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Table 5 Vessel attribute classification performance of generic and attribute-specific representations, calculated for four attributes on 158,850 images of IMO testing set

From: Generic and attribute-specific deep representations for maritime vessels

Classified attribute Employed representation Top 1 accuracy Top 2 accuracy Top 3 accuracy Top 4 accuracy Top 5 accuracy
Draught Generic model + SVM 0.1302 0.3104 0.4432 0.5506 0.6320
Gross tonnage Generic model + SVM 0.4755 0.6393 0.7418 0.8178 0.8678
Length Generic model + SVM 0.4539 0.6345 0.7317 0.8019 0.8510
Summer deadweight Generic model + SVM 0.4304 0.6209 0.7310 0.7998 0.8525
Draught Attribute-specific trained CNN 0.1834 0.4159 0.5761 0.6884 0.7774
Gross tonnage Attribute-specific trained CNN 0.5515 0.7492 0.8556 0.9131 0.9454
Length Attribute-specific trained CNN 0.5289 0.7266 0.8257 0.8896 0.9328
Summer deadweight Attribute-specific trained CNN 0.5155 0.7364 0.8317 0.8938 0.9288