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focal loss运用

时间: 2023-08-08 08:11:45 浏览: 212

Focal Loss for Dense Object Detection

Abstract The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object lo- cations. In contrast, one-stage detectors that are applied over a regular, dense sampling of possible object locations have the potential to be faster and simpler, but have trailed the accuracy of two-stage detectors thus far. In this paper, we investigate why this is the case. We discover that the ex- treme foreground-background class imbalance encountered during training of dense detectors is the central cause. We propose to address this class imbalance by reshaping the standard cross entropy loss such that it down-weights the loss assigned to well-classified examples. Our novel Focal Loss focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelm- ing the detector during training. To evaluate the effective- ness of our loss, we design and train a simple dense detector we call RetinaNet. Our results show that when trained with the focal loss, RetinaNet is able to match the speed of pre- vious one-stage detectors while surpassing the accuracy of all existing state-of-the-art two-stage detectors.

Focal Loss是一种用于密集目标检测的损失函数，旨在解决类别不均衡问题。该方法由He Kaiming和RBG在ICCV2017上发表的论文《Focal Loss for Dense Object Detection》中提出[1]。Focal Loss通过减少易分类样本的权重，使得模型在训练时更专注于难分类的样本，从而提高模型对于难以区分的样本的识别能力[3]。 Focal Loss的原理是在标准交叉熵损失的基础上进行修改得到的。传统的交叉熵损失在处理类别不均衡问题时，容易受到易分类样本的干扰，导致模型对于难分类的样本的识别能力较弱。而Focal Loss通过引入一个可调参数γ，降低易分类样本的权重，使得模型更加关注难分类的样本[3]。 Focal Loss的有效性通过作者设计的一个密集目标检测器RetinaNet进行了实验证明。RetinaNet在训练时采用了Focal Loss作为损失函数，结果表明，RetinaNet不仅具有one-stage detector的速度，还能达到two-stage detector的准确率[3]。总结来说，Focal Loss是一种用于解决类别不均衡问题的损失函数，通过减少易分类样本的权重，使得模型更加关注难分类的样本，提高了模型的识别能力。它在密集目标检测任务中取得了较好的效果[1][3]。

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