Research Article
Deep Learning with Taxonomic Loss for Plant Identification
Table 3
Accuracies of eight state-of-the-art neural networks trained by cross-entropy and taxonomic loss on PlantCLEF 2017 dataset.
| Neural network | Loss function | Accuracy (%) | Family | Genus | Species |
| GoogLeNet [13] | CL | 68.73 | 64.22 | 57.86 | TAX | 73.29 | 68.64 | 61.04 |
| ResNet-50 [23] | CL | 76.32 | 72.49 | 66.68 | TAX | 78.95 | 74.77 | 68.04 |
| Inception-v3 [14] | CL | 77.32 | 73.12 | 67.05 | TAX | 79.87 | 76.02 | 68.76 |
| Inception-ResNet-v2 [11] | CL | 79.98 | 75.43 | 68.97 | TAX | 82.31 | 78.65 | 71.21 |
| MobileNet-v2 [24] | CL | 71.76 | 67.73 | 61.78 | TAX | 73.88 | 69.40 | 62.01 |
| ShuffleNet-v2 [25] | CL | 61.94 | 57.12 | 49.96 | TAX | 66.13 | 60.73 | 53.12 |
| DenSeNet-169 [26] | CL | 76.34 | 72.53 | 66.60 | TAX | 77.87 | 73.92 | 67.10 |
| SENet-154 [27] | CL | 81.84 | 78.63 | 72.53 | TAX | 84.07 | 79.97 | 73.61 |
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