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Table 2 Evaluation and validation of spike detection using the GSYC image model applied to the GSYC image data set

From: Detection and analysis of wheat spikes using Convolutional Neural Networks

Image no GT-count Detected TP FP FN Precision mAP Accuracy F1-score
Test_001.jpg 73 71 70 1 3 0.98 0.7289 96% 0.97
Test_012.jpg 75 68 68 0 7 1.00 0.6002 91% 0.95
Test_025.jpg 87 85 84 1 3 0.98 0.7324 96% 0.97
Test_032.jpg 80 76 76 0 4 1.00 0.7286 95% 0.97
Test_118.jpg 76 73 70 3 6 0.95 0.6126 92% 0.93
Test_141.jpg 66 61 58 3 8 0.95 0.5835 88% 0.91
Test_185.jpg 69 68 65 3 4 0.95 0.7105 94% 0.94
Test_199.jpg 72 69 68 1 4 0.98 0.7184 94% 0.96
Test_220.jpg 80 79 76 3 4 0.96 0.7229 95% 0.95
Test_242.jpg 70 64 63 1 7 0.90 0.5926 90% 0.94
Test_254.jpg 83 77 76 1 7 0.98 0.6085 91% 0.95
Test_320.jpg 80 77 74 3 6 0.96 0.6213 92% 0.94
Test_383.jpg 87 84 78 6 9 0.92 0.5947 90% 0.91
Test_399.jpg 80 78 77 1 3 0.98 0.7301 96% 0.97
Test_417.jpg 96 93 89 4 7 0.95 0.6573 93% 0.94
Test_421.jpg 71 73 70 3 1 0.95 0.7552 98% 0.97
Test_422.jpg 82 79 78 1 4 0.98 0.7211 95% 0.96
Test_432.jpg 85 81 79 2 6 0.97 0.6502 93% 0.95
Test_437.jpg 70 64 62 2 8 0.96 0.5924 88% 0.92
Test_480.jpg 88 84 82 2 6 0.97 0.6441 93% 0.95
Total 1570 1504 1463 41 107
Average 0.97 0.6653 93.4% 0.95
Standard dev. 7.82 8.17 7.86 1.46 1.11 0.02 0.06 0.03 0.02