yolov5目标检测模型的知识蒸馏(基于响应的蒸馏)

Overview

代码地址:

https://github.com/Sharpiless/yolov5-knowledge-distillation

教师模型:

python train.py --weights weights/yolov5m.pt \
        --cfg models/yolov5m.yaml --data data/voc.yaml --epochs 50 \
        --batch-size 8 --device 0 --hyp data/hyp.scratch.yaml 

蒸馏训练:

python train.py --weights weights/yolov5s.pt \
        --cfg models/yolov5s.yaml --data data/voc.yaml --epochs 50 \
        --batch-size 8 --device 0 --hyp data/hyp.scratch.yaml \
        --t_weights yolov5m.pt --distill

训练参数:

--weights:预训练模型

--t_weights:教师模型权重

--distill:使用知识蒸馏进行训练

--dist_loss:l2或者kl

--temperature:使用知识蒸馏时的温度

使用《Object detection at 200 Frames Per Second》中的损失

这篇文章分别对这几个损失函数做出改进,具体思路为只有当teacher network的objectness value高时,才学习bounding box坐标和class probabilities。

实验结果:

这里假设VOC2012中新增加的数据为无标签数据(2k张)。

教师模型 训练方法 蒸馏损失 P R mAP50
正常训练 不使用 0.7756 0.7115 0.7609
Yolov5l output based l2 0.7585 0.7198 0.7644
Yolov5l output based KL 0.7417 0.7207 0.7536
Yolov5m output based l2 0.7682 0.7436 0.7976
Yolov5m output based KL 0.7731 0.7313 0.7931

训练结果

参数和细节正在完善,支持KL散度、L2 logits损失和Sigmoid蒸馏损失等

1. 正常训练:

正常训练

2. L2蒸馏损失:

L2蒸馏损失

我的公众号:

在这里插入图片描述

关于作者

B站:https://space.bilibili.com/470550823

CSDN:https://blog.csdn.net/weixin_44936889

AI Studio:https://aistudio.baidu.com/aistudio/personalcenter/thirdview/67156

Github:https://github.com/Sharpiless

Owner
BIT可达鸭
yolov5目标检测模型的知识蒸馏(基于响应的蒸馏)

代码地址: https://github.com/Sharpiless/yolov5-knowledge-distillation 教师模型: python train.py --weights weights/yolov5m.pt \ --cfg models/yolov5m.ya

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