Papers › Bag of Freebies for Training Object Detection Neural Networks

Bag of Freebies for Training Object Detection Neural Networks

11 Feb 2019arXiv:1902.04103archive 2025-07-28

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

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.

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Tasks

General ClassificationImage ClassificationObject Detectionimage-classificationobject-detection

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Methods

1x1 ConvolutionAverage PoolingBatch NormalizationConvolutionGlobal Average PoolingLogistic RegressionResidual ConnectionSoftmaxYOLOv3k-Means Clustering

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