AgriKD distills multi-level knowledge from Vision Transformers to lightweight CNNs, achieving comparable leaf disease classification accuracy with 172x fewer parameters and 18-22x faster inference.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pages =
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AgriKD: Cross-Architecture Knowledge Distillation for Efficient Leaf Disease Classification
AgriKD distills multi-level knowledge from Vision Transformers to lightweight CNNs, achieving comparable leaf disease classification accuracy with 172x fewer parameters and 18-22x faster inference.