Methods › General › Loss Functions › Dynamic SmoothL1 Loss
Dynamic SmoothL1 Loss
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Dynamic SmoothL1 Loss (DSL) is a loss function in object detection where we change the shape of loss function to gradually focus on high quality samples:
DSL(x, β_(now)) = 0.5|x|²/β_(now), if |x| < β_(now),
DSL(x, β_(now)) = |x| - 0.5β_(now), otherwise
DSL will change the value of β_(now) according to the statistics of regression errors which can reflect the localization accuracy. It was introduced as part of the Dynamic R-CNN model.
Papers archive 2025-07-28
3 shown of 3, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Enhancing Tree Type Detection in Forest Fire Risk Assessment: Multi-Stage Approach and Color Encoding with Forest Fire Risk Evaluation Framework for UAV Imagery 27 Jul 2024 · 0 repositories · arXiv:2407.19184
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Fracture Detection in Wrist X-ray Images Using Deep Learning-Based Object Detection Models 14 Nov 2021 · 0 repositories · arXiv:2111.07355
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Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training 13 Apr 2020 · 3 repositories · arXiv:2004.06002Syntology ran 2 of 18 samples · 16 unverified
Tasks archive 2025-07-28
10 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
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Categories archive 2025-07-28
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