Trained and validated on Sheep No. | No. of patterns in the Train and Validation Dataset | Tested on Sheep No. | No. of patterns in the Test-set | TP hits | TN hits | FP hits | FN hits | Sensitivity [%] | Selectivity [%] | Precision [%] | Accuracy [%] |
2,3,4,5,6,7 | 4567 | 1 | 443 | 166 | 255 | 15 | 7 | 96.0 | 94.4 | 91.7 | 95.0 |
1,3,4,5,6,7 | 4751 | 2 | 259 | 110 | 149 | 0 | 0 | 100 | 100 | 100 | 100 |
1,2,4,5,6,7 | 4731 | 3 | 279 | 81 | 188 | 8 | 2 | 97.6 | 95.6 | 91.0 | 96.4 |
1,2,3,5,6,7 | 3372 | 4 | 1638 | 823 | 734 | 80 | 1 | 99.9 | 90.2 | 91.1 | 95.1 |
1,2,3,4,6,7 | 4088 | 5 | 922 | 452 | 463 | 4 | 3 | 99.3 | 99.1 | 99.1 | 99.2 |
1,2,3,4,5,7 | 4466 | 6 | 544 | 230 | 306 | 7 | 1 | 99.6 | 97.8 | 97.0 | 98.5 |
1,2,3,4,5,6 | 4085 | 7 | 925 | 207 | 715 | 1 | 2 | 99.0 | 99.9 | 99.5 | 99.7 |
Overall performance of the 5 layers WS-CNN in the entire 6 hours | 97.70±1.99 |