基于Swin-Transformer和Unet 项目、自适应多尺度训练、多类别分割：裂缝分割实战

[train hyper-parameters: Namespace(batch_size=4, epochs=40, lr=0.005, lrf=0.01)] [epoch: 1] global correct: 0.9836 precision: ['0.9836', '0.0000', '1.0000', '0.0000'] recall: ['1.0000', '0.0000', '0.0000', '0.0000'] IoU: ['0.9836', '0.0000', '0.0000', '0.0000'] mean IoU: 0.2459 [epoch: 2] global correct: 0.9846 precision: ['0.9877', '0.0000', '0.4567', '0.3667'] recall: ['0.9982', '0.0000', '0.3550', '0.0001'] IoU: ['0.9860', '0.0000', '0.2496', '0.0001'] mean IoU: 0.3089 [epoch: 3] global correct: 0.9759 precision: ['0.9959', '0.0000', '0.2372', '0.3812'] recall: ['0.9856', '0.0000', '0.7791', '0.0542'] IoU: ['0.9817', '0.0000', '0.2222', '0.0498'] mean IoU: 0.3134 [epoch: 4] global correct: 0.9839 precision: ['0.9912', '0.0000', '0.4079', '0.5714'] recall: ['0.9951', '0.0000', '0.6690', '0.0002'] IoU: ['0.9864', '0.0000', '0.3394', '0.0002'] mean IoU: 0.3315 [epoch: 5] global correct: 0.9841 precision: ['0.9953', '0.0000', '0.5245', '0.4968'] recall: ['0.9886', '0.0000', '0.7902', '0.6507'] IoU: ['0.9840', '0.0000', '0.4604', '0.3922'] mean IoU: 0.4591 [epoch: 6] global correct: 0.9840 precision: ['0.9960', '0.0000', '0.5862', '0.4587'] recall: ['0.9878', '0.0000', '0.7291', '0.7869'] IoU: ['0.9839', '0.0000', '0.4813', '0.4080'] mean IoU: 0.4683 [epoch: 7] global correct: 0.9806 precision: ['0.9976', '0.0000', '0.4699', '0.4352'] recall: ['0.9828', '0.0000', '0.8672', '0.8378'] IoU: ['0.9804', '0.0000', '0.4384', '0.4014'] mean IoU: 0.4551 [epoch: 8] global correct: 0.9862 precision: ['0.9950', '0.0000', '0.5467', '0.5853'] recall: ['0.9910', '0.0000', '0.8076', '0.6035'] IoU: ['0.9861', '0.0000', '0.4837', '0.4227'] mean IoU: 0.4731 [epoch: 9] global correct: 0.9852 precision: ['0.9968', '0.0000', '0.5522', '0.5141'] recall: ['0.9882', '0.0000', '0.8176', '0.7956'] IoU: ['0.9851', '0.0000', '0.4917', '0.4541'] mean IoU: 0.4827 [epoch: 10] global correct: 0.9867 precision: ['0.9960', '0.0000', '0.5505', '0.5929'] recall: ['0.9905', '0.0000', '0.8339', '0.6890'] IoU: ['0.9866', '0.0000', '0.4961', '0.4677'] mean IoU: 0.4876 [epoch: 11] global correct: 0.9872 precision: ['0.9959', '0.0000', '0.5838', '0.5876'] recall: ['0.9911', '0.0000', '0.7808', '0.7282'] IoU: ['0.9871', '0.0000', '0.5016', '0.4819'] mean IoU: 0.4926 [epoch: 12] global correct: 0.9831 precision: ['0.9977', '0.0000', '0.4977', '0.4851'] recall: ['0.9852', '0.0000', '0.8838', '0.8397'] IoU: ['0.9830', '0.0000', '0.4672', '0.4439'] mean IoU: 0.4735 [epoch: 13] global correct: 0.9866 precision: ['0.9967', '0.0000', '0.5614', '0.5651'] recall: ['0.9898', '0.0000', '0.8386', '0.7595'] IoU: ['0.9865', '0.0000', '0.5067', '0.4793'] mean IoU: 0.4931 [epoch: 14] global correct: 0.9844 precision: ['0.9976', '0.0000', '0.5432', '0.4902'] recall: ['0.9865', '0.0000', '0.8625', '0.8502'] IoU: ['0.9842', '0.0000', '0.4999', '0.4512'] mean IoU: 0.4838 [epoch: 15] global correct: 0.9818 precision: ['0.9984', '0.0000', '0.4797', '0.4619'] recall: ['0.9832', '0.0000', '0.9144', '0.8844'] IoU: ['0.9816', '0.0000', '0.4591', '0.4356'] mean IoU: 0.4691 [epoch: 16] global correct: 0.9840 precision: ['0.9978', '0.0000', '0.5365', '0.4824'] recall: ['0.9860', '0.0000', '0.8717', '0.8614'] IoU: ['0.9838', '0.0000', '0.4972', '0.4477'] mean IoU: 0.4822 [epoch: 17] global correct: 0.9869 precision: ['0.9968', '0.0000', '0.5902', '0.5556'] recall: ['0.9899', '0.0000', '0.8266', '0.7914'] IoU: ['0.9868', '0.0000', '0.5252', '0.4846'] mean IoU: 0.4992 [epoch: 18] global correct: 0.9837 precision: ['0.9980', '0.0000', '0.5213', '0.4844'] recall: ['0.9855', '0.0000', '0.8898', '0.8638'] IoU: ['0.9836', '0.0000', '0.4897', '0.4500'] mean IoU: 0.4808 [epoch: 19] global correct: 0.9825 precision: ['0.9982', '0.0000', '0.4990', '0.4663'] recall: ['0.9840', '0.0000', '0.9057', '0.8772'] IoU: ['0.9823', '0.0000', '0.4744', '0.4377'] mean IoU: 0.4736 [epoch: 20] global correct: 0.9857 precision: ['0.9975', '0.0000', '0.5674', '0.5170'] recall: ['0.9880', '0.0000', '0.8547', '0.8394'] IoU: ['0.9855', '0.0000', '0.5175', '0.4705'] mean IoU: 0.4934 [epoch: 21] global correct: 0.9863 precision: ['0.9973', '0.0000', '0.5858', '0.5293'] recall: ['0.9889', '0.0000', '0.8381', '0.8311'] IoU: ['0.9862', '0.0000', '0.5263', '0.4779'] mean IoU: 0.4976 [epoch: 22] global correct: 0.9853 precision: ['0.9975', '0.0000', '0.5425', '0.5216'] recall: ['0.9876', '0.0000', '0.8631', '0.8291'] IoU: ['0.9851', '0.0000', '0.4996', '0.4710'] mean IoU: 0.4889 [epoch: 23] global correct: 0.9856 precision: ['0.9974', '0.0000', '0.5268', '0.5495'] recall: ['0.9881', '0.0000', '0.8863', '0.7930'] IoU: ['0.9855', '0.0000', '0.4935', '0.4806'] mean IoU: 0.4899 [epoch: 24] global correct: 0.9847 precision: ['0.9978', '0.0000', '0.5305', '0.5088'] recall: ['0.9867', '0.0000', '0.8875', '0.8437'] IoU: ['0.9845', '0.0000', '0.4971', '0.4650'] mean IoU: 0.4866 [epoch: 25] global correct: 0.9848 precision: ['0.9978', '0.0000', '0.5369', '0.5081'] recall: ['0.9868', '0.0000', '0.8819', '0.8466'] IoU: ['0.9847', '0.0000', '0.5009', '0.4652'] mean IoU: 0.4877 [epoch: 26] global correct: 0.9853 precision: ['0.9975', '0.0000', '0.5247', '0.5399'] recall: ['0.9876', '0.0000', '0.8884', '0.8126'] IoU: ['0.9852', '0.0000', '0.4922', '0.4801'] mean IoU: 0.4894 [epoch: 27] global correct: 0.9866 precision: ['0.9971', '0.0000', '0.5662', '0.5575'] recall: ['0.9894', '0.0000', '0.8535', '0.7933'] IoU: ['0.9865', '0.0000', '0.5160', '0.4868'] mean IoU: 0.4973 [epoch: 28] global correct: 0.9870 precision: ['0.9968', '0.0000', '0.6004', '0.5507'] recall: ['0.9901', '0.0000', '0.8019', '0.8047'] IoU: ['0.9869', '0.0000', '0.5229', '0.4858'] mean IoU: 0.4989 [epoch: 29] global correct: 0.9855 precision: ['0.9975', '0.0000', '0.5421', '0.5312'] recall: ['0.9878', '0.0000', '0.8728', '0.8276'] IoU: ['0.9854', '0.0000', '0.5024', '0.4783'] mean IoU: 0.4915 [epoch: 30] global correct: 0.9866 precision: ['0.9972', '0.0000', '0.5707', '0.5521'] recall: ['0.9892', '0.0000', '0.8535', '0.8074'] IoU: ['0.9865', '0.0000', '0.5198', '0.4879'] mean IoU: 0.4985 [epoch: 31] global correct: 0.9853 precision: ['0.9975', '0.0000', '0.5133', '0.5519'] recall: ['0.9876', '0.0000', '0.9003', '0.8013'] IoU: ['0.9852', '0.0000', '0.4857', '0.4855'] mean IoU: 0.4891 [epoch: 32] global correct: 0.9868 precision: ['0.9970', '0.0000', '0.5838', '0.5541'] recall: ['0.9897', '0.0000', '0.8375', '0.7956'] IoU: ['0.9867', '0.0000', '0.5244', '0.4850'] mean IoU: 0.4990 [epoch: 33] global correct: 0.9869 precision: ['0.9969', '0.0000', '0.5747', '0.5671'] recall: ['0.9898', '0.0000', '0.8483', '0.7833'] IoU: ['0.9868', '0.0000', '0.5211', '0.4902'] mean IoU: 0.4995 [epoch: 34] global correct: 0.9867 precision: ['0.9970', '0.0000', '0.5616', '0.5692'] recall: ['0.9896', '0.0000', '0.8622', '0.7778'] IoU: ['0.9866', '0.0000', '0.5154', '0.4896'] mean IoU: 0.4979 [epoch: 35] global correct: 0.9868 precision: ['0.9970', '0.0000', '0.5763', '0.5566'] recall: ['0.9896', '0.0000', '0.8482', '0.7942'] IoU: ['0.9867', '0.0000', '0.5224', '0.4864'] mean IoU: 0.4989 [epoch: 36] global correct: 0.9868 precision: ['0.9970', '0.0000', '0.5772', '0.5593'] recall: ['0.9896', '0.0000', '0.8484', '0.7940'] IoU: ['0.9867', '0.0000', '0.5232', '0.4884'] mean IoU: 0.4996 [epoch: 37] global correct: 0.9858 precision: ['0.9976', '0.0000', '0.5537', '0.5310'] recall: ['0.9880', '0.0000', '0.8710', '0.8331'] IoU: ['0.9857', '0.0000', '0.5117', '0.4799'] mean IoU: 0.4943 [epoch: 38] global correct: 0.9863 precision: ['0.9973', '0.0000', '0.5588', '0.5496'] recall: ['0.9888', '0.0000', '0.8665', '0.8101'] IoU: ['0.9862', '0.0000', '0.5145', '0.4869'] mean IoU: 0.4969 [epoch: 39] global correct: 0.9864 precision: ['0.9972', '0.0000', '0.5644', '0.5505'] recall: ['0.9890', '0.0000', '0.8600', '0.8080'] IoU: ['0.9863', '0.0000', '0.5169', '0.4868'] mean IoU: 0.4975 [epoch: 40] global correct: 0.9867 precision: ['0.9971', '0.0000', '0.5710', '0.5562'] recall: ['0.9894', '0.0000', '0.8531', '0.