From 3e6b6f4920aae3b075685c864e9ef84a25a14aa9 Mon Sep 17 00:00:00 2001 From: tink2123 Date: Sun, 1 May 2022 15:43:12 +0000 Subject: [PATCH] update ppocrv3 introduction --- doc/doc_ch/ppocr_introduction.md | 68 ++++++++++++++++++++++--------- doc/ppocr_v3/GTC.png | Bin 0 -> 424940 bytes doc/ppocr_v3/SSL.png | Bin 0 -> 114150 bytes doc/ppocr_v3/UDML.png | Bin 0 -> 357024 bytes doc/ppocr_v3/ppocr_v3.png | Bin 0 -> 410148 bytes doc/ppocr_v3/recconaug.png | Bin 0 -> 303742 bytes doc/ppocr_v3/svtr_g2.png | Bin 0 -> 410078 bytes doc/ppocr_v3/svtr_g4.png | Bin 0 -> 342176 bytes doc/ppocr_v3/svtr_tiny.jpg | Bin 0 -> 331368 bytes 9 files changed, 49 insertions(+), 19 deletions(-) create mode 100644 doc/ppocr_v3/GTC.png create mode 100644 doc/ppocr_v3/SSL.png create mode 100644 doc/ppocr_v3/UDML.png create mode 100644 doc/ppocr_v3/ppocr_v3.png create mode 100644 doc/ppocr_v3/recconaug.png create mode 100644 doc/ppocr_v3/svtr_g2.png create mode 100644 doc/ppocr_v3/svtr_g4.png create mode 100644 doc/ppocr_v3/svtr_tiny.jpg diff --git a/doc/doc_ch/ppocr_introduction.md b/doc/doc_ch/ppocr_introduction.md index c3163026..267e55f0 100644 --- a/doc/doc_ch/ppocr_introduction.md +++ b/doc/doc_ch/ppocr_introduction.md @@ -52,25 +52,55 @@ 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 |4|1 + 2 + LKPAN|4.6M|86.0|156ms| -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:使用[SVTR](todo:add_link)中的 Transformer block 替换LSTM,提升模型精度和预测速度; -- 训练策略上: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;设计方向分类前序任务,获取更优预训练模型,加速模型收敛过程,提升精度。 -- 数据增强上:使用[RecConAug](todo:add_link)数据增广方法,随机结合图片,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,速度加速30%,精度进一步提升4.5%。 具体消融实验: - -| id | 策略 | 模型大小 | 精度 | CPU+mkldnn 预测耗时 | -|-----|-----|--------|----|------------| -| 01 | PP-OCRv2 | 8M | 69.3% | 26ms | -| 02 | SVTR_tiny | 19M | 80.1% | - | -| 03 | LCNet_SVTR_G6 | 8.2M | 76% | - | -| 04 | LCNet_SVTR_G1 | - | - | - | -| 05 | PP-OCRv3 | 12M | 71.9% | 19ms | -| 06 | + GTC | 12M | 75.8% | 19ms | -| 07 | + RecConAug | 12M | 76.3% | 19ms | -| 08 | + SSL pretrain | 12M | 76.9% | 19ms | -| 09 | + UDML | 12M | 78.4% | 19ms | -| 10 | + unlabeled data | 12M | 79.4% | 19ms | +- 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%。 + +![](../ppocr_v3/svtr_tiny.jpg) + +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。 + +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: + +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。 +![](../ppocr_v3/svtr_g4.png) +2. 将4个Global attention 减小到2个,精度为72.9%,速度提升3倍。 +![](../ppocr_v3/svtr_g2.png) +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%。 +![](../ppocr_v3/ppocr_v3.png) + +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%。 +![](../ppocr_v3/GTC.png) + +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%。 +![](../ppocr_v3/SSL.png) ![](../ppocr_v3/UDML.png) + +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%, +![](../ppocr_v3/recconaug.png) + + +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: + +| id | 策略 | 模型大小 | 精度 | +|-----|-----|--------|----| +| 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