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ARXIV:2603.17684 · COMPUTER VISION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.17684COMPUTER VISIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
A novel sampling strategy for YOLO detectors that optimizes training efficiency by selectively resampling images based on learning sufficiency.
Opportunity summary
Pain A novel sampling strategy for YOLO detectors that optimizes training efficiency by selectively resampling images based on learning sufficiency.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A novel sampling strategy for YOLO detectors that optimizes training efficiency by selectively resampling images based on learning sufficiency. This stands in clear contrast to the efficiency suggested by the ``You Only Look Once''…
YOLO detectors are known for their fast inference speed, yet training them remains unexpectedly time-consuming due to their exhaustive pipeline that processes every training image in every epoch, even when many images have already…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. On widely used natural image detection benchmarks (MS COCO 2017 and PASCAL VOC 2007) and remote sensing detection datasets (DOTA-v1.0 and DIOR-R), AFSS achieves…
Computer Vision moved forward this cycle; last verified April 2026. Public score 7.0/10.
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A novel sampling strategy for YOLO detectors that optimizes training efficiency by selectively resampling images based on learning sufficiency.
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10.48550/arXiv.2603.17684A novel sampling strategy for YOLO detectors that optimizes training efficiency by selectively resampling images based on learning sufficiency.
Abstract
YOLO detectors are known for their fast inference speed, yet training them remains unexpectedly time-consuming due to their exhaustive pipeline that processes every training image in every epoch, even when many images have already been sufficiently learned. This stands in clear contrast to the efficiency suggested by the ``You Only Look Once'' philosophy. This naturally raises an important question: \textit{Does YOLO really need to see every training image in every epoch?} To explore this, we propose an Anti-Forgetting Sampling Strategy (AFSS) that dynamically determines which images should be used and which can be skipped during each epoch, allowing the detector to learn more effectively and efficiently. Specifically, AFSS measures the learning sufficiency of each training image as the minimum of its detection recall and precision, and dynamically categorizes training images into easy, medium, or hard levels accordingly. Easy training images are sparsely resampled during training in a continuous review manner, with priority given to those that have not been used for a long time to reduce redundancy and prevent forgetting. Moderate training images are partially selected, prioritizing recently unused ones and randomly choosing the rest from unselected images to ensure coverage and prevent forgetting. Hard training images are fully sampled in every epoch to ensure sufficient learning. The learning sufficiency of each training image is periodically updated, enabling detectors to adaptively shift its focus toward the informative training images over time while progressively discarding redundant ones. On widely used natural image detection benchmarks (MS COCO 2017 and PASCAL VOC 2007) and remote sensing detection datasets (DOTA-v1.0 and DIOR-R), AFSS achieves more than $1.43\times$ training speedup for YOLO-series detectors while also improving accuracy.
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PROBLEM
A novel sampling strategy for YOLO detectors that optimizes training efficiency by selectively resampling images based on learning sufficiency. This stands in clear contrast to the efficiency suggested by the ``You Only Look Once'' philosophy.
METHOD
YOLO detectors are known for their fast inference speed, yet training them remains unexpectedly time-consuming due to their exhaustive pipeline that processes every training image in every epoch, even when many images have already been sufficiently learned. This stands in clear...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. On widely used natural image detection benchmarks (MS COCO 2017 and PASCAL VOC 2007) and remote sensing detection datasets (DOTA-v1.0 and DIOR-R), AFSS achieves more than $1.43\times$ training speedup for...
WHY NOW
Computer Vision moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A novel sampling strategy for YOLO detectors that optimizes training efficiency by selectively resampling images based on learning sufficiency. This stands in clear contrast to the efficiency suggested by the ``You Only Look Once'' philosophy.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
YOLO detectors are known for their fast inference speed, yet training them remains unexpectedly time-consuming due to their exhaustive pipeline that processes every training image in every epoch, even when many images have already been sufficiently learned. This stands in clear contrast to the efficiency suggested by the ``You Only Look Once'' philosophy.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. On widely used natural image detection benchmarks (MS COCO 2017 and PASCAL VOC 2007) and remote sensing detection datasets (DOTA-v1.0 and DIOR-R), AFSS achieves more than $1.43\times$ training speedup for YOLO-series detectors while also improving accuracy.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Computer Vision moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A novel sampling strategy for YOLO detectors that optimizes training efficiency by selectively resampling images based on learning sufficiency.
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