# Bag of Freebies for Training Object Detection Neural Networks

Source: [Bag of Freebies for Training Object Detection Neural Networks](<https://www.sota2.com/research/paper/68fa659e1e209131cea0b3c1>)

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

Data updated: 2025-10-25T21:30:43.473Z

Authors: Zhi Zhang, Tong He, Hang Zhang, Zhongyue Zhang, Junyuan Xie, Mu Li

Published: 2019-02-11T19:20:09.000Z

## Abstract

Training heuristics greatly improve various image classification model accuracies~\\cite{he2018bag}. Object detection models, however, have more complex neural network structures and optimization targets. The training strategies and pipelines dramatically vary among different models. In this works, we explore training tweaks that apply to various models including Faster R-CNN and YOLOv3. These tweaks do not change the model architectures, therefore, the inference costs remain the same. Our empirical results demonstrate that, however, these freebies can improve up to 5% absolute precision compared to state-of-the-art baselines.

## 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.

- [Object Detection on COCO 2017 (val)](<https://www.sota2.com/research/sota/object-detection-on-coco-2017-val.md>): Object Detection; COCO 2017 (val); AP: 41.1.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1902.04103>)

[PDF](<https://arxiv.org/pdf/1902.04103.pdf>)