Euclid preparation - XXXIII. Characterization of convolutional neural networks for the identification of galaxy-galaxy strong-lensing events

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Table B.2

Comparison between the metrics of tests on the selection S2 with the models trained on S2 (top) and on S4 (bottom).

S2/S2

	VGG-like network		Inception Network		Residual Network
Class	0	1	0	1	0	1

Precision	0.94	0.97	0.97	0.96	0.90	0.92

Recall	0.98	0.94	0.96	0.97	0.92	0.89

F1-score	0.96	0.96	0.96	0.96	0.91	0.91

Accuracy	0.96		0.96		0.91

AUC	0.58		0.88		0.85

S4/S2

	VGG-like network		Inception Network		Residual Network
Class	0	1	0	1	0	1

Precision	0.96	0.74	0.99	0.77	0.95	0.67

Recall	0.89	0.91	0.90	0.98	0.85	0.89

F1-score	0.93	0.82	0.94	0.86	0.90	0.76

Accuracy	0.89		0.92		0.86

AUC	0.51		0.88		0.75

Notes. Class 0 refers to the nonlenses, while class 1 refers to the lenses.

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