Table 1.
Capsule network architectures trained on the CFHT dataset.
| Convolutional layers |
Capsule layers (C, D) |
||||||
|---|---|---|---|---|---|---|---|
| Number | Feature maps | ConvCaps | Total | ||||
| Model | F. maps | M × (F, N, S) | (F, I, D, S) | 1 | 2 | 3 | weights |
| 1 | 5 | 3 × (5, 256, 1) | (5, 40, 12, 2) | (30, 15) | (1, 25) | 9 861 010 | |
| 2 × (5, 256, 2) | |||||||
| 2 | 2 | 2 × (5, 256, 2) | (5, 40, 12, 2) | (30, 15) | (1, 25) | 4 945 042 | |
| 3 | 2 | 2 × (5, 256, 2) | (5, 40, 12, 2) | (1, 15) | 4 724 992 | ||
| 4 | 1 | 1 × (9, 256, 3) | (5, 40, 12, 2) | (30, 15) | (10, 25) | (1, 25) | 3 428 222 |
| 5 | 1 | 1 × (9, 256, 3) | (5, 40, 12, 2) | (30, 15) | (1, 25) | 3 320 722 | |
| ALED-m | 1 | 1 × (9, 16, 3) | (5, 32, 8, 2) | (24, 12) | (8, 16) | (1, 16) | 216 608 |
| 6 | 1 | 1 × (9, 16, 3) | (5, 24, 8, 2) | (16, 10) | (4, 12) | (1, 12) | 117,280 |
| 7 | 1 | 1 × (9, 16, 3) | (5, 6, 4, 2) | (8, 6) | (4, 8) | (1, 8) | 13 880 |
| 8 | 1 | 1 × (9, 8, 3) | (5, 4, 4, 2) | (8, 6) | (3, 8) | (1, 8) | 5984 |
| 9 | 1 | 1 × (9, 8, 3) | (5, 2, 4, 2) | (6, 4) | (2, 6) | (1, 6) | 2816 |
| 10 | 1 | 1 × (9, 8, 3) | (5, 2, 3, 2) | (6, 4) | (3, 6) | (1, 6) | 2546 |
Notes. Models are defined by the feature map(s), ConvCaps layer, and capsule layer(s), where C is the number of capsules in the layer, D is the number of dimensions per capsule, F is the length of each filter, N is the number of filters used, S is the stride, I is the number of capsule types, and M is the number of feature maps. The ‘Number F. Maps’ column lists the total number of feature maps in each model. The ‘Total Weights’ column lists the total number of trainable weights in each model. All models were trained on 250 images.
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