From 46c52d437bedd4985a18b173b9d91755c66bbf06 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Thu, 1 Sep 2022 12:37:48 +0000 Subject: [PATCH] update docs --- README_ch.md | 12 +- README_en.md | 2 +- docs/images/structure.png | Bin 389488 -> 1912917 bytes docs/zh_CN/PPShiTu/PPShiTuV2_introduction.md | 151 ++++++++++++++++++ docs/zh_CN/PPShiTu/PPShiTu_introduction.md | 23 +++ docs/zh_CN/algorithm_introduction/reid.md | 2 +- .../feature_extraction.md | 20 +-- 7 files changed, 195 insertions(+), 15 deletions(-) create mode 100644 docs/zh_CN/PPShiTu/PPShiTuV2_introduction.md create mode 100644 docs/zh_CN/PPShiTu/PPShiTu_introduction.md diff --git a/README_ch.md b/README_ch.md index 2ca73fdc..351fbf44 100644 --- a/README_ch.md +++ b/README_ch.md @@ -127,21 +127,26 @@ PULC融合了骨干网络、数据增广、蒸馏等多种前沿算法,可以 PaddleClas提供了覆盖人、车、OCR场景九大常见任务的分类模型,CPU推理3ms,精度比肩SwinTransformer。 -## PP-ShiTu图像识别系统 + +## PP-ShiTuV2图像识别系统 + +在 **[PP-ShiTu](https://arxiv.org/pdf/2111.00775.pdf)** 的基础上,我们针对数据集、特征提取模块进行了优化,加入了更多数据集和更为通用的优化策略,使得模型的检索精度在多种常见场景下得到显著提升。更多细节请参考 **[PP-ShiTuV2](./docs/zh_CN/PPShiTu/PPShiTu_introduction.md)**
- +
-PP-ShiTu是一个实用的轻量级通用图像识别系统,主要由主体检测、特征学习和向量检索三个模块组成。该系统从骨干网络选择和调整、损失函数的选择、数据增强、学习率变换策略、正则化参数选择、预训练模型使用以及模型裁剪量化8个方面,采用多种策略,对各个模块的模型进行优化,最终得到在CPU上仅0.2s即可完成10w+库的图像识别的系统。更多细节请参考[PP-ShiTu技术方案](https://arxiv.org/pdf/2111.00775.pdf)。 +* PP-ShiTu 是一个实用的轻量级通用图像识别系统,主要由主体检测、特征学习和向量检索三个模块组成。该系统从骨干网络选择和调整、损失函数的选择、数据增强、学习率变换策略、正则化参数选择、预训练模型使用以及模型裁剪量化8个方面,采用多种策略,对各个模块的模型进行优化,最终得到在CPU上仅0.2s即可完成10w+库的图像识别的系统。更多细节请参考 [PP-ShiTu技术方案](https://arxiv.org/pdf/2111.00775.pdf)。 + ## PULC实用图像分类模型效果展示
+ ## PP-ShiTu图像识别系统效果展示 - 瓶装饮料识别
@@ -177,6 +182,7 @@ PP-ShiTu是一个实用的轻量级通用图像识别系统,主要由主体检 + ## 贡献代码 我们非常欢迎你为PaddleClas贡献代码,也十分感谢你的反馈。 如果想为PaddleCLas贡献代码,可以参考[贡献指南](./docs/zh_CN/advanced_tutorials/how_to_contribute.md)。 diff --git a/README_en.md b/README_en.md index 4bf960e5..1efd9e4e 100644 --- a/README_en.md +++ b/README_en.md @@ -105,7 +105,7 @@ PULC models inference within 3ms on CPU devices, with accuracy comparable with S ## Introduction to Image Recognition Systems
- +
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