Garbage Classification Using Inception V3 as Image Embedding and Extreme Gradient Boosting

Employing the image embedding method with the Inception V3 model, this study explored the possibility of utilizing visual features for garbage classification. Several algorithms, including Extreme Gradient Boosting (XGBoost), Naive Bayes, AdaBoost, and Random Forest, were evaluated for their effectiveness in classifying garbage based on extracted visual characteristics. Our investigation commenced with the collection of low-quality image data before conducting feature extraction with the Inception V3 model. For assessment, a dataset that contains the above characteristics is utilized as input. This is then fed into various classification algorithms, and assessment is done based on evaluation measures such as F1 Score and Area under the ROC Curve (AUC). Given the class imbalance in the garbage dataset, the focus is mainly on the AUC measure, as it is more effective in handling such imbalances. Garbage classification can be efficiently carried out by combining the Inception V3 and XGboost algorithms, as per the research. A valuable insight into effective waste management through the use of machine learning is obtained herein. Such a technique could pave the way for ecologically conscious waste disposal measures.