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README.md
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Training complete in 1239m 26s
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Best val Acc: 0.9866
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The model showed high accuracy in predicting common categories such as plastic, paper, and metal, but struggled with classes like shoes and clothes, reflecting the challenges of web-scraped images for such categories.
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## Conclusion
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This ResNet50-based garbage classification model shows promising performance for sorting household waste into multiple categories. It can be used in waste management systems to automate and optimize the recycling process. Future work includes improving data quality by collecting real-world garbage images, fine-tuning the model, and addressing potential biases.
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Training complete in 1239m 26s
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Best val Acc: 0.9866
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Test Loss: 0.0427, Test Acc: 0.9866
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Precision: 0.9866, Recall: 0.9866, F1 Score: 0.9866
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The model showed high accuracy in predicting common categories such as plastic, paper, and metal, but bit struggled with classes like shoes and clothes, reflecting the challenges of web-scraped images for such categories.
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## Conclusion
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This ResNet50-based garbage classification model shows promising performance for sorting household waste into multiple categories. It can be used in waste management systems to automate and optimize the recycling process. Future work includes improving data quality by collecting real-world garbage images, fine-tuning the model, and addressing potential biases.
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