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V-Net characteristics used for satisfactory training of 2D and 3D models.
| 2D | 3D | |
|---|---|---|
| Architecture | - Number of channels: 16 | - Number of channels: 12 |
| - Number of levels: 2 | - Number of levels: 4 | |
| - Number of convolutions: [4, 4] | - Number of convolutions: [1, 3, 4, 3] | |
| - Bottom convolutions: 2 | - Bottom convolutions: 4 | |
| Learning rate | 0.0005 | 0.00001 |
2D, two-dimensional; 3D, three-dimensional.
A convolution is when two sets of information are merged to form a new function.
