深度学习 Deeplabv3+Resnet101 实战分割项目、多尺度训练、多类别分割：大脑磁共振脑瘤图像分割(训练1000轮)

[train hyper-parameters: Namespace(base_size=400, batch_size=4, crop_size=256, epochs=1000, lr=0.01, lrf=0.001)] [epoch: 1] global correct: 0.9885 precision: ['0.9922', '0.8610'] recall: ['0.9960', '0.7605'] IoU: ['0.9882', '0.6773'] mean IoU: 0.8328 [epoch: 2] global correct: 0.9903 precision: ['0.9958', '0.8313'] recall: ['0.9942', '0.8716'] IoU: ['0.9900', '0.7406'] mean IoU: 0.8653 [epoch: 3] global correct: 0.9903 precision: ['0.9957', '0.8340'] recall: ['0.9943', '0.8685'] IoU: ['0.9901', '0.7405'] mean IoU: 0.8653 [epoch: 4] global correct: 0.9903 precision: ['0.9969', '0.8099'] recall: ['0.9930', '0.9066'] IoU: ['0.9900', '0.7476'] mean IoU: 0.8688 [epoch: 5] global correct: 0.9915 precision: ['0.9971', '0.8349'] recall: ['0.9941', '0.9111'] IoU: ['0.9912', '0.7720'] mean IoU: 0.8816 [epoch: 6] global correct: 0.9913 precision: ['0.9982', '0.8116'] recall: ['0.9928', '0.9449'] IoU: ['0.9910', '0.7749'] mean IoU: 0.8830 [epoch: 7] global correct: 0.9920 precision: ['0.9976', '0.8372'] recall: ['0.9941', '0.9277'] IoU: ['0.9917', '0.7859'] mean IoU: 0.8888 [epoch: 8] global correct: 0.9921 precision: ['0.9979', '0.8344'] recall: ['0.9939', '0.9376'] IoU: ['0.9919', '0.7905'] mean IoU: 0.8912 [epoch: 9] global correct: 0.9923 precision: ['0.9981', '0.8353'] recall: ['0.9939', '0.9435'] IoU: ['0.9921', '0.7955'] mean IoU: 0.8938 [epoch: 10] global correct: 0.9921 precision: ['0.9984', '0.8270'] recall: ['0.9935', '0.9510'] IoU: ['0.9919', '0.7932'] mean IoU: 0.8926 [epoch: 11] global correct: 0.9924 precision: ['0.9980', '0.8405'] recall: ['0.9942', '0.9388'] IoU: ['0.9922', '0.7969'] mean IoU: 0.8945 [epoch: 12] global correct: 0.9931 precision: ['0.9979', '0.8568'] recall: ['0.9949', '0.9375'] IoU: ['0.9928', '0.8105'] mean IoU: 0.9017 [epoch: 13] global correct: 0.9916 precision: ['0.9984', '0.8157'] recall: ['0.9930', '0.9502'] IoU: ['0.9914', '0.7823'] mean IoU: 0.8868 [epoch: 14] global correct: 0.9931 precision: ['0.9985', '0.8470'] recall: ['0.9944', '0.9529'] IoU: ['0.9928', '0.8130'] mean IoU: 0.9029 [epoch: 15] global correct: 0.9931 precision: ['0.9981', '0.8550'] recall: ['0.9948', '0.9436'] IoU: ['0.9929', '0.8134'] mean IoU: 0.9032 [epoch: 16] global correct: 0.9934 precision: ['0.9978', '0.8686'] recall: ['0.9954', '0.9328'] IoU: ['0.9932', '0.8175'] mean IoU: 0.9053 [epoch: 17] global correct: 0.9937 precision: ['0.9982', '0.8668'] recall: ['0.9952', '0.9449'] IoU: ['0.9935', '0.8251'] mean IoU: 0.9093 [epoch: 18] global correct: 0.9930 precision: ['0.9973', '0.8677'] recall: ['0.9954', '0.9189'] IoU: ['0.9928', '0.8060'] mean IoU: 0.8994 [epoch: 19] global correct: 0.9935 precision: ['0.9980', '0.8654'] recall: ['0.9952', '0.9401'] IoU: ['0.9933', '0.8201'] mean IoU: 0.9067 [epoch: 20] global correct: 0.9927 precision: ['0.9981', '0.8448'] recall: ['0.9943', '0.9435'] IoU: ['0.9925', '0.8041'] mean IoU: 0.8983 [epoch: 21] global correct: 0.9925 precision: ['0.9986', '0.8328'] recall: ['0.9937', '0.9562'] IoU: ['0.9923', '0.8022'] mean IoU: 0.8972 [epoch: 22] global correct: 0.9936 precision: ['0.9981', '0.8672'] recall: ['0.9953', '0.9429'] IoU: ['0.9934', '0.8240'] mean IoU: 0.9087 [epoch: 23] global correct: 0.9924 precision: ['0.9986', '0.8287'] recall: ['0.9935', '0.9575'] IoU: ['0.9921', '0.7993'] mean IoU: 0.8957 [epoch: 24] global correct: 0.9938 precision: ['0.9981', '0.8734'] recall: ['0.9955', '0.9425'] IoU: ['0.9937', '0.8292'] mean IoU: 0.9114 [epoch: 25] global correct: 0.9934 precision: ['0.9985', '0.8543'] recall: ['0.9947', '0.9552'] IoU: ['0.9932', '0.8214'] mean IoU: 0.9073 [epoch: 26] global correct: 0.9940 precision: ['0.9981', '0.8763'] recall: ['0.9956', '0.9432'] IoU: ['0.9938', '0.8324'] mean IoU: 0.9131 [epoch: 27] global correct: 0.9938 precision: ['0.9985', '0.8642'] recall: ['0.9951', '0.9533'] IoU: ['0.9936', '0.8291'] mean IoU: 0.9113 [epoch: 28] global correct: 0.9939 precision: ['0.9984', '0.8700'] recall: ['0.9954', '0.9503'] IoU: ['0.9937', '0.8321'] mean IoU: 0.9129 [epoch: 29] global correct: 0.9936 precision: ['0.9987', '0.8549'] recall: ['0.9947', '0.9606'] IoU: ['0.9934', '0.8259'] mean IoU: 0.9097 [epoch: 30] global correct: 0.9939 precision: ['0.9983', '0.8710'] recall: ['0.9954', '0.9496'] IoU: ['0.9938', '0.8325'] mean IoU: 0.9131 [epoch: 31] global correct: 0.9943 precision: ['0.9979', '0.8908'] recall: ['0.9962', '0.9355'] IoU: ['0.9941', '0.8393'] mean IoU: 0.9167 [epoch: 32] global correct: 0.9937 precision: ['0.9984', '0.8638'] recall: ['0.9951', '0.9512'] IoU: ['0.9935', '0.8271'] mean IoU: 0.9103 [epoch: 33] global correct: 0.9935 precision: ['0.9987', '0.8530'] recall: ['0.9946', '0.9603'] IoU: ['0.9933', '0.8239'] mean IoU: 0.9086 [epoch: 34] global correct: 0.9940 precision: ['0.9982', '0.8747'] recall: ['0.9956', '0.9453'] IoU: ['0.9938', '0.8325'] mean IoU: 0.9132 [epoch: 35] global correct: 0.9939 precision: ['0.9986', '0.8654'] recall: ['0.9951', '0.9574'] IoU: ['0.9937', '0.8333'] mean IoU: 0.9135 [epoch: 36] global correct: 0.9942 precision: ['0.9984', '0.8755'] recall: ['0.9956', '0.9520'] IoU: ['0.9940', '0.8385'] mean IoU: 0.9162 [epoch: 37] global correct: 0.9941 precision: ['0.9984', '0.8728'] recall: ['0.9955', '0.9510'] IoU: ['0.9939', '0.8352'] mean IoU: 0.9146 [epoch: 38] global correct: 0.9935 precision: ['0.9988', '0.8501'] recall: ['0.9944', '0.9647'] IoU: ['0.9933', '0.8245'] mean IoU: 0.9089 [epoch: 39] global correct: 0.9939 precision: ['0.9985', '0.8677'] recall: ['0.9952', '0.9546'] IoU: ['0.9938', '0.8333'] mean IoU: 0.9135 [epoch: 40] global correct: 0.9939 precision: ['0.9985', '0.8670'] recall: ['0.9952', '0.9557'] IoU: ['0.9938', '0.8335'] mean IoU: 0.9136 [epoch: 41] global correct: 0.9943 precision: ['0.9984', '0.8775'] recall: ['0.9957', '0.9517'] IoU: ['0.9941', '0.8401'] mean IoU: 0.9171 [epoch: 42] global correct: 0.9943 precision: ['0.9982', '0.8847'] recall: ['0.9960', '0.9447'] IoU: ['0.9942', '0.8411'] mean IoU: 0.9176 [epoch: 43] global correct: 0.9938 precision: ['0.9982', '0.8708'] recall: ['0.9954', '0.9446'] IoU: ['0.9936', '0.8284'] mean IoU: 0.9110 [epoch: 44] global correct: 0.9938 precision: ['0.9984', '0.8656'] recall: ['0.9952', '0.9515'] IoU: ['0.9936', '0.8290'] mean IoU: 0.9113 [epoch: 45] global correct: 0.9943 precision: ['0.9981', '0.8856'] recall: ['0.9960', '0.9435'] IoU: ['0.9942', '0.8410'] mean IoU: 0.9176 [epoch: 46] global correct: 0.9941 precision: ['0.9984', '0.8726'] recall: ['0.9954', '0.9522'] IoU: ['0.9939', '0.8360'] mean IoU: 0.9149 [epoch: 47] global correct: 0.9938 precision: ['0.9987', '0.8595'] recall: ['0.9949', '0.9607'] IoU: ['0.9936', '0.8303'] mean IoU: 0.9120 [epoch: 48] global correct: 0.9941 precision: ['0.9985', '0.8726'] recall: ['0.9954', '0.9540'] IoU: ['0.9939', '0.8374'] mean IoU: 0.9157 [epoch: 49] global correct: 0.9944 precision: ['0.9983', '0.8821'] recall: ['0.9958', '0.9491'] IoU: ['0.9942', '0.8423'] mean IoU: 0.9183 [epoch: 50] global correct: 0.9938 precision: ['0.9988', '0.8581'] recall: ['0.9948', '0.9625'] IoU: ['0.9936', '0.8303'] mean IoU: 0.9119 [epoch: 51] global correct: 0.9943 precision: ['0.9985', '0.8763'] recall: ['0.9956', '0.9540'] IoU: ['0.9941', '0.8408'] mean IoU: 0.9175 [epoch: 52] global correct: 0.9939 precision: ['0.9987', '0.8629'] recall: ['0.9950', '0.9602'] IoU: ['0.9937', '0.8331'] mean IoU: 0.9134 [epoch: 53] global correct: 0.9944 precision: ['0.9981', '0.8880'] recall: ['0.9961', '0.9415'] IoU: ['0.9942', '0.8415'] mean IoU: 0.9179 [epoch: 54] global correct: 0.9945 precision: ['0.9982', '0.8872'] recall: ['0.9961', '0.9452'] IoU: ['0.9943', '0.8438'] mean IoU: 0.9190 [epoch: 55] global correct: 0.9946 precision: ['0.9982', '0.8911'] recall: ['0.9962', '0.9448'] IoU: ['0.9944', '0.8470'] mean IoU: 0.9207 [epoch: 56] global correct: 0.9941 precision: ['0.9987', '0.8691'] recall: ['0.9953', '0.9592'] IoU: ['0.9939', '0.8381'] mean IoU: 0.9160 [epoch: 57] global correct: 0.9944 precision: ['0.9986',