From d7e539e4f45758bc005172f06aa4cdab38f8289b Mon Sep 17 00:00:00 2001 From: tink2123 Date: Tue, 3 May 2022 16:11:26 +0000 Subject: [PATCH] update introduction --- doc/doc_ch/ppocr_introduction.md | 59 +++++++++++++++++-------------- doc/ppocr_v3/ppocr_v3.png | Bin 410148 -> 541331 bytes doc/ppocr_v3/svtr_g2.png | Bin 410078 -> 435889 bytes doc/ppocr_v3/svtr_g4.png | Bin 342176 -> 376366 bytes 4 files changed, 32 insertions(+), 27 deletions(-) diff --git a/doc/doc_ch/ppocr_introduction.md b/doc/doc_ch/ppocr_introduction.md index e1980dd2..549ddda6 100644 --- a/doc/doc_ch/ppocr_introduction.md +++ b/doc/doc_ch/ppocr_introduction.md @@ -54,56 +54,61 @@ 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](todo:add_link) 证明了强大的单视觉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。基于SVTR的工作,PP-OCRv3首先验证了SVTR_tiny结构在中文数据上的效果。 -实验发现SVTR_tiny在中文测试集上acc可以提升10.7%。 +[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,识别精度可以提升10.7%,网络结构如下所示: -非常遗憾,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。 +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 期望提升模型精度的同时不带来额外的推理耗时,以加速预测为目的,分析得到主要耗时部分在Transformer Block,并对 SVTR_tiny 的结构进行了一系列优化: +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: -1. 将SVTR网络前半部分替换为LCNet的前三个stage,保留4个 SVTR 的 Global attenntion,精度为76%,速度基本不变。 +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: -2. 将4个Global attention 减小到2个,精度为72.9%,速度提升3倍。 +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. attention 的预测速度与输入其feature的shape有关,因此移动Global attention至avg_pool后,精度下降为71.9%,速度超越 CNN-base 的PP-OCRv2 27%。 +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: -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%。 +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: -训练策略方面参考 [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%。 +在训练策略方面,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%, +数据增强方面: + +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: +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%。 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:结合 LCNet 与[SVTR](todo:add_link) 的 Transformer block,去除LSTM特征解码模块,并行处理序列特征,兼顾精度与预测速度;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。 -基于上述策略,PP-OCRv3识别模型相比上一版本,速度基本无损,精度进一步提升4.5%。 具体消融实验: -实验细节: +- 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。 +- 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。 -| id | 策略 | 模型大小 | 精度 | -|-----|-----|--------|----| -| 01 | PP-OCRv2 | 8M | 69.3% | -| 02 | SVTR_tiny | 19M | 80.1% | -| 03 | LCNet_SVTR_G6 | 8.2M | 76% | -| 04 | LCNet_SVTR_G1 | - | 72.98% | -| 05 | PP-OCRv3 | 12M | 71.9% | -| 06 | + GTC | 12M | 75.8% | -| 07 | + RecConAug | 12M | 76.3% | -| 08 | + SSL pretrain | 12M | 76.9% | -| 09 | + UDML | 12M | 78.4% | -| 10 | + unlabeled data | 12M | 79.4% | +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4.5%。 体消融实验如下所示: +实验细节: +| 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 | +| 05 | PP-OCRv3 | 12M | 71.9% | 6.6ms | +| 06 | + large input_shape | 12M | 73.98% | 7.6ms | +| 06 | + GTC | 12M | 75.8% | 7.6ms | +| 07 | + RecConAug | 12M | 76.3% | 7.6ms | +| 08 | + SSL pretrain | 12M | 76.9% | 7.6ms | +| 09 | + UDML | 12M | 78.4% | 7.6ms | +| 10 | + unlabeled data | 12M | 79.4% | 7.6ms | + +注: 测试速度时,输入图片尺寸均为(3,32,320) 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