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arXiv Computer Vision By Khush Kataruka, Mohammad Muzammila, Zahid Akhtar, Raghavendra Ramachandra, Praveen Kumar Chandaliya

Enhancing Cross-Grade Generalization in Banana Variety Classification via Hybrid CNN-Transformers and Knowledge Self-Distillation

Read the original on arXiv Computer Vision →

The paper introduces a dual‑stream Swin ResNet hybrid architecture that combines CNN and Transformer streams for cross‑grade banana variety classification. It employs KL‑divergence‑based self‑distillation to align the probabilistic outputs of the two streams, achieving a mean accuracy of 81.25% on a dataset of 31,678 images across three varieties and three quality grades. Experiments show the hybrid model outperforms both CNN and Transformer baselines, improving feature transferability, attention localization, and optimization stability.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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