Generalization abilities of foundation models in waste classification

Industrial waste classification systems based on computer vision require strong generalization abilities across location and time period in order to be deployed. This study investigates the potential of foundation models, known for their adaptability to a wide range of tasks and promising generalization capabilities, to serve as the basis for such systems. To evaluate the generalization performance of foundation models we use five waste classification datasets spanning various domains, train the models on one dataset and test them on all others. Additionally, we explore various training procedures to optimize foundation model adaptation for this specific domain. Our findings reveal that foundation models exhibit superior generalization abilities compared to standard models and that good generalization performance is correlated with the model size and the size of the model pretraining dataset. Furthermore, we demonstrate that elaborate classifier heads are not necessary for extracting discriminative features from foundation models. Both standard fine-tuning and Parameter-Efficient Fine-tuning (PEFT) improve generalization performance, with PEFT being particularly effective for larger models. Simple data augmentation techniques were found to be ineffective. Overall, application of foundation models to industrial waste classification holds very promising results.