From 24869c9415630d374f4105eb395971d2179621e5 Mon Sep 17 00:00:00 2001 From: tink2123 Date: Fri, 6 May 2022 05:35:07 +0000 Subject: [PATCH] update rec introduc --- doc/doc_ch/PP-OCRv3_introduction.md | 34 +++++++++++++++++----------- doc/ppocr_v3/GTC.png | Bin 424940 -> 243918 bytes doc/ppocr_v3/LCNet_SVTR.png | Bin 0 -> 292502 bytes doc/ppocr_v3/ppocr_v3.png | Bin 436384 -> 0 bytes doc/ppocr_v3/svtr_g2.png | Bin 331043 -> 184105 bytes doc/ppocr_v3/svtr_g4.png | Bin 562904 -> 212817 bytes doc/ppocr_v3/svtr_tiny.png | Bin 599656 -> 743285 bytes 7 files changed, 21 insertions(+), 13 deletions(-) create mode 100644 doc/ppocr_v3/LCNet_SVTR.png delete mode 100644 doc/ppocr_v3/ppocr_v3.png diff --git a/doc/doc_ch/PP-OCRv3_introduction.md b/doc/doc_ch/PP-OCRv3_introduction.md index dc0271f2..57068e9d 100644 --- a/doc/doc_ch/PP-OCRv3_introduction.md +++ b/doc/doc_ch/PP-OCRv3_introduction.md @@ -70,7 +70,7 @@ LKPAN(Large Kernel PAN)是一个具有更大感受野的轻量级[PAN](https://a ## 3. 识别优化 -[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%,SVTR_Tiny 网络结构如下所示: @@ -83,11 +83,22 @@ PP-OCRv3 期望在提升模型精度的同时,不带来额外的推理耗时 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 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: - + + +| 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 | PP-LCNet_SVTR(G4) | 9.2M | 76% | 30ms | +| 04 | PP-LCNet_SVTR(G2) | 13M | 72.98% | 9.37ms | +| 05 | PP-LCNet_SVTR | 12M | 71.9% | 6.6ms | + +注: 测试速度时,输入图片尺寸均为(3,32,320) 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: + 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: @@ -114,17 +125,14 @@ PP-OCRv3 期望在提升模型精度的同时,不带来额外的推理耗时 |-----|-----|--------|----| --- | | 01 | PP-OCRv2 | 8M | 69.3% | 8.54ms | | 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 | -| 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,实验01-05输入图片尺寸均为(3,32,320),06-10输入图片尺寸均为(3,48,320) +| 03 | PP-LCNet_SVTR | 12M | 71.9% | 6.6ms | +| 04 | + GTC | 12M | 75.8% | 7.6ms | +| 05 | + TextConAug | 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