From d17444f5500b02fb12f306c96a4a90f59fa44a5b Mon Sep 17 00:00:00 2001 From: tink2123 Date: Thu, 5 May 2022 02:52:08 +0000 Subject: [PATCH] update pics --- doc/doc_ch/ppocr_introduction.md | 6 +++--- doc/ppocr_v3/ppocr_v3.png | Bin 436242 -> 436384 bytes 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/doc/doc_ch/ppocr_introduction.md b/doc/doc_ch/ppocr_introduction.md index 443ab263..dfc5b45e 100644 --- a/doc/doc_ch/ppocr_introduction.md +++ b/doc/doc_ch/ppocr_introduction.md @@ -60,7 +60,7 @@ 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 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: +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: @@ -90,7 +90,7 @@ PP-OCRv3 期望在提升模型精度的同时,不带来额外的推理耗时 - 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。 -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: +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%。 具体消融实验如下所示: 实验细节: @@ -108,7 +108,7 @@ PP-OCRv3 期望在提升模型精度的同时,不带来额外的推理耗时 | 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) +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) ## 2. 特性 diff --git a/doc/ppocr_v3/ppocr_v3.png b/doc/ppocr_v3/ppocr_v3.png index 72eb5eaac14a9e5248728732bb1e4a2f765f7482..123c125acdcbc9e2ef6e4d6a0a1c92d01136ffde 100644 GIT binary patch literal 436384 zcmeEtWl)^Y`Xzx72<`-j!97TD7=pVy1b6oUNf_MS2^K7n0Kp{?GPn~GAh>&QXMm9% 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