From f55e946c5dbe5394bad48dda18cf367f94071b10 Mon Sep 17 00:00:00 2001 From: LDOUBLEV Date: Fri, 6 May 2022 13:55:01 +0800 Subject: [PATCH] fix doc --- doc/doc_ch/PP-OCRv3_introduction.md | 50 +++++++++++++--------------- doc/ppocr_v3/ppocrv3_det_cml.png | Bin 0 -> 268216 bytes doc/ppocr_v3/teacher_dml.png | Bin 0 -> 386915 bytes 3 files changed, 24 insertions(+), 26 deletions(-) create mode 100644 doc/ppocr_v3/ppocrv3_det_cml.png create mode 100644 doc/ppocr_v3/teacher_dml.png diff --git a/doc/doc_ch/PP-OCRv3_introduction.md b/doc/doc_ch/PP-OCRv3_introduction.md index dc0271f2..568f9434 100644 --- a/doc/doc_ch/PP-OCRv3_introduction.md +++ b/doc/doc_ch/PP-OCRv3_introduction.md @@ -23,48 +23,46 @@ 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,在蒸馏的student模型、teacher模型精度提升,CML蒸馏策略上分别做了优化。 +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 CML蒸馏训练框架图如下: -- 在蒸馏student模型精度提升方面,提出了基于残差结构的通道注意力模块RSEFPN(Residual Squeeze-and-Excitation FPN),用于提升student模型精度和召回。 +
+ +
+ +- 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%。 -RSEFPN的网络结构如下图所示,RSEFPN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。 +教师模型自蒸馏训练的pipeline如下:
- +
-RSEFPN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。 +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%。 -*注:PP-OCRv2的FPN通道数仅为96和24,如果直接用SE模块代替FPN的卷积会导致精度下降,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。* - -- 在蒸馏的teacher模型精度提升方面,提出了LKPAN结构替换PP-OCRv2的FPN结构,并且使用ResNet50作为Backbone,更大的模型带来更多的精度提升。另外,对teacher模型使用[DML](https://arxiv.org/abs/1706.00384)蒸馏策略进一步提升teacher模型的精度。最终teacher的模型指标相比ppocr_server_v2.0从83.2%提升到了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| -*注:[PP-OCRv2的FPN结构](https://github.com/PaddlePaddle/PaddleOCR/blob/77acb3bfe51c8a46c684527f73cd218cefedb4a3/ppocr/modeling/necks/db_fpn.py#L107)对DB算法FPN结构做了轻量级设计* +- 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。 -LKPAN的网络结构如下图所示: +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。
- +
-LKPAN(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LKPAN的path augmentation中,使用kernel size为`9*9`的卷积;更大的kernel size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PP-OCR检测模型的精度hmean从81.3%提升到84.9%。 - -*注:LKPAN相比RSEFPN有更多的精度提升,但是考虑到模型大小和预测速度等因素,在student模型中使用RSEFPN。* - -采用上述策略,PP-OCRv3相比PP-OCRv2,hmean指标从83.3%提升到85.4%;预测速度从平均117ms/image变为124ms/image。 - -3. PP-OCRv3检测模型消融实验 +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。 +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| |-|-|-|-|-| -|0|PP-OCR|3M|81.3%|117ms| -|1|PP-OCRV2|3M|83.3%|117ms| -|2|0 + RESFPN|3.6M|84.5%|124ms| -|3|0 + LKPAN|4.6M|84.9%|156ms| -|4|ppocr_server_v2.0 |124M|83.2%||171ms| -|5|teacher + DML + LKPAN|124M|86.0%|396ms| -|6|0 + 2 + 5 + CML|3.6M|85.4%|124ms| - +|0|PP-OCRv2|3M|81.3%|117ms| +|1|+ teacher(dml)|3M|83.8%|117ms| +|2|+ RSE-FPN|3.6M|84.5%|124ms| diff --git a/doc/ppocr_v3/ppocrv3_det_cml.png b/doc/ppocr_v3/ppocrv3_det_cml.png new file mode 100644 index 0000000000000000000000000000000000000000..ccb5c8b21faeab75027690e520b072186972f796 GIT binary patch literal 268216 zcmeFZWmH_-(l!c&1Pvs(hJ>I&gF7Jv*WeBb?i#Fd5=fBX?(UG_(zv@zMc_F2*nQKgS)AgjG2N0(ksL zOCmEMJ^1r`6eOfzOC;34#wa58fBeNGwm+`<*Zx5!@?WD7-({lwJ^GP;=7YbFQHuV! 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