From a5b5bfe4fe19b714dc5a7f4f45b2562678f60da0 Mon Sep 17 00:00:00 2001 From: LDOUBLEV Date: Thu, 5 May 2022 18:18:21 +0800 Subject: [PATCH] fix --- doc/doc_ch/PP-OCRv3_introduction.md | 11 ++++++----- doc/doc_ch/ppocr_introduction.md | 2 +- doc/ppocr_v3/LKPAN.png | Bin 128683 -> 133314 bytes 3 files changed, 7 insertions(+), 6 deletions(-) diff --git a/doc/doc_ch/PP-OCRv3_introduction.md b/doc/doc_ch/PP-OCRv3_introduction.md index f65ee7cc..55292ab1 100644 --- a/doc/doc_ch/PP-OCRv3_introduction.md +++ b/doc/doc_ch/PP-OCRv3_introduction.md @@ -44,10 +44,11 @@ RSEFPN的网络结构如下图所示,RSEFPN在PP-OCRv2的FPN基础上,将FPN - RSEFPN将PP-OCR检测模型的精度hmean从81.3%提升到84.5%。模型大小从3M变为3.6M。 -- 在蒸馏的teacher模型精度提升方面,提出了LKPAN结构替换PP-OCRv2的FPN结构,并且使用ResNet50作为Backbone,更大的模型带来更多的精度提升。另外,对teacher模型使用[DML](https://arxiv.org/abs/1706.00384)蒸馏策略进一步提升teacher模型的精度。最终teacher的模型指标hmean从83.2%提升到了86.0%。 +*注:PP-OCRv2的FPN通道数仅为96和24,如果直接用SE模块代替FPN的卷积会导致精度下降,使用残差结构可以防止训练中包含重要特征的通道被抑制。* + +- 在蒸馏的teacher模型精度提升方面,提出了LKPAN结构替换PP-OCRv2的FPN结构,并且使用ResNet50作为Backbone,更大的模型带来更多的精度提升。另外,对teacher模型使用[DML](https://arxiv.org/abs/1706.00384)蒸馏策略进一步提升teacher模型的精度。最终teacher的模型指标相比ppocr_server_v2.0从83.2%提升到了86.0%。 *注:[PP-OCRv2的FPN结构](https://github.com/PaddlePaddle/PaddleOCR/blob/77acb3bfe51c8a46c684527f73cd218cefedb4a3/ppocr/modeling/necks/db_fpn.py#L107)对DB算法FPN结构做了轻量级设计* @@ -57,7 +58,7 @@ LKPAN的网络结构如下图所示: -LKPAN(Large Kernel PAN)是一个具有更大感受野的轻量级[PAN](https://arxiv.org/pdf/1803.01534.pdf)结构。在LKPAN的path augmentation中,使用kernel size为`9*9`的卷积;更大的kernel size意味着更大的感受野,更容易检测大字体的文字以及极端长宽比的文字。LKPAN将base检测模型的精度hmean从81.3%提升到84.9%。 +LKPAN(Large Kernel PAN)是一个具有更大感受野的轻量级[PAN](https://arxiv.org/pdf/1803.01534.pdf)结构。在LKPAN的path augmentation中,使用kernel size为`9*9`的卷积;更大的kernel size意味着更大的感受野,更容易检测大字体的文字以及极端长宽比的文字。LKPAN将PP-OCR检测模型的精度hmean从81.3%提升到84.9%。 *注:LKPAN相比RSEFPN有更多的精度提升,但是考虑到模型大小和预测速度等因素,在student模型中使用RSEFPN。* @@ -71,8 +72,8 @@ LKPAN(Large Kernel PAN)是一个具有更大感受野的轻量级[PAN](https://a |1|PP-OCRV2|3M|83.3%|117ms| |2|0 + RESFPN|3.6M|84.5%|124ms| |3|0 + LKPAN|4.6M|84.9%|156ms| -|4|teacher |124M|83.2%|-| -|5|teacher + DML + LKPAN|124M|86.0%|-| +|4|ppocr_server_v2.0 |124M|83.2%||171ms| +|5|teacher + DML + LKPAN|124M|86.0%|396ms| |6|0 + 2 + 5 + CML|3.6M|85.4%|124ms| diff --git a/doc/doc_ch/ppocr_introduction.md b/doc/doc_ch/ppocr_introduction.md index c3163026..480f2caf 100644 --- a/doc/doc_ch/ppocr_introduction.md +++ b/doc/doc_ch/ppocr_introduction.md @@ -47,7 +47,7 @@ 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 |-|-|-|-|-| |0|ppocr_mobile|3M|81.3|117ms| |1|PPOCRV2|3M|83.3|117ms| -|2|teacher DML|124M|86.0|-| +|2|teacher DML + LKPAN|124M|86.0|396ms| |3|1 + 2 + RESFPN|3.6M|85.4|124ms| |4|1 + 2 + LKPAN|4.6M|86.0|156ms| diff --git a/doc/ppocr_v3/LKPAN.png b/doc/ppocr_v3/LKPAN.png index 87f7f69fb516d496c9357d81b97e5bdb750f808a..ff0578f6901603185809e10c85793c212c40dc48 100644 GIT binary patch literal 133314 zcmb?@by!qe`!*mThzN>;G!g=m(kX+ql(Yi^qI5UX4h9O6BHgJX4MT@P_s|VWx1{v9 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