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Chazhoor et al. Intell Robot 2022;2:1-19 https://dx.doi.org/10.20517/ir.2021.15 Page 9

Table 2. The mean and class wise accuracies of the models pretrained on the ImageNet dataset, along with the time taken for
training for 20 epochs. The standard deviation indicates the average deviation in accuracy across the five-folds in the respective
model along with the total number of parameters for each model
AlexNet Resnet-50 ResNeXt MoblineNet_v2 DenseNet SqueezeNet
Mean 80.08 85.54 87.44 87.35 85.58 82.59
accuracy (%)
PETE (%) 84.8 85 85 85 88.8 84.4
PE-HD (%) 85.0 95.4 97.6 94.2 95.6 91.4
PP (%) 67.2 68.6 74 74.8 66.4 66.8
PS (%) 80.2 86.0 83.2 89.6 85.4 82.2
Other (%) 100 100 100 100 100 97.5
Time 11.8 12.05 13.11 12.06 17.33 12.01
(min)
Std. deviation 7.5 4.9 5.4 6.0 5.3 1.7
σ (%)
No. of parameters 57 23 22 2 6 0.7
(in million)
PETE: Polyethylene terephthalate; PP: polypropylene, PS: polystyrene.

Figure 9. Flowchart summarizing the experiment.

models. In Table 2, the standard deviation, σ, is displayed, which is a measure of how far values deviate from
the mean. The standard deviation is given by the following unbiased estimation: