From 7be78486d5d836985413fe41550631f41bbae888 Mon Sep 17 00:00:00 2001 From: LDOUBLEV Date: Fri, 6 May 2022 15:54:09 +0800 Subject: [PATCH] update --- doc/doc_ch/PP-OCRv3_introduction.md | 40 +++++++++++++++------------- doc/ppocr_v3/LKPAN.png | Bin 0 -> 94683 bytes doc/ppocr_v3/teacher_dml.png | Bin 386915 -> 233342 bytes 3 files changed, 22 insertions(+), 18 deletions(-) create mode 100644 doc/ppocr_v3/LKPAN.png diff --git a/doc/doc_ch/PP-OCRv3_introduction.md b/doc/doc_ch/PP-OCRv3_introduction.md index 7ddf4a08..589970c6 100644 --- a/doc/doc_ch/PP-OCRv3_introduction.md +++ b/doc/doc_ch/PP-OCRv3_introduction.md @@ -23,31 +23,30 @@ PP-OCRv3系统pipeline如下: ## 2. 检测优化 -PP-OCRv3采用PP-OCRv2的[CML](https://arxiv.org/pdf/2109.03144.pdf)蒸馏策略,CML蒸馏包含一个蒸馏教师模型和两个蒸馏学生模型,在训练过程中,教师模型不参与训练,学生模型受到来自标签和教师模型的监督,同时两个学生模型互相学习。相比较PP-OCRv2,PP-OCRv3在蒸馏教师模型、蒸馏学生模型的精度提升两个方面进一步优化。 +PP-OCRv3采用PP-OCRv2的[CML](https://arxiv.org/pdf/2109.03144.pdf)蒸馏策略,CML蒸馏包含一个教师模型和两个学生模型,在训练过程中,教师模型不参与训练,学生模型受到来自标签和教师模型的监督,同时两个学生模型互相学习。相比较PP-OCRv2,PP-OCRv3在教师模型、学生模型的精度提升两个方面进一步优化。 PP-OCRv3 CML蒸馏训练框架图如下:
-- 在蒸馏的教师模型精度提升方面,提出了LK-PAN结构替换PP-OCRv2的FPN结构提升模型的召回,并且使用ResNet50作为Backbone。另外,对教师模型使用[DML](https://arxiv.org/abs/1706.00384)蒸馏策略进一步提升教师模型的精度。最终教师模型指标相比ppocr_server从83.2%提升到了86.0%。 +- 在教师模型精度提升方面,提出了LK-PAN结构替换PP-OCRv2的FPN结构提升模型的召回,并且使用ResNet50作为Backbone。 +另外,使用Deep Mutual Learning([DML](https://arxiv.org/abs/1706.00384))蒸馏策略进一步提升教师模型的精度,DML是一种自蒸馏策略,区别于传统的教师模型监督学生模型的蒸馏方法。DML是多个学生模型以协作的方式互相监督。加上DML自蒸馏后,教师模型Hmean进一步提升到86.0%。 -教师模型自蒸馏训练的pipeline如下: +LK-PAN(Large Kernel PAN)是一个具有更大感受野的轻量级[PAN](https://arxiv.org/pdf/1803.01534.pdf)结构。在LK-PAN的path augmentation中,使用卷积核为`9*9`的卷积;更大的卷积核意味着更大的感受野,更容易检测大字体的文字以及极端长宽比的文字。LK-PAN将ppocr_server检测模型的精度hmean从83.2%提升到85.0%。
- +
-LK-PAN(Large Kernel PAN)是一个具有更大感受野的轻量级[PAN](https://arxiv.org/pdf/1803.01534.pdf)结构。在LK-PAN的path augmentation中,使用卷积核为`9*9`的卷积;更大的卷积核意味着更大的感受野,更容易检测大字体的文字以及极端长宽比的文字。LKPAN将ppocr_server检测模型的精度hmean从83.2%提升到85.0%,加上DML自蒸馏后,hmean进一步提升到86。0%。 -蒸馏教师模型的消融试验如下: -|序号|策略|模型大小|hmean|Intel Gold 6148CPU+mkldnn预测耗时| -|-|-|-|-|-| -|0|ppocr_server|49M|83.2%|171ms| -|1|+ LK-PAN|124M|85.0%|396ms| -|2|+ DML|124M|86.0%|396ms| +教师模型DML训练的pipeline如下: + +
+ +
-- 在蒸馏学生模型精度提升方面,使用hmean 86%的模型作为CML中的教师模型,精度更高的蒸馏教师模型可以给学生模型更好的监督信息。另外,提出了基于残差结构的通道注意力模块RSE-FPN(Residual Squeeze-and-Excitation FPN),用于提升学生模型精度和召回。 +- 在学生模型精度提升方面,使用Hmean指标为86%的模型作为CML的教师模型,精度更高的教师模型可以给学生模型更好的监督信息。另外,提出了基于残差结构的通道注意力模块RSE-FPN(Residual Squeeze-and-Excitation FPN),用于提升学生模型精度和召回。 RSE-FPN的网络结构如下图所示,RSE-FPN在PP-OCRv2的FPN基础上,将FPN中的卷积层更换为了通道注意力结构的RSEConv层。 @@ -55,14 +54,19 @@ RSE-FPN的网络结构如下图所示,RSE-FPN在PP-OCRv2的FPN基础上,将F -PP-OCRv2的FPN通道数仅为96和24,如果直接用SEblock代替FPN中卷积会导致某些通道的特征被抑制,进而导致精度下降,RSEConv引入残差结构可以防止训练中包含重要特征的通道被抑制。RSE-FPN将PP-OCR检测模型的精度hmean从81.3%提升到84.5%。模型大小从3M变为3.6M。CPU预测速度从平均117ms/image变为124ms/image。 +PP-OCRv2的FPN通道数仅为96和24,如果直接用SEblock代替FPN中卷积会导致某些通道的特征被抑制,进而导致精度下降,RSEConv引入残差结构可以防止训练中包含重要特征的通道被抑制,使精度提升。RSE-FPN将PP-OCR检测模型的精度Hmean从81.3%提升到84.5%。模型大小从3M变为3.6M。CPU预测速度从平均117ms/image变为124ms/image。 -蒸馏学生模型的消融试验如下: -|序号|策略|模型大小|hmean|Intel Gold 6148CPU+mkldnn预测耗时| +消融实验如下: +|序号|策略|模型大小|hmean|速度(cpu + mkldnn)| |-|-|-|-|-| -|0|PP-OCRv2|3M|81.3%|117ms| -|1|+ teacher(dml)|3M|84.3%|117ms| -|2|+ RSE-FPN|3.6M|85.4%|124ms| +|0|PP-OCRv2|3M|83.2%|117ms| +|1|PP-OCR server|49M|83.2%|171ms| +|2|teacher1:DB-R50-LK-PAN|124M|85.0%|396ms| +|3|teacher2:DB-R50-LK-PAN-DML|124M|86.0%|396ms| +|4|DB-MV3-CML(teacher2)|3M|84.3%|117ms| +|5|DB-MV3-RSE-FPN-CML(teacher2)|3.6M|85.4%|124ms| + +注: CPU速度测试硬件是Intel Gold 6148,paddlepaddle版本是2.2.2,速度耗时为305张图的平均预测时间,预测时开启MKLDNN加速。 diff --git a/doc/ppocr_v3/LKPAN.png b/doc/ppocr_v3/LKPAN.png new file mode 100644 index 0000000000000000000000000000000000000000..6b1605362317da48110b64a1a774b6f1e017eaa1 GIT binary patch literal 94683 zcma%j2UJtr@;6Osf(Rl??^Qsm(whhg(z{4eI#L7?qy$6+lp>H&qy{iSnl$Mh8%3HC zlp;l`p_9-;%Xfm_d%gd6-+iBJv2r}wXP?a) z%*#-dFe~LBn>DSk@eoDXx`B);O3*$7Ub$Wf;;=u z%3bgr&kEX^7+>4RDF#g#&t*zY2JQ4UWBbxvUD`N_h$&QwNa!9Ck^SrC1$vjBa`A>o zgkfRyl;4z!@Q>*;YA$CC4a@prV_sHAdho9{TNqE)e30O>1*fmtqdJLu2%4NSj0yqC3 zk2t;xu|e*{C=%!k5hp5QicKOC*tKo1CE~_*vHu~g zut#JRSDD^v@DX0wP5*rSrAMi=*gS{F>TWfh>D4@Ya-D}(*@jfUwPf%?lGE0wQNywN 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