Visual Computing for Industry, Biomedicine, and Art

Table 3 Accuracy for each type of waste prediction

From: Focus-RCNet: a lightweight recyclable waste classification algorithm based on focus and knowledge distillation

Classes	Precision	Recall	F1-score
Cardboard	0.99	0.96	0.97
Glass	0.91	0.90	0.90
Metal	0.89	0.93	0.91
Paper	0.94	0.97	0.95
Plastic	0.91	0.90	0.90
Trash	0.80	0.74	0.77
Macro average	0.91	0.90	0.90

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