MigrationNet：在gprMax数据上使用基于DNN的反投影进行地下管道检测

[{"title":"（ 25 个子文件 46.86MB ） MigrationNet：在gprMax数据上使用基于DNN的反投影进行地下管道检测","children":[{"title":"MigrationNet-main","children":[{"title":"requirements.txt <span style='color:#111;'> 53B </span>","children":null,"spread":false},{"title":"migrationnet","children":[{"title":"migrationnet_model.py <span style='color:#111;'> 2.32KB </span>","children":null,"spread":false},{"title":"migrationnet_parts.py <span style='color:#111;'> 2.90KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 39B </span>","children":null,"spread":false},{"title":"__pycache__","children":[{"title":"unet_parts.cpython-38.pyc <span style='color:#111;'> 3.52KB </span>","children":null,"spread":false},{"title":"unet_model.cpython-38.pyc <span style='color:#111;'> 4.33KB </span>","children":null,"spread":false},{"title":"__init__.cpython-38.pyc <span style='color:#111;'> 197B </span>","children":null,"spread":false},{"title":"unet_parts.cpython-37.pyc <span style='color:#111;'> 2.78KB </span>","children":null,"spread":false},{"title":"unet_model.cpython-37.pyc <span style='color:#111;'> 1.26KB </span>","children":null,"spread":false},{"title":"__init__.cpython-37.pyc <span style='color:#111;'> 174B </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"train_mat.py <span style='color:#111;'> 7.91KB </span>","children":null,"spread":false},{"title":"eval.py <span style='color:#111;'> 1.13KB </span>","children":null,"spread":false},{"title":"LICENSE <span style='color:#111;'> 34.32KB </span>","children":null,"spread":false},{"title":"utils","children":[{"title":"dataset_mat.py <span style='color:#111;'> 5.57KB </span>","children":null,"spread":false},{"title":"data_vis.py <span style='color:#111;'> 508B </span>","children":null,"spread":false},{"title":"__pycache__","children":[{"title":"data_vis.cpython-38.pyc <span style='color:#111;'> 708B </span>","children":null,"spread":false},{"title":"dataset.cpython-37.pyc <span style='color:#111;'> 2.36KB </span>","children":null,"spread":false},{"title":"dataset_img.cpython-38.pyc <span style='color:#111;'> 2.42KB </span>","children":null,"spread":false},{"title":"dataset_mat.cpython-38.pyc <span style='color:#111;'> 4.77KB </span>","children":null,"spread":false},{"title":"dataset.cpython-38.pyc <span style='color:#111;'> 2.51KB </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"checkpoints","children":[{"title":"CP_epoch381.pth <span style='color:#111;'> 50.73MB </span>","children":null,"spread":false}],"spread":true},{"title":"README.md <span style='color:#111;'> 520B </span>","children":null,"spread":false},{"title":"predict_mat.py <span style='color:#111;'> 4.06KB </span>","children":null,"spread":false},{"title":"metric.py <span style='color:#111;'> 4.92KB </span>","children":null,"spread":false},{"title":"dice_loss.py <span style='color:#111;'> 1.20KB </span>","children":null,"spread":false}],"spread":false}],"spread":true}]