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Accuracy and F1-score of models Our proposed SE-Xception model outperforms every other model with a maximum accuracy of 0.9890. Precision, recall, and F1-score values of 0.9867, 0.9879, and 0.9873 have been achieved, respectively. Hence, the accuracy of our model is remarkable, and it can very well identify and classify the different categories of trash (Fig. 3). SE blocks have been fused together with the Xception architecture which in turn has allowed the model to adjust the importance of various features dynamically and thus improving the overall performance a lot. The findings are very clear showing that it is really a SE-Xception model our group has proposed, which is to be the best-qualified one in the garbage classification, as we have already obtained the highest metrics in all evaluated categories. This excellent result is strong evidence of the fact that employing SE blocks in the Xception framework is a very important step to increase its efficiency in getting and sorting out mixed field images of the garbage dataset. The minute-by-minute performance of Xception and SE blocks is the major reason e.g. for the high success of the models. This is because the combination of Xception and SE blocks has the main contribution to the capturing of derails, the sorting of the images, which are the cell phone charger, and the coke can.

Accuracy and F1-score of models Our proposed SE-Xception model outperforms every other model with a maximum accuracy of 0.9890. Precision, recall, and F1-score values of 0.9867, 0.9879, and 0.9873 have been achieved, respectively. Hence, the accuracy of our model is remarkable, and it can very well identify and classify the different categories of trash (Fig. 3). SE blocks have been fused together with the Xception architecture which in turn has allowed the model to adjust the importance of various features dynamically and thus improving the overall performance a lot. The findings are very clear showing that it is really a SE-Xception model our group has proposed, which is to be the best-qualified one in the garbage classification, as we have already obtained the highest metrics in all evaluated categories. This excellent result is strong evidence of the fact that employing SE blocks in the Xception framework is a very important step to increase its efficiency in getting and sorting out mixed field images of the garbage dataset. The minute-by-minute performance of Xception and SE blocks is the major reason e.g. for the high success of the models. This is because the combination of Xception and SE blocks has the main contribution to the capturing of derails, the sorting of the images, which are the cell phone charger, and the coke can.

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Fig. 1 Example images from Garbage Dataset
Fig. 2 Xception architecture [34]
Fig. 3 Accuracy and F1-score of models Our proposed SE-Xception model...
Fig. 4 Confusion matrix of proposed model The confusion matrix of Fig....
Images counts of garbage classes by split
A Novel SE-Xception Model for Robust Classification of Garbage
Conference Paper
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  • Sep 2024
In this study, we propose a novel deep learning model, SE-Xception, which integrates Squeeze-and-Excitation (SE) blocks into the Xception architecture for solid waste classification. This model is designed to address the growing global challenge of waste accumulation by enhancing the classification accuracy of waste materials. Utilizing a publicly...
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... well in the garbage classification task but is surpassed by more advanced models. While DeiT-Base performs commendably, there is room for improvement, particularly with more sophisticated feature recalibration techniques. Hence, the accuracy of our model is remarkable, and it can very well identify and classify the different categories of trash (Fig. 3). SE blocks have been fused together with the Xception architecture which in turn has allowed the model to adjust the importance of various features dynamically and thus improving the overall performance a lot. The findings are very clear showing that it is really a SE-Xception model our group has proposed, which is to be the ...