ea\u30f3\u30af\u3092\u53c2\u8003\u306b\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u4eca\u56de\u306fClassMetrics\u3092\u51fa\u529b\u3055\u305b\u3066\u3044\u307e\u3059\u3002<\/p>\n
> semanticSegmentationMetrics<\/a><\/p>\n
pxdsResults = semanticseg(imdsTest,net,\"WriteLocation\",tempdir); % \u30c6\u30b9\u30c8\u306e\u5b9f\u884c\r\n\r\n% \u5206\u985e\u7d50\u679c\u306e\u8a55\u4fa1\r\nmetrics = evaluateSemanticSegmentation(pxdsResults,pxdsTest);\r\nmetrics.ClassMetrics % \u5404\u30af\u30e9\u30b9\u306e\u8a55\u4fa1\uff08Accuracy\u3001IoU\u3001MeanBFScore\uff09\r\n<\/pre>\n
GlobalAccuracy    MeanAccuracy    MeanIoU    WeightedIoU    MeanBFScore\r\n______________    ____________    _______    ___________    ___________\r\n0.91578          0.92705       0.79753      0.85466        0.72192  \r\n<\/pre>\n
\u30c6\u30b9\u30c8\u7d50\u679c\u306e\u3046\u3061\u4e00\u3064\u3092\u62bd\u51fa\u3057\u3001\u6b63\u89e3\u30e9\u30d9\u30eb\u3068\u91cd\u306d\u3066\u8868\u793a\u3055\u305b\u3066\u307f\u307e\u3059\u3002\u4e0b\u56f3\u306f\u5de6\u304b\u3089\u30c6\u30b9\u30c8\u753b\u50cf\u3001\u6b63\u89e3\u30e9\u30d9\u30eb\u753b\u50cf\u3001\u30c6\u30b9\u30c8\u7d50\u679c\u3001\u6b63\u89e3\u30e9\u30d9\u30eb\u3068\u30c6\u30b9\u30c8\u7d50\u679c\u306e\u6bd4\u8f03\u753b\u50cf\u3067\u3059\u3002\u6b63\u89e3\u30e9\u30d9\u30eb\u3068\u30c6\u30b9\u30c8\u7d50\u679c\u306f\u6c34\u90e8\u306e\u307f\u9752\u8272\u306b\u306a\u308b\u3088\u3046\u306b\u8868\u793a\u3055\u305b\u3066\u3044\u307e\u3059\u3002\u6b63\u89e3\u30e9\u30d9\u30eb\u3068\u30c6\u30b9\u30c8\u7d50\u679c\u306e\u6bd4\u8f03\u753b\u50cf\u306f\u6c34\u90e8\u3092\u6b63\u3057\u304f\u63a8\u5b9a\u3067\u304d\u3066\u3044\u308b\u90e8\u5206\u3092\u767d\u8272\u3001\u305d\u306e\u4ed6\u3092\u6b63\u3057\u304f\u63a8\u5b9a\u3067\u304d\u3066\u3044\u308b\u90e8\u5206\u3092\u9ed2\u8272\u3001\u6c34\u90e8\u3092\u305d\u306e\u4ed6\u3068\u63a8\u5b9a\u3057\u3066\u3044\u308b\u90e8\u5206\u3092\u30de\u30bc\u30f3\u30bf\u3001\u305d\u306e\u4ed6\u3092\u6c34\u90e8\u3068\u63a8\u5b9a\u3057\u3066\u3044\u308b\u90e8\u5206\u3092\u7dd1\u8272\u3067\u8868\u793a\u3055\u305b\u3066\u3044\u307e\u3059\u3002<\/p>\n
resNo = 1; % \u8868\u793a\u3055\u305b\u308b\u30c6\u30b9\u30c8\u7d50\u679c\u306e\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u756a\u53f7\r\nresImage = readimage(imdsTest, resNo); % \u753b\u50cf\u306e\u8aad\u307f\u8fbc\u307f\r\nresLabel = readimage(pxdsTest, resNo); % \u6b63\u89e3\u30e9\u30d9\u30eb\u306e\u8aad\u307f\u8fbc\u307f\r\nC = imread(pxdsResults.Files{resNo}); % \u30c6\u30b9\u30c8\u7d50\u679c\u306e\u8aad\u307f\u8fbc\u307f\r\n\r\ncmap = [0 0 255; 0 0 0] .\/255; % \u30ab\u30e9\u30fc\u30de\u30c3\u30d7\u306e\u8a2d\u5b9a\r\n% \u753b\u50cf\u3068\u6b63\u89e3\u30e9\u30d9\u30eb\u3092\u91cd\u306d\u305f\u753b\u50cf\u306e\u4f5c\u6210\r\nGroundTruth = labeloverlay(resImage,resLabel,'Colormap',cmap,'Transparency',0.6);\r\n% \u753b\u50cf\u3068\u30c6\u30b9\u30c8\u7d50\u679c\u3092\u91cd\u306d\u305f\u753b\u50cf\u306e\u4f5c\u6210\r\nEstimated = labeloverlay(resImage,C,'Colormap',cmap,'Transparency',0.6);\r\n\r\ntiledlayout(1, 4, \"TileSpacing\", \"tight\")\r\nnexttile; imshow(resImage) % \u753b\u50cf\u306e\u8868\u793a\r\ntitle('\u30c6\u30b9\u30c8\u753b\u50cf')\r\nnexttile; imshow(GroundTruth) % \u6b63\u89e3\u30e9\u30d9\u30eb\u306e\u8868\u793a\r\ntitle('\u6b63\u89e3')\r\nnexttile; imshow(Estimated) % \u30c6\u30b9\u30c8\u7d50\u679c\u306e\u8868\u793a\r\ntitle('\u63a8\u5b9a\u7d50\u679c')\r\nnexttile; imshowpair(C == find(classes == 'Water'), resLabel=='Water') % \u6b63\u89e3\u3068\u30c6\u30b9\u30c8\u7d50\u679c\u306e\u6bd4\u8f03\r\ntitle('\u6b63\u89e3\u3068\u63a8\u5b9a\u7d50\u679c\u306e\u6bd4\u8f03')\r\n<\/pre>\n
\"\"<\/a>
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\u304a\u308f\u308a\u306b<\/b><\/h2>\n
MATLAB\u3092\u4f7f\u3063\u3066\u8ee2\u79fb\u5b66\u7fd2\u3092\u884c\u3046\u6d41\u308c\u306b\u3064\u3044\u3066\u3054\u7d39\u4ecb\u3057\u307e\u3057\u305f\u3002\u672c\u8a18\u4e8b\u3067\u306fMATLAB\u304c\u63d0\u4f9b\u3059\u308b\u4e8b\u524d\u5b66\u7fd2\u6e08\u307f\u306eDeepLabv3+\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u4f7f\u7528\u3057\u307e\u3057\u305f\u304c\u3001TensorFlow\u3084PyTorch\u306a\u3069\u4ed6\u306e\u30e2\u30c7\u30eb\u5f62\u5f0f\u306e\u3082\u306e\u3082\u30a4\u30f3\u30dd\u30fc\u30c8\u3057\u3066\u4f7f\u7528\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u8a73\u3057\u304f\u306f\u4ee5\u4e0b\u306e\u30ea\u30f3\u30af\u3092\u3054\u53c2\u8003\u306b\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n
> \u30cb\u30e5\u30fc\u30e9\u30eb \u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u30a4\u30f3\u30dd\u30fc\u30c8\u3068\u30a8\u30af\u30b9\u30dd\u30fc\u30c8<\/a><\/p>\n
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\u7b2c\uff13\u56de\uff1aDeepLabv3+\u3092\u7528\u3044\u305f\u8ee2\u79fb\u5b66\u7fd2 \u8a2d\u5099\u30fb\u60c5\u5831\u6280\u8853\u5ba4\u3000AI\u30fb\u30e1\u30ab\u30c8\u30ed\u30cb Read More …<\/a><\/p>\n","protected":false},"author":7,"featured_media":0,"parent":2226,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_exactmetrics_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"footnotes":""},"_links":{"self":[{"href":"https:\/\/et.kyushu-u.ac.jp\/index.php\/wp-json\/wp\/v2\/pages\/5745"}],"collection":[{"href":"https:\/\/et.kyushu-u.ac.jp\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/et.kyushu-u.ac.jp\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/et.kyushu-u.ac.jp\/index.php\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/et.kyushu-u.ac.jp\/index.php\/wp-json\/wp\/v2\/comments?post=5745"}],"version-history":[{"count":3,"href":"https:\/\/et.kyushu-u.ac.jp\/index.php\/wp-json\/wp\/v2\/pages\/5745\/revisions"}],"predecessor-version":[{"id":5755,"href":"https:\/\/et.kyushu-u.ac.jp\/index.php\/wp-json\/wp\/v2\/pages\/5745\/revisions\/5755"}],"up":[{"embeddable":true,"href":"https:\/\/et.kyushu-u.ac.jp\/index.php\/wp-json\/wp\/v2\/pages\/2226"}],"wp:attachment":[{"href":"https:\/\/et.kyushu-u.ac.jp\/index.php\/wp-json\/wp\/v2\/media?parent=5745"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}