Table 3.
Prediction accuracy on the validation and test sets is listed for each model.
| Accuracy |
Training |
||||||
|---|---|---|---|---|---|---|---|
| Network type | Model | Weights | Validation | Test | Duration | Steps | Time (s) |
| Capsule | 1 | 9 861 010 | 80% | 78% | 14h | 7860 | – |
| 2 | 4 945 042 | 84% | 86% | 15h | 7692 | 0.35 | |
| 3 | 4 724 992 | 50% | 50% | 2h | 11990 | 0.013 | |
| 4 | 3 428 222 | 88% | 88% | 11h | 2965 | 0.79 | |
| 5 | 3 320 722 | 82% | 82% | 11h | 3250 | 0.83 | |
| ALED-m | 216 608 | 88% | 92% | 4h | 1620 | 0.63 | |
| 6 | 117 280 | 88% | 92% | 2h | 1950 | 0.22 | |
| 7 | 13 880 | 88% | 86% | < 1h | 3960 | 0.022 | |
| 8 | 5984 | 88% | 90% | < 1h | 9200 | 0.015 | |
| 9 | 2816 | 84% | 90% | < 1h | 2320 | 0.0085 | |
| 10 | 2546 | 86% | 84% | < 1h | 2015 | 0.0086 | |
| Convolutional | 11 | 22 848 778 | Over-fitting | < 1h | 456 | – | |
| 12 | 4 551 906 | 88% | 86% | < 1h | 1653 | 0.0029 | |
| 13 | 4 551 906 | 82% | 86% | < 1h | 360 | 0.0029 | |
| 14 | 4 551 906 | 60% | 66% | < 1h | 234 | 0.0029 | |
| 15 | 875 586 | 66% | 60% | < 1h | 1000 | 0.0016 | |
Notes. The time taken to train each model is given under the ‘Duration’ column. The total number of times the weights were updated during training is given under the ‘Steps’ column. The time taken to classify a single image is given under the ‘Time’ column, and this number was averaged over 50 images.
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