{"id":29882,"date":"2025-04-20T13:41:26","date_gmt":"2025-04-20T05:41:26","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/29882.html"},"modified":"2025-04-20T13:41:26","modified_gmt":"2025-04-20T05:41:26","slug":"nnu-netv2%e5%9c%a8%e6%9c%8d%e5%8a%a1%e5%99%a8%e4%b8%8a%e4%bd%bf%e7%94%a8%e5%85%a8%e6%b5%81%e7%a8%8b%ef%bc%88%e5%b0%8f%e7%99%bd%e8%be%b9%e8%b8%a9%e5%9d%91%e8%be%b9%e5%ad%a6%e4%b9%a0%e7%9a%84%e8%ae%b0-2","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/29882.html","title":{"rendered":"nnU-Netv2\u5728\u670d\u52a1\u5668\u4e0a\u4f7f\u7528\u5168\u6d41\u7a0b\uff08\u5c0f\u767d\u8fb9\u8e29\u5751\u8fb9\u5b66\u4e60\u7684\u8bb0\u5f55\uff09"},"content":{"rendered":"

\u76ee\u5f55<\/p>\n

\u6e90\u7801\u5730\u5740<\/p>\n

\u6b65\u9aa4\u96f6：\u4e0a\u4f20\u51c6\u5907\u597d\u7684\u6587\u4ef6\u5939（\u8be6\u89c1\u6b65\u9aa4\u56db）<\/p>\n

\u6b65\u9aa4\u4e00：\u5148conda \u65b0\u73af\u5883 python==3.9：<\/p>\n

\u6b65\u9aa4\u4e8c：\u5b89\u88c5pytorch\u5927\u793c\u5305（pytorch 2.0）<\/p>\n

\u6b65\u9aa4\u4e09：\u5b89\u88c5nnunetv2<\/p>\n

\u6b65\u9aa4\u56db：\u914d\u7f6e\u6587\u4ef6\u7ed3\u6784（\u53ef\u4ee5\u5728\u672c\u5730\u63d0\u524d\u51c6\u5907\u597d）<\/p>\n

\u6b65\u9aa4\u4e94：\u6570\u636e\u96c6\u7684\u8f6c\u6362\u4e0ejson\u6587\u4ef6<\/p>\n

\u6b65\u9aa4\u516d：\u4fee\u6539\u73af\u5883\u53d8\u91cf<\/p>\n

\u6b65\u9aa4\u4e03：\u6570\u636e\u96c6\u9884\u5904\u7406<\/p>\n

\u6b65\u9aa4\u516b：\u6a21\u578b\u8bad\u7ec3<\/p>\n

\u6b65\u9aa4\u4e5d：\u6a21\u578b\u63a8\u7406<\/p>\n

\u6b65\u9aa4\u5341：\u6a21\u578b\u9884\u6d4b<\/p>\n

\u6b65\u9aa4\u5341\u4e00：\u9884\u6d4b\u7ed3\u679c\u5206\u6790<\/p>\n

\u6700\u7ec8：\u83b7\u53d6\u8d85\u7b97\u4e0a\u7684\u6587\u4ef6<\/p>\n

\u53c2\u8003\u6587\u732e<\/p>\n

\u80cc\u666f\u4e0e\u603b\u7ed3<\/p>\n

\n

\u6e90\u7801\u5730\u5740<\/h2>\n

GitHub – MIC-DKFZ\/nnUNet<\/p>\n

https:\/\/github.com\/MIC-DKFZ\/nnUNet<\/p>\n

\n

\u4ee5\u4e0b\u90fd\u662f\u76f4\u63a5\u5728\u670d\u52a1\u5668\u7684Terminal<\/span>\u4e0b\u5b8c\u6210\u7684<\/span><\/h2>\n

\u6b65\u9aa4\u96f6：\u4e0a\u4f20\u6570\u636e<\/h2>\n

\u6211\u670d\u52a1\u5668\u7528\u7684featurize，\u4ed6\u53ef\u4ee5\u5728\u5b9e\u4f8b\u5916\u90e8\u9884\u5148\u4e0a\u4f20，\u53c2\u8003：\u6570\u636e\u96c6 | Featurize<\/p>\n

\u5f53\u7136\u4e5f\u53ef\u4ee5\u5728\u5b9e\u4f8b\u91cc\u4e0a\u4f20（\u4f1a\u6bd4\u8f83\u6162）<\/p>\n

\"\"<\/p>\n

\u5728\u53f3\u4fa7\u4efb\u52a1\u6846\u4e0a\u4fa7，\u4e5f\u53ef\u4ee5\u76f4\u63a5\u9009\u62e9\u4e0a\u4f20（\u4e00\u822c\u4ec5\u652f\u6301zip\u7b49\u538b\u7f29\u6587\u4ef6）<\/p>\n

\n

\n

\u6b65\u9aa4\u4e00：\u5148conda \u65b0\u73af\u5883 python==3.9：<\/h2>\n

\u975e\u5fc5\u987b，\u5982\u679c\u670d\u52a1\u5668\u672c\u6765\u73af\u5883\u5c31\u5df2\u7ecf\u662fpytorch2.0+python3.9\u5c31\u6ca1\u95ee\u9898（\u8fd9\u548cnnunet\u521d\u4ee3\u7248\u672c\u914d\u7f6e\u6709\u5f88\u5927\u5dee\u522b）<\/p>\n

\u6a21\u677f：<\/p>\n

conda create -n your_env_name python=x.x<\/span><\/span><\/span><\/p>\n

conda activate your_env_name <\/span><\/span><\/p>\n

conda create -n nnunet python=3.9 #\u8fc7\u7a0b\u4e2dy \u786e\u8ba4
\nconda activate nnunet<\/p>\n

\u00a0\u5e38\u7528conda\u8bed\u53e5\u53c2\u8003：<\/p>\n

Anaconda conda\u5e38\u7528\u547d\u4ee4：\u4ece\u5165\u95e8\u5230\u7cbe\u901a_conda\u547d\u4ee4_\u7b28\u725b\u6162\u8015\u7684\u535a\u5ba2-CSDN\u535a\u5ba2<\/p>\n

\n

\n

\u6b65\u9aa4\u4e8c：\u5b89\u88c5pytorch\u5927\u793c\u5305（\u6ce8\u610f：pytorch 2.0）<\/h2>\n

conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia <\/p>\n

\u4e2d\u95f4\u6709\u62a5\u9519\u91cd\u65b0\u8f93\u5165\u8f93\u5165\u5373\u53ef，\u3010Y\/n\u3011\u8f93\u5165Y\u786e\u8ba4<\/p>\n

\n

\u8865\u5145：\u67e5\u770b\u663e\u5361\u7248\u672c,torch\u5b89\u88c5GPU\u7248\u672c\u7684\u65f6\u5019，\u8981\u5b89\u88c5\u663e\u5361\u4ee5\u4e0b\u7684\u7248\u672c（\u5373\u4e0a\u8ff0\u4ee3\u7801pytorch-cuda=11.7，\u8fd9\u4e2a11.7\u8981\u5c0f\u4e8e\u663e\u5361\u7684cuda-version\u503c）<\/p>\n

（\u672c\u5730）windows+R，\u6253\u5f00Windows<\/span><\/span>\u547d\u4ee4，\u8f93\u5165cmd<\/span>，<\/span><\/span>\u518d<\/span><\/span>\u8f93\u5165nvidia-smi<\/span><\/p>\n

\"\"<\/p>\n

（\u670d\u52a1\u5668\u7684terminal）\u76f4\u63a5\u8f93\u5165nvidia-smi<\/span><\/p>\n

\"\"<\/p>\n

\n

\n

\u6b65\u9aa4\u4e09：\u5b89\u88c5nnunetv2<\/h2>\n

git clone https:\/\/github.com\/MIC-DKFZ\/nnUNet.git
\ncd nnUNet
\npip install -e . <\/p>\n

\u6ce8\u610f\u6700\u540e\u4e00\u884ce\u540e\u9762\u8fd8\u6709\u4e00\u4e2a\u70b9！<\/span><\/p>\n

\u81ea\u5df1\u5c31\u4f1a\u5b89\u88c5nnunetv2\u4e86！<\/span><\/p>\n

\"\"<\/p>\n

\u4e00\u5957\u6d41\u7a0b\u540e pip list \u91cc\u9762\u6709 nnunetv2 \u5373\u5b8c\u6210（\u6216\u8005 pip show nnunetv2）<\/span><\/p>\n

\"\"<\/p>\n

\n

\n

\n

\u6b65\u9aa4\u56db：\u5f00\u59cb\u914d\u7f6e\u6587\u4ef6\u7ed3\u6784（\u53ef\u4ee5\u5728\u672c\u5730\u63d0\u524d\u51c6\u5907\u597d）<\/h2>\n

\u8fd9\u90e8\u5206\u8fd8\u662f\u548c\u4ee5\u524d\u5dee\u4e0d\u591a<\/p>\n

DATASET\u6587\u4ef6\u5939\u4e0b\u65b9\u521b\u5efa\u4e09\u4e2a\u6587\u4ef6\u5939，\u5206\u522b\u4e3a\"\"<\/p>\n\n\n\n\n\n
nnUNet_raw<\/td>\n\u5b58\u653e\u521d\u59cb\u7684\u5f71\u50cf\u548cmask\u6587\u4ef6<\/td>\n<\/tr>\n
\n

nnUNet_preprocessed<\/p>\n<\/td>\n

\u9884\u5904\u7406\u540e\u7684\u4e2d\u95f4\u6587\u4ef6\u5b58\u653e\u5730<\/td>\n<\/tr>\n
nnUNet_trained_models<\/td>\n\u8bad\u7ec3\u5b8c\u6210\u7684\u6a21\u578b\u3001\u8bad\u7ec3\u8fc7\u7a0b\u5b58\u653e\u5730<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u5728nnUNet_raw\u6587\u4ef6\u5939\u4e0b\u65b9\u5728\u521b\u7acb\u4e00\u4e2a"DatsetXXX_NAME<\/span><\/span>\u201d<\/span><\/span><\/p>\n

\u53c2\u8003\u540d：<\/span><\/span>\u201cDataset101_Lung\u201d<\/p>\n

（\u6570\u5b57\u7f16\u53f7\u662f000-999<\/span><\/span>\u7684\u4e09\u4f4d\u6570\u5b57<\/span><\/span>，\u6700\u597d\u5927\u4e8e<\/span><\/span>200<\/span><\/span>，\u56e0\u4e3a\u6bd4\u8f83\u5c0f\u7684\u6570\u5b57\u5df2\u7ecf\u88ab<\/span><\/span>fabian<\/span><\/span>\u7684<\/span><\/span>task id<\/span><\/span>\u5360\u7528，\u4ee5\u514d\u5728\u54ea\u91cc\u83ab\u540d\u5176\u5999\u51b2\u7a81\u4e86<\/span><\/span>）<\/p>\n

\"\"<\/p>\n

Dataset101_Lung\u4e0b\u56db\u4e2a\u6587\u4ef6\u5939+\u4e00\u4e2ajson\u6587\u4ef6<\/p>\n\n\n\n\n\n\n\n
imagesTr<\/td>\ntrain\u8bad\u7ec3\u96c6\u7684\u533b\u5b66\u5f71\u50cf（CT\u3001MRI\u7b49）<\/td>\n<\/tr>\n
imagesTs<\/td>\ntest \u68c0\u9a8c\u96c6\u7684\u533b\u5b66\u5f71\u50cf（CT\u3001MRI\u7b49）（optional）<\/td>\n<\/tr>\n
labelsTr<\/td>\ntrain\u8bad\u7ec3\u96c6\u7684label\u6d82\u5c42<\/td>\n<\/tr>\n
labelsTs<\/td>\ntest \u8bad\u7ec3\u96c6\u7684label\u6d82\u5c42（optional）<\/td>\n<\/tr>\n
dataset.json<\/td>\n\u6587\u4ef6\u4fe1\u606f，\u8be6\u89c1\u6b65\u9aa4\u4e94<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u7279\u522b\u6ce8\u610f，\u53d6\u540d\u539f\u5219\u5982\u4e0b：<\/p>\n

images\u91cc\u9762\u5728.nii.gz\u524d\u8981\u52a0\u51650000（\u4ee3\u8868\u662f\u6a21\u60010，\u82e5\u6709\u5176\u4ed6\u6a21\u6001\u7684\u5c31\u4f9d\u6b210001，\u8fd9\u4e2a\u6570\u5b57\u5728json\u6587\u4ef6\u91cc\u8981\u6709\u4f53\u73b0，\u6bd4\u59820\u6a21\u6001\u662fCT，1\u6a21\u6001\u662fMRI，\u8be6\u60c5\u8bf7\u89c1\u6b65\u9aa46），label\u662f\u540c\u540d\u6587\u4ef6，\u4f46\u6ca1\u67090000,0001<\/span><\/p>\n

\"\"<\/p>\n

\u4e0d\u540c\u4e8ennunet\u521d\u4ee3\u7248\u672c\u5728nnUNet_raw\u91cc\u9762\u4e0d\u9700\u8981\u989d\u5916\u7684 cropped<\/p>\n

\n

\n

\u6b65\u9aa4\u4e94：<\/span>\u6570\u636e\u96c6\u7684\u8f6c\u6362\u4e0ejson\u6587\u4ef6<\/span><\/span><\/h2>\n

\u6570\u636e\u96c6\u8f6c\u5316\u8fd9\u6b21\u6ca1\u7528\u4e0a，\u5c31\u5148\u4e0d\u7ec6\u8bf4\u4e86，\u540e\u7eed\u518d\u8865\u5145<\/p>\n

json\u6587\u4ef6\u76f8\u8f83nnunet\u521d\u4ee3\u7248\u672c\u6539\u4e86\u597d\u591a\u3002\u4f46\u8003\u8651\u5230\u5176\u5b9e\u5199\u7684\u6bd4\u539f\u5148\u7248\u672c\u66f4\u7b80\u6d01\u4e86，\u53ef\u4ee5\u8ba4\u4e3a\u662f\u4ef6\u597d\u4e8b\u60c5\u5427：<\/p>\n

\n

\u6211\u4e00\u5f00\u59cb\u7528\u4e86\u521d\u4ee3\u7684json\u60e8\u906d\u62a5\u9519： \"\"<\/p>\n

\u6807\u51c6\u683c\u5f0f\u7684\u6587\u4ef6\u5982\u4e0b：<\/p>\n

\"\"<\/p>\n

nnunetV2\u7ed9\u4e86\u76f8\u5e94\u7684\u811a\u672c：<\/p>\n

\u53ef\u53c2\u8003：\u00a0nnU-Net v2\u7684\u73af\u5883\u914d\u7f6e\u5230\u8bad\u7ec3\u81ea\u5df1\u7684\u6570\u636e\u96c6（\u8be6\u7ec6\u6b65\u9aa4）_\u5c0f\u841d\u5317hh\u7684\u535a\u5ba2-CSDN\u535a\u5ba2<\/p>\n

\"\"<\/p>\n

\u5176\u4e2dlabels 0,1,2,3\u8bf7\u52a1\u5fc5\u5bf9\u5e94\u597d！<\/span>\u6240\u6709mask\u6587\u4ef6\u4e0a\u51fa\u73b0\u7684\u6570\u5b57\u90fd\u8981\u6709\u5f52\u5c5e，\u76ee\u524dnnunet\u5c1a\u4e0d\u652f\u6301\u91cd\u5408\u7684mask（\u5373\u4e0d\u540cmask\u4e4b\u95f4\u6709\u4ea4\u96c6）\u3002\u5bf9\u6587\u4ef6\u91ccmask\u6570\u5b57\u4f4d\u7f6e\u7684，\u53ef\u4ee5\u6253\u5f00python\u53bb\u8bfb\u6587\u4ef6\u6216\u8005，ITK-SNAP\u8bfb\u4e00\u4e0b：ITK-SNAP Home (itksnap.org)<\/p>\n

\"\"<\/p>\n

Label1\u662f\u7ea2\u8272，label2\u662f\u7eff\u8272，\u5219\u8fd9\u5c42mask\u7684\u77e9\u9635，\u5bf9\u5e94\u7ea2\u8272\u533a\u57df\u4f4d\u7f6e\u662f1，\u7eff\u8272\u7684\u662f2，\u5176\u4f59（background）\u662f0<\/p>\n

（\u5907\u6ce8：ITK-SNAP，\u5305\u62ecpython\u91cc\u9762\u4e00\u4e9b\u9700\u8981\u8c03\u7528itk\u5305，\u90fd\u662f\u5728\u8def\u5f84\u4e2d\u4e0d\u80fd\u51fa\u73b0\u4e2d\u6587\u5b57，\u751a\u81f3\u201c-\u201d\u90fd\u4e0d\u80fd\u51fa\u73b0，\u53ef\u4ee5\u6539\u7528\u4e3a\u201c_\u201d）<\/p>\n

\n

\n

\u6b65\u9aa4\u516d：\u4fee\u6539\u73af\u5883\u53d8\u91cf（\u4e09\u4e2a）<\/h2>\n

\u8fd9\u4e00\u6b65\u5373\u4e3a\u91cd\u8981，\u5728\u670d\u52a1\u5668\u4e0a\u9762\u65e0\u6cd5ctrl+H\u7684\u6253\u5f00.bashrc\u6587\u4ef6，\u4e00\u822c\u7684\u672c\u5730\u4e5f\u9700\u8981\u4fee\u6539\u73af\u5883\u53d8\u91cf，\u7528\u8fd9\u4e2a\u65b9\u6cd5\u5e94\u8be5\u4e5f\u53ef\u4ee5<\/p>\n

\u9700\u8981\u7528\u5230vim<\/span><\/p>\n

1\u3001\u8fdb\u5165vim<\/p>\n

vim ~\/.zshrc <\/p>\n

vim ~\/.bashrc （\u5728\u6211\u7684\u4f7f\u7528\u4e2d\u662f vim ~\/.zshrc<\/span><\/span><\/p>\n

2\u3001<\/span>\u8fdb\u5165\u7f16\u8f91\u6a21\u5f0f<\/p>\n

:a <\/p>\n

（\u6ce8\u610f\u5192\u53f7！）<\/span><\/p>\n

\n

3\u3001\u4fee\u6539\u4e09\u4e2a\u73af\u5883\u53d8\u91cf：<\/p>\n

\u8bf7\u4e00\u4e00\u5bf9\u5e94\u597d\u6b65\u9aa4\u56db \u4e0a\u8ff0\u6587\u4ef6\u683c\u5f0f\u91cc\u7684\u4e09\u4e2a\u6587\u4ef6\u5939<\/span><\/p>\n

（\u53ef\u4ee5\u70b9\u5230\u8fd9\u4e2a\u6587\u4ef6\u5939\u7684\u4f4d\u7f6e，\u7136\u540e\u53f3\u952e\u5f39\u5f00，\u70b9\u51fb\u201c\u590d\u5236\u7edd\u5bf9\u8def\u5f84\u201d）<\/span> \u00a0<\/p>\n

export nnUNet_preprocessed="\/home\/featurize\/nnUNet\/nnUNetFrame\/DATASET\/nnUNet_preprocessed"<\/p>\n

export nnUNet_results="\/home\/featurize\/nnUNet\/nnUNetFrame\/DATASET\/nnUNet_trained_models"<\/p>\n

export nnUNet_raw="\/home\/featurize\/nnUNet\/nnUNetFrame\/DATASET\/nnUNet_raw" <\/p>\n

\n

4\u3001\u9000\u51fa\u7f16\u8f91\u6a21\u5f0f<\/p>\n

\u6309<\/span>\u00a0 Esc<\/span><\/span><\/p>\n

5\u3001<\/span>\u9000\u51fa\u00a0 \u8bb0\u5f97\u5148\u6309\u4e0a\u4e00\u6b65\u7684Esc<\/p>\n

:wq<\/p>\n

\u8f93\u5165 \u00a0:wq <\/span><\/span>\u00a0（ ：\u4e0d\u53ef\u5ffd\u7565，\u4fdd\u5b58\u9000\u51fa）<\/p>\n

\u8f93\u5165 :q!\u00a0\u00a0\u00a0 <\/span><\/span>（\u4e0d\u4fdd\u5b58\u9000\u51fa）<\/p>\n

6\u3001\u9000\u51fa\u540e\u52a0\u8f7d\u66f4\u65b0\u540e\u7684\u914d\u7f6e<\/p>\n

source ~\/.zshrc <\/p>\n

source ~\/.bashrc\u00a0\u00a0\u00a0 （\u6211\u7684\u662fsource ~\/zshrc<\/span>）<\/p>\n

7\u3001\u66f4\u65b0\u540e\u91cd\u65b0\u6fc0\u6d3b\u4e4b\u524d\u914d\u7f6e\u7684\u73af\u5883<\/p>\n

conda activate your_env_name <\/span><\/span><\/p>\n

conda activate nnunet <\/p>\n

\u5e38\u89c1\u95ee\u9898： \u8fd9\u4e2a\u5c31\u662f\u5178\u578b\u73af\u5883\u53d8\u91cf\u6ca1\u914d\u597d，\u62a5\u9519\u5199\u7684\u5f88\u660e\u663e\u4e86，\u5199\u660e\u4e86nnUNet_result\u53d8\u91cf\u4e0d\u5b58\u5728<\/p>\n

\u6f5c\u5728\u7684\u95ee\u9898：<\/p>\n

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    \n

    \n

    \u6b65\u9aa4\u4e03：\u6570\u636e\u96c6\u9884\u5904\u7406<\/h2>\n

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    nnUNetv2_plan_and_preprocess -d 101 –verify_dataset_integrity <\/p>\n

    \"\"<\/p>\n

    \u5e38\u89c1\u62a5\u9519：RAM\u7206\u4e86，\u6211\u5f53\u65f6\u5728\u62a5\u9519\u56de\u6eaf\u4e00\u4e2a\u4e00\u4e2a\u53bb\u67e5，\u5728plan_preprocess_entrypoints.py\u91cc\u627e\u5230\u4e86\u53ef\u4ee5\u4fee\u6539\u7684\u5730\u65b9<\/p>\n

    \"\"<\/p>\n

    \"\"<\/p>\n

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    \n

    \n

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    nnUNetv2_train TASK-NUM\u00a02d FOLD\u00a0<\/span><\/span>\u00a0<\/p>\n

    nnUNetv2_train 101 2d 0
    \nnnUNetv2_train 101 2d 1
    \nnnUNetv2_train 101 2d 2
    \nnnUNetv2_train 101 2d 3
    \nnnUNetv2_train 101 2d 4 <\/p>\n

    \u00a0# \u5176\u4e2d101\u8868\u793a\u6570\u636e\u96c6ID，\u4e0a\u8ff0\u63d0\u53ca\u8fc7\u30020\u8868\u793a\u7b2c1\u6298<\/p>\n

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    \"\"<\/p>\n

    \u5f00\u59cb\u8bad\u7ec3\u4e86！Win！<\/p>\n

    \n

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    \"\"<\/p>\n

    \n

    3.\u00a0<\/span><\/span>\u5728\u6b63\u5e38\u8fd0\u884c\u4ee3\u7801\u540e\u9047\u5230\u7684\u95ee\u9898：<\/span><\/span><\/p>\n

    Ctrl+C<\/span><\/span>\u4f1a\u4e2d\u65ad\u5b9e\u9a8c，<\/span><\/span>\u539f\u6765\u6307\u4ee4\u540e\u9762\u52a0<\/span><\/span>–c<\/span><\/span>\u53ef\u4ee5\u63a5\u7740\u8fd0\u884c，\u518d\u4e5f\u4e0d\u62c5\u5fc3\u8dd1\u4e86\u4e00\u6574\u665a\u7684\u4e1c\u897f\u767d\u8dd1\u4e86！<\/span><\/span><\/p>\n

    \n

    4.\u7ed3\u679c\u67e5\u770b，\u6bcf\u4e00\u6298\u8bad\u7ec3\u5b8c\u90fd\u53ef\u4ee5\u770b\u770b\u8bad\u7ec3\u7684\u8fc7\u7a0b：<\/p>\n

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    \"\"<\/p>\n

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    \"\"<\/p>\n

    \n

    \n

    \u6b65\u9aa4\u4e5d：\u6a21\u578b\u63a8\u7406<\/h2>\n

    \u53ef\u53c2\u8003：<\/p>\n

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    nnUnet v2\u9879\u76ee\u5b66\u4e60\u8bb0\u5f55，\u8bad\u7ec3\u81ea\u5b9a\u4e49\u6a21\u578b（\u4e0d\u662fUnet） – \u77e5\u4e4e (zhihu.com)<\/p>\n

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    \u2191\u53c2\u8003，\u53ef\u7528\u4e8e\u63d0\u524d\u63a8\u7406<\/span><\/span><\/p>\n

    \n

    nnUNetv2_find_best_configuration 101 -f 0 1 2 3 4 -c 2d <\/p>\n

    \"\"<\/p>\n

    \n

    \"\"<\/p>\n

    \n

    \n

    \n

    \u6b65\u9aa4\u5341：\u6a21\u578b\u9884\u6d4b<\/h2>\n

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    \"\"<\/p>\n

    \n

    \n

    \u6b65\u9aa4\u5341\u4e00：\u9884\u6d4b\u7ed3\u679c\u5206\u6790<\/h2>\n

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    \u9884\u6d4b\u7ed3\u679c\u5206\u6790：<\/span><\/p>\n

    \/home\/featurize\/nnUNet\/nnunetv2\/evaluation\/evaluate_predictions.py<\/span><\/p>\n

    \"\"<\/span><\/p>\n

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    \"\"<\/p>\n

    \"\"<\/p>\n

    \u5206\u522b\u5bf9\u5e94\u7740label1,2,3\u7684\u8bc4\u4ef7\u6307\u6807<\/span><\/p>\n

    \n

    \n

    \u6700\u7ec8：\u83b7\u53d6\u670d\u52a1\u5668\u4e0a\u7684\u6587\u4ef6<\/h2>\n

    \u00a0\u628a\u6587\u4ef6\u538b\u7f29，\u7136\u540e\u53f3\u952e\u5f39\u5f00download<\/span><\/span><\/p>\n

    \n

    \n

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    \u8fd8\u662f\u591a\u770b\u770b\u5f00\u53d1\u8005\u7684github\u548c\u5199\u7684documentation，\u8fd8\u662f\u6709\u4e0d\u5c11\u542f\u53d1\u7684<\/p>\n

    \n

    \u00a0nnU-Net v2\u7684\u73af\u5883\u914d\u7f6e\u5230\u8bad\u7ec3\u81ea\u5df1\u7684\u6570\u636e\u96c6（\u8be6\u7ec6\u6b65\u9aa4）_\u5c0f\u841d\u5317hh\u7684\u535a\u5ba2-CSDN\u535a\u5ba2<\/p>\n

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    GitHub – MIC-DKFZ\/nnUNet<\/p>\n

    nnUNet_v2\/documentation\/how_to_use_nnunet.md at master \u00b7 DIAGNijmegen\/nnUNet_v2 \u00b7 GitHub<\/p>\n

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    \n

    \u4e3b\u8981\u95ee\u9898：nnunet\u548cnnunetv2\u6709\u4e00\u5b9a\u5dee\u5f02：<\/p>\n

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    \n

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    \n

    \n

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    \n

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