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Generalization abilities of foundation models in waste classification.

Aloïs Babé1, Rémi Cuingnet2, Mihaela Scuturici3

  • 1Université Lumière Lyon 2, CNRS, Ecole Centrale de Lyon, INSA Lyon, Université Claude Bernard, Lyon 1, LIRIS, UMR 5205, Bron 69676, France; Veolia Scientific & Technical Expertise Department, Maisons-Laffitte 78600, France.

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Summary
This summary is machine-generated.

Foundation models show superior generalization for industrial waste classification compared to standard models. Larger models and pretraining datasets enhance performance, with Parameter-Efficient Fine-tuning (PEFT) proving effective.

Keywords:
Computer visionFoundation modelGeneralizationWaste classification

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Environmental Science

Background:

  • Industrial waste classification systems need robust generalization across diverse locations and time periods for practical deployment.
  • Foundation models offer adaptability and strong generalization potential for various AI tasks.

Purpose of the Study:

  • To investigate the efficacy of foundation models for industrial waste classification.
  • To evaluate their generalization capabilities across different datasets and training strategies.

Main Methods:

  • Utilized five diverse waste classification datasets for training and cross-testing foundation models.
  • Explored various adaptation techniques, including standard fine-tuning and Parameter-Efficient Fine-tuning (PEFT).
  • Assessed the impact of model size and pretraining dataset size on generalization performance.

Main Results:

  • Foundation models significantly outperformed standard models in generalization for waste classification.
  • Model size and pretraining dataset scale positively correlated with generalization performance.
  • Parameter-Efficient Fine-tuning (PEFT) demonstrated effectiveness, especially for larger foundation models.
  • Elaborate classifier heads and simple data augmentation were found to be unnecessary or ineffective.

Conclusions:

  • Foundation models present a highly promising approach for developing generalized industrial waste classification systems.
  • PEFT offers an efficient adaptation strategy for large foundation models in this domain.