From e5eaeec87797ab450d81038e99277b9d9c5e4c3e Mon Sep 17 00:00:00 2001 From: tink2123 Date: Wed, 4 May 2022 17:29:13 +0000 Subject: [PATCH] update ppocrv3_rec_introduction --- doc/doc_ch/ppocr_introduction.md | 16 ++++++++-------- doc/ppocr_v3/ppocr_v3.png | Bin 541331 -> 436242 bytes doc/ppocr_v3/svtr_g2.png | Bin 435889 -> 331043 bytes doc/ppocr_v3/svtr_g4.png | Bin 376366 -> 562904 bytes doc/ppocr_v3/svtr_tiny.png | Bin 0 -> 599656 bytes 5 files changed, 8 insertions(+), 8 deletions(-) create mode 100644 doc/ppocr_v3/svtr_tiny.png diff --git a/doc/doc_ch/ppocr_introduction.md b/doc/doc_ch/ppocr_introduction.md index 173f0c56..443ab263 100644 --- a/doc/doc_ch/ppocr_introduction.md +++ b/doc/doc_ch/ppocr_introduction.md @@ -54,19 +54,19 @@ PP-OCRv3文本检测从网络结构、蒸馏训练策略两个方向做了进一 - PP-OCRv3 文本识别 -[SVTR](https://arxiv.org/abs/2205.00159) 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10.7%,网络结构如下所示: +[SVTR](https://arxiv.org/abs/2205.00159) 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10.7%,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Transformer 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: +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 SVTR 的 Global Attenntion Block,精度为76%,加速69%,网络结构如下所示: +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: 2. 将4个 Global Attenntion Block 减小到2个,精度为72.9%,加速69%,网络结构如下所示: -3. 实验发现 Global Attention 的预测速度与输入其特征的shape有关,因此后移Global Attenntion Block的位置到池化层之后,精度下降为71.9%,速度超越 CNN-base 的PP-OCRv2 22%,网络结构如下所示: +3. 实验发现 Global Attention 的预测速度与输入其特征的shape有关,因此后移Global Mixing Block的位置到池化层之后,精度下降为71.9%,速度超越 CNN-base 的PP-OCRv2 22%,网络结构如下所示: 为了提升模型精度同时不引入额外推理成本,PP-OCRv3参考GTC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: @@ -86,7 +86,7 @@ PP-OCRv3 期望在提升模型精度的同时,不带来额外的推理耗时 总体来讲PP-OCRv3识别从网络结构、训练策略、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 Attention Block和卷积组合提取特征,并将Global Attention 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。 +- 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。 - 训练策略上: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 RecConAug 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;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。 @@ -97,9 +97,9 @@ PP-OCRv3 期望在提升模型精度的同时,不带来额外的推理耗时 | id | 策略 | 模型大小 | 精度 | 速度(cpu + mkldnn)| |-----|-----|--------|----| --- | | 01 | PP-OCRv2 | 8M | 69.3% | 8.54ms | -| 02 | SVTR_tiny | 21M | 80.1% | 97ms | -| 03 | LCNet_SVTR_G6 | 9.2M | 76% | 30ms | -| 04 | LCNet_SVTR_G1 | 13M | 72.98% | 9.37ms | +| 02 | SVTR_Tiny | 21M | 80.1% | 97ms | +| 03 | LCNet_SVTR_G4 | 9.2M | 76% | 30ms | +| 04 | LCNet_SVTR_G2 | 13M | 72.98% | 9.37ms | | 05 | PP-OCRv3 | 12M | 71.9% | 6.6ms | | 06 | + large input_shape | 12M | 73.98% | 7.6ms | | 06 | + GTC | 12M | 75.8% | 7.6ms | diff --git a/doc/ppocr_v3/ppocr_v3.png b/doc/ppocr_v3/ppocr_v3.png index 972f24b40f316e6af032c901932f06330583bad7..72eb5eaac14a9e5248728732bb1e4a2f765f7482 100644 GIT binary patch literal 436242 zcmeGDbx<5%^e>8{!QI_GxD#Lq8Z5Yz;O-ED1qRpP!3hKj?gR^g2?W;=++6}BxC{(4 zugUj!&VBDyy{h-;t-7bWU}|9X?%mycuf0C9rB%~K{8Ace!dc))eX1c15VVEzly5g8$ zWD(0JFftl65T(h<#|bjUE~F}<&+{=`Di|q|zQSR2>3>b$zs5kW|G~G>QejsU`{3|q z?S2i~-ElLS;{(?dx>=(_dQ%mk>x5s0BUi_&MNUCKMxB>7!YqS~$%w=&@jl>P#|JtZ z8uU+>>j9r0)q(T@!jFMURTMf?N)R ztusQJAJpWF4c zypn@tD+~-x)`VESIo$iV!a?4p#M6v_(@IqaGwKuimb82LfM1w4J$3zNO{Qj2!tX&UA$+rK zv&r2)kpL$edcPFq|v6=f~wC zQM^NY-#372nLPoh+w$^Ip5YDQ9q04LH~?fjHW{yayj$%*EOZ3&h_Ozex;8l z!vT+j<=)_J{)rTC=*tm6(xX%*k`lyj{o`s8AQ8Xy1NjJpPR~zbpzepI! 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