Abstract: Trash classification is an effective measure to protect the environment and improve resource utilization.In recent years,deep learning has been successful in various fields with its powerful modeling capabilities,which makes the use of deep learning for waste classification an emerging direction.Most of the trash classification datasets have the problem of an uneven number of category images (long-tail distribution).This paper proposes a new framework based on causal inference for TrashNet which is a long-tail dataset in trash classification.The framework mitigates the long-tail problem of the TrashNet dataset by finding the direct causal effects caused by the input samples through a causal inference approach.The model is trained with a migration learning approach,which reduces the number of training parameters,and is de-confounded using causal intervention and counterfactual inference.The proposed method is validated with class activation map(CAM),and the experimental results show that the proposed model has better feature extraction capability.The model has better recognition effect for difficult classes in TrashNet dataset.And it achieves a better accuracy of 94.23% on the TrashNet dataset.

Key words: Trash classification, Deep learning, Computer vision, Causal inference, Counterfactual reasoning