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.
Abstract (translated)
神经架构搜索（NAS）在目标检测领域由于评估成本高昂而面临严重瓶颈，因为完全训练每个候选YOLO架构需要大量的GPU时间。同时，现有的NAS基准测试主要针对图像分类问题，导致目标检测社区缺乏一个相应的NAS性能评估基准。为解决这一差距，我们引入了 YOLO-NAS-Bench，这是第一个专为目标检测（特别是类似YOLO的模型）设计的替代性基准。 **YOLO-NAS-Bench** 定义了一个搜索空间，包括通道宽度、块深度和操作类型等参数，这些覆盖了从YOLOv8到YOLO12的核心模块。我们通过随机采样、分层抽样以及拉丁超立方体（Latin Hypercube）策略生成了一千个架构，并在COCO-mini数据集上进行训练，从而构建了一个LightGBM替代预测器。 为了提高该预测器的精度，特别是在高性能区间内——这对于NAS来说是最相关的部分，我们提出了一种自我进化机制。通过使用现有的预测器来发现并评估具有信息量的架构，在每个迭代中逐步将预测器的训练分布与高性能前沿对齐，这种机制使得预测更加准确和可靠。 采用该方法后，我们将架构池增加到了1500个，并且整个集合的R2系数从0.770提高到0.815，而稀疏肯德尔τ（Sparse Kendall Tau）也从0.694提升至0.752。这表明最终预测器不仅具有强大的预测准确性，而且在排名一致性方面表现良好。 最后，我们利用该预测器作为进化搜索的适应度函数，以发现可以在COCO-mini上与官方YOLOv8-YOLO12基准模型相媲美的架构，并且其延迟相当。这证实了我们的替代预测器能够区分出高性能的目标检测架构的能力。 简而言之，通过这些创新和改进，我们为神经网络架构搜索在目标检测领域的应用提供了新的工具和策略。
URL
https://arxiv.org/abs/2603.09405
