|
Dataset\(\rightarrow \)
|
Y1/Y1
|
Y2/Y2
|
Whole dataset
|
|---|
|
Network \(\downarrow \)
|
IA
|
FIA
|
LA
|
IA
|
FIA
|
LA
|
IA
|
FIA
|
LA
|
|---|
|
ResNet18
|
94.55
|
94.87
|
98.71
|
84.00
|
82.00
|
90.50
|
83.19
|
78.41
|
89.56
|
|
ResNet34
|
94.09
|
94.87
|
97.43
|
84.37
|
83.50
|
90.00
|
84.85
|
81.65
|
91.36
|
|
ResNet50
|
94.43
|
96.15
|
98.71
|
82.43
|
77.50
|
88.00
|
82.60
|
79.85
|
89.92
|
|
ResNet101
|
93.04
|
91.02
|
98.71
|
82.75
|
83.00
|
88.50
|
82.49
|
83.09
|
88.12
|
|
ResNet152
|
93.27
|
91.02
|
97.43
|
80.81
|
74.50
|
86.00
|
82.60
|
79.85
|
87.76
|
- For each network structure tested, % accuracy scores are reported as Image Accuracy (IA), First Image Accuracy (FIA) and Leaf Accuracy (LA). The highest accuracy in each column and corresponding feature extractor is highlighted in bold. All models here employ the Adam optimizer with learning rate (lr) \(=1e^{-4}\)
