From d9c3a4b9954bdb5e5b143d9b121ee8f96618228a Mon Sep 17 00:00:00 2001 From: MissPenguin Date: Fri, 6 May 2022 15:28:23 +0000 Subject: [PATCH] update doc --- doc/doc_ch/PP-OCRv3_introduction.md | 143 +++++++++++++++++++--------- doc/ppocr_v3/UIM.png | Bin 0 -> 84550 bytes doc/ppocrv3_framework.png | Bin 980152 -> 1202594 bytes 3 files changed, 99 insertions(+), 44 deletions(-) create mode 100644 doc/ppocr_v3/UIM.png diff --git a/doc/doc_ch/PP-OCRv3_introduction.md b/doc/doc_ch/PP-OCRv3_introduction.md index 164f049d..3fc17ebe 100644 --- a/doc/doc_ch/PP-OCRv3_introduction.md +++ b/doc/doc_ch/PP-OCRv3_introduction.md @@ -8,85 +8,105 @@ - [4. 端到端评估](#4) - ## 1. 简介 -PP-OCRv3在PP-OCRv2的基础上进一步升级。检测模型仍然基于DB算法,优化策略采用了带残差注意力机制的FPN结构RSEFPN、增大感受野的PAN结构LKPAN、基于DML训练的更优的教师模型;识别模型将base模型从CRNN替换成了IJCAI 2022论文[SVTR](https://arxiv.org/abs/2205.00159),并采用SVTR轻量化、带指导训练CTC、数据增广策略RecConAug、自监督训练的更好的预训练模型、无标签数据的使用进行模型加速和效果提升。更多细节请参考PP-OCRv3[技术报告](./PP-OCRv3_introduction.md)。 - -PP-OCRv3系统pipeline如下: +PP-OCRv3在PP-OCRv2的基础上进一步升级。整体的框架图保持了与PP-OCRv2相同的pipeline,针对检测模型和识别模型进行了优化。其中,检测模型仍基于DB模型优化,而识别模型不再采用CRNN,换成了会议IJCAI 2022中的最新方法[SVTR](https://arxiv.org/abs/2205.00159),PP-OCRv3系统框图如下所示(粉色框中为PP-OCRv3新增策略):
+ +从算法改进思路上看,分别针对检测和识别模型,进行了共八个方面的改进: + + +- 检测模型优化: + - LK-PAN:增大感受野的PAN模块; + - DML:教师模型互学习策略; + - RSE-FPN:带残差注意力机制的FPN模块; + + +- 识别模型优化: + - SVTR_LCNet:轻量级文本识别网络; + - GTC:Attention指导CTC训练策略; + - TextConAug:丰富图像上下文信息的数据增广策略; + - TextRotNet:自监督的预训练模型; + - UIM:无标签数据挖掘方案。 + +从效果上看,速度可比情况下,多种场景精度均有大幅提升: +- 中文场景,相对于PP-OCRv2中文模型提升超5%; +- 英文数字场景,相比于PP-OCRv2英文模型提升11%; +- 多语言场景,优化80+语种识别效果,平均准确率提升超5%。 + + ## 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检测模型整体训练方案仍采用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。PP-OCRv3分别针对教师模型、学生模型进一步优化。其中,在对教师模型优化时,采用了增大感受野的PAN模块LK-PAN和DML蒸馏策略;在对学生模型优化时,采用了带残差注意力机制的FPN模块RSE-FPN。 + PP-OCRv3 CML蒸馏训练框架图如下:
-- 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%。 +消融实验如下: + +|序号|策略|模型大小|hmean|速度(cpu + mkldnn)| +|-|-|-|-|-| +|baseline teacher|PP-OCR server|49M|83.2%|171ms| +|teacher1|DB-R50-LK-PAN|124M|85.0%|396ms| +|teacher2|DB-R50-LK-PAN-DML|124M|86.0%|396ms| +|baseline student|PP-OCRv2|3M|83.2%|117ms| +|student0|DB-MV3-RSE-FPN|3.6M|84.5%|124ms| +|student1|DB-MV3-CML(teacher2)|3M|84.3%|117ms| +|student2|DB-MV3-RSE-FPN-CML(teacher2)|3.6M|85.4%|124ms| + +测试环境: Intel Gold 6148 CPU,预测时开启MKLDNN加速。 + +**(1)增大感受野的PAN模块LK-PAN(Large Kernel PAN)** -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。LK-PAN将PP-OCR server检测模型的hmean从83.2%提升到85.0%。
+**(2)DML(Deep Mutual Learning)蒸馏进一步提升teacher模型精度。** + +[DML](https://arxiv.org/abs/1706.00384) 互学习蒸馏方法,通过两个结构相同的模型互相学习,相比于传统的教师模型监督学生模型的蒸馏方法,DML 摆脱了对大的 Teacher 模型的依赖,蒸馏训练的流程更加简单。在PP-OCRv3的检测模型训练中,使用DML蒸馏策略进一步提升教师模型的精度,并使用ResNet50作为Backbone。DML策略将teacher模型的hmean从85%进一步提升至86%。 -教师模型DML训练的pipeline如下: +教师模型DML训练流程图如下:
-- 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。相比较PP-OCRv2,使用Hmean指标为86%的模型作为教师模型,Hmean指标从83.2提升到84.3%。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。 +**(3)带残差注意力机制的FPN模块RSE-FPN(Residual SE-FPN)。** -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。 +残差结构的通道注意力模块RSE-FPN结构如下图所示,RSE-FPN在PP-OCRv2的FPN基础上,将FPN中的卷积层更换为通道注意力结构的RSEConv层。考虑到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引入残差结构防止训练中包含重要特征的通道被抑制。直接添加RSE-FPN模块,可将PP-OCR检测模型的精度Hmean从81.3%提升到84.5%。在学生模型中加入RSE-FPN后进行CML蒸馏,比不加时,Hmean指标从83.2提升到84.3%。
-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。 - -消融实验如下: -|序号|策略|模型大小|hmean|速度(cpu + mkldnn)| -|-|-|-|-|-| -|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-RSE-FPN|3.6M|84.5%|124ms| -|5|DB-MV3-CML(teacher2)|3M|84.3%|117ms| -|6|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。 - ## 3. 识别优化 -PP-OCRv3 识别模型在 PP-OCRv2 的基础上从8个策略上进一步优化,PP-OCRv3系统流程图如下: +PP-OCRv3识别模型从网络结构、训练策略、数据增广等多个方面进行了优化,PP-OCRv3系统流程图如下所示: +
-总体来讲PP-OCRv3识别主要从网络结构、蒸馏策略、数据增强三个方向做了进一步优化: -- 网络结构上:考虑[SVTR](https://arxiv.org/abs/2205.00159) 在中英文效果上的优越性,采用SVTR_Tiny作为base,选取Global Mixing Block和卷积组合提取特征,并将Global Mixing Block位置后移进行加速; 参考 [GTC](https://arxiv.org/pdf/2002.01276.pdf) 策略,使用注意力机制模块指导CTC训练,定位和识别字符,提升不规则文本的识别精度。 -- 蒸馏策略上:参考 [SSL](https://github.com/ku21fan/STR-Fewer-Labels) 设计了方向分类前序任务,获取更优预训练模型,加速模型收敛过程,提升精度; 使用UDML蒸馏策略、监督attention、ctc两个分支得到更优模型。 -- 数据增强上:基于 [ConCLR](https://www.cse.cuhk.edu.hk/~byu/papers/C139-AAAI2022-ConCLR.pdf) 中的ConAug方法,改进得到 TextConAug 数据增广方法,支持随机结合任意多张图片,提升训练数据的上下文信息丰富度,增强模型鲁棒性 -- 无标注数据: 使用 SVTR_large 预测无标签数据,向训练集中补充81w高质量真实数据。 +上图中,蓝色方块中列举了PP-OCRv3识别模型的6个主要模块。首先在模块①,将base模型从CRNN替换为精度更高的单一视觉模型[SVTR](https://arxiv.org/abs/2205.00159),并进行一系列的结构优化进行加速,得到全新的轻量级文本识别网络SVTR_LCNet(如图中红色虚线框所示);在模块②,借鉴[GTC](https://arxiv.org/pdf/2002.01276.pdf)策略,引入Attention指导CTC训练,进一步提升模型精度;在模块③,使用基于上下文信息的数据增广策略TextConAug,丰富训练数据上下文信息,提升训练数据多样性;在模块④,使用TextRotNet训练自监督的预训练模型,充分利用无标注识别数据的信息;模块⑤基于PP-OCRv2中提出的UDML蒸馏策略进行蒸馏学习,除计算2个模型的CTC分支的DMLLoss外,也计算2个模型的Attention分支之间的DMLLoss,从而得到更优模型;在模块⑥中,基于UIM无标注数据挖掘方法,使用效果好但速度相对较慢的SVTR_tiny模型进行无标签数据挖掘,为模型训练增加更多真实数据。 + 基于上述策略,PP-OCRv3识别模型相比PP-OCRv2,在速度可比的情况下,精度进一步提升4.6%。 具体消融实验如下所示: -| ID | 策略 | 模型大小 | 精度 | 速度(CPU + MKLDNN)| +| ID | 策略 | 模型大小 | 精度 | 预测耗时(CPU + MKLDNN)| |-----|-----|--------|----| --- | | 01 | PP-OCRv2 | 8M | 74.8% | 8.54ms | | 02 | SVTR_Tiny | 21M | 80.1% | 97ms | @@ -100,16 +120,17 @@ PP-OCRv3 识别模型在 PP-OCRv2 的基础上从8个策略上进一步优化, 注: 测试速度时,实验01-03输入图片尺寸均为(3,32,320),04-08输入图片尺寸均为(3,48,320)。在实际预测时,图像为变长输入,速度会有所变化。 -下面具体介绍各策略的设计思路: +**(1)轻量级文本识别网络SVTR_LCNet。** -网络结构上,PP-OCRv3将base模型从CRNN替换成了[SVTR](https://arxiv.org/abs/2205.00159),SVTR证明了强大的单视觉模型(无需序列模型)即可高效准确完成文本识别任务,在中英文数据上均有优秀的表现。经过实验验证,SVTR_Tiny 在自建的 [中文数据集上](https://arxiv.org/abs/2109.03144) ,识别精度可以提升至80.1%,SVTR_Tiny 网络结构如下所示: +PP-OCRv3将base模型从CRNN替换成了[SVTR](https://arxiv.org/abs/2205.00159),SVTR证明了强大的单视觉模型(无需序列模型)即可高效准确完成文本识别任务,在中英文数据上均有优秀的表现。经过实验验证,SVTR_Tiny 在自建的[中文数据集](https://arxiv.org/abs/2109.03144)上 ,识别精度可以提升至80.1%,SVTR_Tiny 网络结构如下所示:
-由于 MKLDNN 加速库支持的模型结构有限,SVTR 在 CPU+MKLDNN 上相比 PP-OCRv2 慢了10倍。 -PP-OCRv3 期望在提升模型精度的同时,不带来额外的推理耗时。通过分析发现,SVTR_Tiny 结构的主要耗时模块为 Mixing Block,因此我们对 SVTR_Tiny 的结构进行了一系列优化(详细速度数据请参考下方消融实验表格): + +由于 MKLDNN 加速库支持的模型结构有限,SVTR 在 CPU+MKLDNN 上相比 PP-OCRv2 慢了10倍。PP-OCRv3 期望在提升模型精度的同时,不带来额外的推理耗时。通过分析发现,SVTR_Tiny 结构的主要耗时模块为 Mixing Block,因此我们对 SVTR_Tiny 的结构进行了一系列优化(详细速度数据请参考下方消融实验表格): + 1. 将 SVTR 网络前半部分替换为 PP-LCNet 的前三个stage,保留4个 Global Mixing Block ,精度为76%,加速69%,网络结构如下所示:
@@ -136,28 +157,62 @@ PP-OCRv3 期望在提升模型精度的同时,不带来额外的推理耗时 注: 测试速度时,输入图片尺寸均为(3,32,320); PP-OCRv2-baseline 代表没有借助蒸馏方法训练得到的模型 +**(2)采用Attention指导CTC训练。** + 为了提升模型精度同时不引入额外推理成本,PP-OCRv3 参考 GTC(Guided Training of CTC) 策略,使用 Attention 监督 CTC 训练,预测时完全去除 Attention 模块,在推理阶段不增加任何耗时, 精度提升3.8%,训练流程如下所示:
+**(3)TextConAug数据增广策略。** -在蒸馏策略方面: +在论文[ConCLR](https://www.cse.cuhk.edu.hk/~byu/papers/C139-AAAI2022-ConCLR.pdf)中,作者提出ConAug数据增广,在一个batch内对2张不同的图像进行联结,组成新的图像并进行自监督对比学习。PP-OCRv3将此方法应用到有监督的学习任务中,设计了TextConAug数据增强方法,支持更多图像的联结,从而进一步丰富了图像的上下文信息。最终将识别模型精度进一步提升0.5%。TextConAug示意图如下所示: -PP-OCRv3参考 [SSL](https://github.com/ku21fan/STR-Fewer-Labels) 设计了文本方向任务,训练了适用于文本识别的预训练模型,加速模型收敛过程,精度提升了0.6%; 使用UDML蒸馏策略,进一步提升精度1.5%,训练流程所示:
- +
-数据增强方面: +**(4)TextRotNet无监督训练优化预训练模型。** + +为了充分利用自然场景中的大量无标注文本数据,PP-OCRv3参考论文[STR-Fewer-Labels](https://github.com/ku21fan/STR-Fewer-Labels),设计TextRotNet自监督任务,对识别图像进行旋转并预测其旋转角度,同时结合中文场景文字识别任务的特点,在训练时适当调整图像的尺寸,添加文本识别数据增广,最终产出针对文本识别任务的PP-LCNet预训练模型,帮助识别模型精度进一步提升0.6%。TextRotNet训练流程如下图所示: -1. 基于 [ConCLR](https://www.cse.cuhk.edu.hk/~byu/papers/C139-AAAI2022-ConCLR.pdf) 中的ConAug方法,设计了 RecConAug 数据增强方法,增强数据多样性,精度提升0.5%,增强可视化效果如下所示:
- + +
+ + +**(5)UIM(Unlabeled Images Mining)无标签数据挖掘。** + +为更直接利用自然场景中包含大量无标注数据,使用PP-OCRv2检测模型以及SVTR_tiny识别模型对百度开源的40W [LSVT弱标注数据集](https://ai.baidu.com/broad/introduction?dataset=lsvt)进行检测与识别,并筛选出识别得分大于0.95的文本,共81W文本行数据,将其补充到训练数据中,最终进一步提升模型精度1.0%。 + +
+
-2. 使用训练好的 SVTR_large 预测 120W 的 lsvt 无标注数据,取出其中得分大于0.95的数据,共得到81W识别数据加入到PP-OCRv3的训练数据中,精度提升1%。 ## 4. 端到端评估 + +经过以上优化,最终PP-OCRv3在速度可比情况下,中文场景端到端Hmean指标相比于PP-OCRv2提升5%,效果大幅提升。具体指标如下表所示: + +| Model | Hmean | Model Size (M) | Time Cost (CPU, ms) | Time Cost (T4 GPU, ms) | +|-----|-----|--------|----| --- | +| PP-OCR mobile | 50.3% | 8.1 | 356 | 116 | +| PP-OCR server | 57.0% | 155.1 | 1056 | 200 | +| PP-OCRv2 | 57.6% | 11.6 | 330 | 111 | +| PP-OCRv3 | 62.9% | 15.6 | 331 | 86.64 | + +除了更新中文模型,本次升级也同步优化了英文数字模型,端到端效果提升11%,如下表所示: + +| Model | Recall | Precision | Hmean | +|-----|-----|--------|----| +| PP-OCR_en | 38.99% | 45.91% | 42.17% | +| PP-OCRv3_en | 50.95% | 55.53% | 53.14% | + +同时,也对已支持的80余种语言识别模型进行了升级更新,在有评估集的四种语系识别准确率平均提升5%以上,如下表所示: + +| Model | 拉丁语系 | 阿拉伯语系 | 日语 | 韩语 | +|-----|-----|--------|----| --- | +| PP-OCR_mul | 69.6% | 40.5% | 38.5% | 55.4% | +| PP-OCRv3_mul | 75.2%| 45.37% | 45.8% | 60.1% | diff --git a/doc/ppocr_v3/UIM.png b/doc/ppocr_v3/UIM.png new file mode 100644 index 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