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YOLOX: decoupled anchor-free detection with simplified assignment

Contribution grade

Object-detection methods · Human-designed research method or resource

Reference score120,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Combined a decoupled classification/regression head, anchor-free prediction, strong augmentation and SimOTA dynamic positive assignment in a practical detector.

Scientific scope

Combined a decoupled classification/regression head, anchor-free prediction, strong augmentation and SimOTA dynamic positive assignment in a practical detector. SimOTA approximates a more expensive transport-inspired assignment rather than solving the exact global transport problem. YOLOX's optional one-to-one/NMS-free variant is not its default model. Table latencies excluding post-processing are not full application latency.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. SimOTA approximates a more expensive transport-inspired assignment rather than solving the exact global transport problem. YOLOX's optional one-to-one/NMS-free variant is not its default model. Table latencies excluding post-processing are not full application latency.

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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