  This paper investigates video game identification through single screenshots,
utilizing five convolutional neural network (CNN) architectures (MobileNet,
DenseNet, EfficientNetB0, EfficientNetB2, and EfficientNetB3) across 22 home
console systems, spanning from Atari 2600 to PlayStation 5, totalling 8,796
games and 170,881 screenshots. Confirming the hypothesis, CNNs autonomously
extract image features, enabling the identification of game titles from
screenshots without additional features. Using ImageNet pre-trained weights as
initial weights, EfficientNetB3 achieves the highest average accuracy (74.51%),
while DenseNet169 excels in 14 of the 22 systems. Employing alternative initial
weights trained in an arcade screenshots dataset boosts accuracy for
EfficientNetB2 and EfficientNetB3, with the latter reaching a peak accuracy of
76.36% and demonstrating reduced convergence epochs from 23.7 to 20.5 on
average. Overall, the combination of optimal architecture and weights attains
77.67% accuracy, primarily led by EfficientNetB3 in 19 systems. These findings
underscore the efficacy of CNNs in video game identification through
screenshots.
