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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:
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