A hybrid of logit and attention distillation transfers Swin Transformer knowledge to a MobileNetV3 student, reaching around 92 to 95 percent accuracy on tomato disease classification at a fraction of the computational cost.
A comparative study of fine-tuning deep learning models for plant disease identification,
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Hybrid Knowledge Transfer through Attention and Logit Distillation for On-Device Vision Systems in Agricultural IoT
A hybrid of logit and attention distillation transfers Swin Transformer knowledge to a MobileNetV3 student, reaching around 92 to 95 percent accuracy on tomato disease classification at a fraction of the computational cost.