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October 3, 2025Open Access

Fine-Tuning Without Forgetting: Adaptation of YOLOv8 Preserves COCO Performance

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Deeper fine-tuning of YOLOv8 leads to a 10% increase in absolute mAP50 for fruit detection.
Fine-tuned model achieved less than 0.1% performance loss on the COCO benchmark across freeze levels.
Systematic study explores unfreezing different backbone layers to optimize model adaptation.
Effective adaptation can occur without incurring catastrophic forgetting, making deeper strategies viable.

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Evaluation of fine-tuning depth on YOLOv8 shows significant performance gains, suggesting effective adaptation strategies.

Gandhi et al. (2025) studied this question.

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