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ARXIV:2603.09405 · NEURAL ARCHITECTURE SEARCH · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.09405NEURAL ARCHITECTURE SEARCHSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
YOLO-NAS-Bench is a surrogate benchmark for efficient YOLO architecture search, enhancing predictive accuracy for object detection.
Opportunity summary
Pain YOLO-NAS-Bench is a surrogate benchmark for efficient YOLO architecture search, enhancing predictive accuracy for object detection.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
YOLO-NAS-Bench is a surrogate benchmark for efficient YOLO architecture search, enhancing predictive accuracy for object detection. Meanwhile, existing NAS benchmarks largely target image classification, leaving the detection community without a comparable benchmark for NAS…
Neural Architecture Search (NAS) for object detection is severely bottlenecked by high evaluation cost, as fully training each candidate YOLO architecture on COCO demands days of GPU time. Meanwhile, existing NAS benchmarks largely target…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Using the final predictor as the fitness function for evolutionary search, we discover architectures that surpass all official YOLOv8-YOLO12 baselines at comparable latency on…
Neural Architecture Search moved forward this cycle; last verified April 2026. Public score 7.0/10.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score7.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
YOLO-NAS-Bench is a surrogate benchmark for efficient YOLO architecture search, enhancing predictive accuracy for object detection.
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10.48550/arXiv.2603.09405YOLO-NAS-Bench is a surrogate benchmark for efficient YOLO architecture search, enhancing predictive accuracy for object detection.
Abstract
Neural Architecture Search (NAS) for object detection is severely bottlenecked by high evaluation cost, as fully training each candidate YOLO architecture on COCO demands days of GPU time. Meanwhile, existing NAS benchmarks largely target image classification, leaving the detection community without a comparable benchmark for NAS evaluation. To address this gap, we introduce YOLO-NAS-Bench, the first surrogate benchmark tailored to YOLO-style detectors. YOLO-NAS-Bench defines a search space spanning channel width, block depth, and operator type across both backbone and neck, covering the core modules of YOLOv8 through YOLO12. We sample 1,000 architectures via random, stratified, and Latin Hypercube strategies, train them on COCO-mini, and build a LightGBM surrogate predictor. To sharpen the predictor in the high-performance regime most relevant to NAS, we propose a Self-Evolving Mechanism that progressively aligns the predictor's training distribution with the high-performance frontier, by using the predictor itself to discover and evaluate informative architectures in each iteration. This method grows the pool to 1,500 architectures and raises the ensemble predictor's R2 from 0.770 to 0.815 and Sparse Kendall Tau from 0.694 to 0.752, demonstrating strong predictive accuracy and ranking consistency. Using the final predictor as the fitness function for evolutionary search, we discover architectures that surpass all official YOLOv8-YOLO12 baselines at comparable latency on COCO-mini, confirming the predictor's discriminative power for top-performing detection architectures.
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
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Viability
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Commercial
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Dimensions overall score 7.0
PROBLEM
YOLO-NAS-Bench is a surrogate benchmark for efficient YOLO architecture search, enhancing predictive accuracy for object detection. Meanwhile, existing NAS benchmarks largely target image classification, leaving the detection community without a comparable benchmark for NAS eval...
METHOD
Neural Architecture Search (NAS) for object detection is severely bottlenecked by high evaluation cost, as fully training each candidate YOLO architecture on COCO demands days of GPU time. Meanwhile, existing NAS benchmarks largely target image classification, leaving the detect...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Using the final predictor as the fitness function for evolutionary search, we discover architectures that surpass all official YOLOv8-YOLO12 baselines at comparable latency on COCO-mini, confirming the pr...
WHY NOW
Neural Architecture Search moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
YOLO-NAS-Bench is a surrogate benchmark for efficient YOLO architecture search, enhancing predictive accuracy for object detection. Meanwhile, existing NAS benchmarks largely target image classification, leaving the detection community without a comparable benchmark for NAS evaluation.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Neural Architecture Search (NAS) for object detection is severely bottlenecked by high evaluation cost, as fully training each candidate YOLO architecture on COCO demands days of GPU time. Meanwhile, existing NAS benchmarks largely target image classification, leaving the detection community without a comparable benchmark for NAS evaluation.
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. Using the final predictor as the fitness function for evolutionary search, we discover architectures that surpass all official YOLOv8-YOLO12 baselines at comparable latency on COCO-mini, confirming the predictor's discriminative power for top-performing detection architectures.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Neural Architecture Search 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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Concepts
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YOLO-NAS-Bench is a surrogate benchmark for efficient YOLO architecture search, enhancing predictive accuracy for object detection.
Segment
Neural Architecture Search
Adoption evidence
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Commercial read
7.0/10 public viability
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Write integration checklist from prototype path and target workflow.
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ARTIFACTS
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DEFENSIBILITY
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