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YOLOv4/v7/v9: detector integration and auxiliary training pathways

Contribution grade

Object-detection training systems · Human-designed research method or resource

Reference score145,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Studied component/augmentation combinations for efficient detection, then developed aggregation-aware scaling and reparameterization, lead-guided auxiliary labels, and programmable-gradient auxiliary branches with generalized layer aggregation.

Scientific scope

Studied component/augmentation combinations for efficient detection, then developed aggregation-aware scaling and reparameterization, lead-guided auxiliary labels, and programmable-gradient auxiliary branches with generalized layer aggregation. Papers have different author sets and are not three versions of one single-authored manuscript. Training-only additions can raise training cost even when removed at inference. Original information-bottleneck motivation is not an independently established theorem that all task information is preserved. Inherited components retain credit.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Papers have different author sets and are not three versions of one single-authored manuscript. Training-only additions can raise training cost even when removed at inference. Original information-bottleneck motivation is not an independently established theorem that all task information is preserved. Inherited components retain credit.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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