Model Soups, extending Stochastic Weights Averaging (SWA), combine models
fine-tuned with different hyperparameters. Yet, their adoption is hindered by
computational challenges due to subset selection issues. In this paper, we
propose to speed up model soups by approximating soups performance using
averaged ensemble logits performances. Theoretical insights validate the
congruence between ensemble logits and weight averaging soups across any mixing
ratios. Our Resource ADjusted soups craftINg (RADIN) procedure stands out by
allowing flexible evaluation budgets, enabling users to adjust his budget of
exploration adapted to his resources while increasing performance at lower
budget compared to previous greedy approach (up to 4% on ImageNet).

通过使用平均集成的日志绩效近似模型汤的性能，我们提出了一种加速模型汤的方法。理论上证实了集成日志与权重平均模型汤在任何混合比例下的一致性。我们的资源调整模型汤方法在允许灵活的评估预算的同时，相比以前的贪婪方法能够在更低的预算下提高性能（ImageNet 上高达 4%）。

RADIN：在预算范围内煲汤

RADIN: Souping on a Budget

Do you want to improve 1.0 AP for your object detector without any inference
cost and any change to your detector? Let us tell you such a recipe. It is
surprisingly simple: train your detector for an extra 12 epochs using cyclical
learning rates and then average these 12 checkpoints as your final detection
model}. This potent recipe is inspired by Stochastic Weights Averaging (SWA),
which is proposed in arXiv:1803.05407 for improving generalization in deep
neural networks. We found it also very effective in object detection. In this
technique report, we systematically investigate the effects of applying SWA to
object detection as well as instance segmentation. Through extensive
experiments, we discover the aforementioned workable policy of performing SWA
in object detection, and we consistently achieve $\sim$1.0 AP improvement over
various popular detectors on the challenging COCO benchmark, including Mask
RCNN, Faster RCNN, RetinaNet, FCOS, YOLOv3 and VFNet. We hope this work will
make more researchers in object detection know this technique and help them
train better object detectors. Code is available at:
this https URL .

使用随机权重平均的技术和循环学习率，我们在目标检测和实例分割中系统地研究了 SWA 的应用效果，并取得了对 COCO 基准的稳定提升。