Trained and validated on Sheep No. | No. of patterns in the train 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 | 170 | 270 | 3 | 0 | 100 | 98.3 | 100 | 99.3 |
1,3,4,5,6,7 | 4751 | 2 | 259 | 109 | 149 | 1 | 0 | 100 | 99.1 | 100 | 99.6 |
1,2,4,5,6,7 | 4731 | 3 | 279 | 83 | 195 | 0 | 1 | 98.8 | 100 | 99.5 | 99.6 |
1,2,3,5,6,7 | 3372 | 4 | 1638 | 822 | 806 | 2 | 8 | 99.0 | 99.8 | 99.0 | 99.4 |
1,2,3,4,6,7 | 4088 | 5 | 922 | 455 | 455 | 12 | 0 | 100 | 97.4 | 100 | 98.7 |
1,2,3,4,5,7 | 4466 | 6 | 544 | 230 | 313 | 1 | 0 | 100 | 99.6 | 100 | 99.8 |
1,2,3,4,5,6 | 4085 | 7 | 925 | 208 | 707 | 1 | 9 | 95.9 | 99.5 | 98.7 | 98.9 |
Overall performance of the 9 layers WF-CNN in the entire 6 hours | 99.33±0.36 |