图片 | 方法 | OA | IoU | P | R | F1 |
图片1 | TransUNet | 0.9171 | 0.9033 | 0.6792 | 0.9024 | 0.7751 |
SwinUNet | 0.9306 | 0.9217 | 0.8159 | 0.7256 | 0.7681 | |
U-Net-vgg16 | 0.9508 | 0.8159 | 0.8867 | 0.7905 | 0.8358 | |
| U-Net-Res50 | 0.9221 | 0.9117 | 0.8532 | 0.6711 | 0.7513 |
DeeplabV3+ | 0.9504 | 0.9400 | 0.7993 | 0.9172 | 0.8542 | |
CUT_UNet | 0.9387 | 0.9297 | 0.8026 | 0.8129 | 0.8077 | |
MSANet | 0.9641 | 0.9580 | 0.9210 | 0.8617 | 0.8904 | |
图片2 | TransUNet | 0.9246 | 0.8968 | 0.8637 | 0.8914 | 0.8773 |
SwinUNet | 0.9319 | 0.9088 | 0.9321 | 0.8357 | 0.8812 | |
U-Net-vgg16 | 0.9401 | 0.9197 | 0.9575 | 0.8392 | 0.8944 | |
U-Net-Res50 | 0.9254 | 0.9013 | 0.9383 | 0.8063 | 0.8673 | |
DeeplabV3+ | 0.9505 | 0.9322 | 0.9392 | 0.8944 | 0.9162 | |
CUT_UNet | 0.9487 | 0.9297 | 0.9328 | 0.8949 | 0.9135 | |
MSANet | 0.9594 | 0.9441 | 0.9575 | 0.9061 | 0.9311 | |
图片3 | TransUNet | 0.8675 | 0.7517 | 0.9068 | 0.8147 | 0.8582 |
SwinUNet | 0.8880 | 0.7763 | 0.9223 | 0.8307 | 0.8741 | |
U-Net-vgg16 | 0.8764 | 0.7574 | 0.9267 | 0.8057 | 0.8619 | |
U-Net-Res50 | 0.8884 | 0.7752 | 0.8842 | 0.9170 | 0.9003 | |
DeeplabV3+ | 0.8995 | 0.7925 | 0.8851 | 0.9390 | 0.9113 | |
CUT_UNet | 0.7070 | 0.7851 | 0.9128 | 0.8488 | 0.8796 | |
MSANet | 0.9238 | 0.8446 | 0.9334 | 0.9274 | 0.9304 |