# HPS-Det: Dynamic Sample Assignment with Hyper-Parameter Search for Object Detection

Source: [HPS-Det: Dynamic Sample Assignment with Hyper-Parameter Search for Object Detection](<https://www.sota2.com/research/paper/68fa6d151e209131cea37896>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:25:36.384Z

Authors: Ji Liu, Dong Li, Zekun Li, Han Liu, Wenjing Ke, Lu Tian, Yi Shan

Published: 2022-07-23T15:13:57.000Z

## Abstract

Sample assignment plays a prominent part in modern object detection approaches. However, most existing methods rely on manual design to assign positive / negative samples, which do not explicitly establish the relationships between sample assignment and object detection performance. In this work, we propose a novel dynamic sample assignment scheme based on hyper-parameter search. We first define the number of positive samples assigned to each ground truth as the hyper-parameters and employ a surrogate optimization algorithm to derive the optimal choices. Then, we design a dynamic sample assignment procedure to dynamically select the optimal number of positives at each training iteration. Experiments demonstrate that the resulting HPS-Det brings improved performance over different object detection baselines. Moreover, We analyze the hyper-parameter reusability when transferring between different datasets and between different backbones for object detection, which exhibits the superiority and versatility of our method.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2207.11539>)

[PDF](<https://arxiv.org/pdf/2207.11539.pdf>)