**Sharpness-Aware Minimization**, or **SAM**, is a procedure that improves model generalization by simultaneously minimizing loss value and loss sharpness. SAM functions by seeking parameters that lie in neighborhoods having uniformly low loss value (rather than parameters that only themselves have low loss value).

Estimation statistics is a data analysis framework that uses a combination of effect sizes, confidence intervals, precision planning, and meta-analysis to plan experiments, analyze data and interpret results. It is distinct from null hypothesis significance testing (NHST), which is considered to be less informative. The primary aim of estimation methods is to report an effect size (a point estimate) along with its confidence interval, the latter of which is related to the precision of the estimate. The confidence interval summarizes a range of likely values of the underlying population effect. Proponents of estimation see reporting a P value as an unhelpful distraction from the important business of reporting an effect size with its confidence intervals, and believe that estimation should replace significance testing for data analysis.

Estimation Statistics

Sharpness-Aware Minimization

Sharpness-Aware Minimization for Efficiently Improving Generalization

The Fast-YOLOv4-SmallObj model is a modified version of Fast-[YOLOv4](https://paperswithcode.com/method/yolov4) to improve the detection of small objects. Seven layers were added so that it predicts bounding boxes at 3 different scales instead of 2.

Source	Sharpness-Aware Minimization for Efficiently Improving Generalization
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com