diff --git a/README_ch.md b/README_ch.md index 320bd1819..08816e470 100644 --- a/README_ch.md +++ b/README_ch.md @@ -40,7 +40,8 @@ ## 特性 -支持多种图像分类、识别相关算法，在此基础上打造[PULC超轻量图像分类方案](docs/zh_CN/PULC/PULC_person_exists.md)和[PP-ShiTu图像识别系统](./docs/zh_CN/quick_start/quick_start_recognition.md)。 +PaddleClas发布了[PP-HGNet](docs/zh_CN/models/PP-HGNet.md)、[PP-LCNetv2](docs/zh_CN/models/PP-LCNetV2.md)、 [PP-LCNet](docs/zh_CN/models/PP-LCNet.md)和[SSLD半监督知识蒸馏方案](docs/zh_CN/advanced_tutorials/ssld.md)等算法， +并支持多种图像分类、识别相关算法，在此基础上打造[PULC超轻量图像分类方案](docs/zh_CN/PULC/PULC_quickstart.md)和[PP-ShiTu图像识别系统](./docs/zh_CN/quick_start/quick_start_recognition.md)。 ![](https://user-images.githubusercontent.com/19523330/173273046-239a42da-c88d-4c2c-94b1-2134557afa49.png) diff --git a/README_en.md b/README_en.md index 9b0d7c85d..f6bb7e33c 100644 --- a/README_en.md +++ b/README_en.md @@ -4,10 +4,24 @@ ## Introduction -PaddleClas is an image recognition toolset for industry and academia, helping users train better computer vision models and apply them in real scenarios. +PaddleClas is an image classification and image recognition toolset for industry and academia, helping users train better computer vision models and apply them in real scenarios. + +
+ + +PULC demo images +
+  + + +
+ + +PP-ShiTu demo images +
**Recent updates** - +- 2022.6.15 Release [**P**ractical **U**ltra **L**ight-weight image **C**lassification solutions](./docs/en/PULC/PULC_quickstart_en.md). PULC models inference within 3ms on CPU devices, with accuracy comparable with SwinTransformer. We also release 9 practical models covering pedestrian, vehicle and OCR. - 2022.4.21 Added the related [code](https://github.com/PaddlePaddle/PaddleClas/pull/1820/files) of the CVPR2022 oral paper [MixFormer](https://arxiv.org/pdf/2204.02557.pdf). - 2021.09.17 Add PP-LCNet series model developed by PaddleClas, these models show strong competitiveness on Intel CPUs. @@ -19,24 +33,12 @@ For the introduction of PP-LCNet, please refer to [paper](https://arxiv.org/pdf/ ## Features -- A practical image recognition system consist of detection, feature learning and retrieval modules, widely applicable to all types of image recognition tasks. -Four sample solutions are provided, including product recognition, vehicle recognition, logo recognition and animation character recognition. - -- Rich library of pre-trained models: Provide a total of 164 ImageNet pre-trained models in 35 series, among which 6 selected series of models support fast structural modification. - -- Comprehensive and easy-to-use feature learning components: 12 metric learning methods are integrated and can be combined and switched at will through configuration files. - -- SSLD knowledge distillation: The 14 classification pre-training models generally improved their accuracy by more than 3%; among them, the ResNet50_vd model achieved a Top-1 accuracy of 84.0% on the Image-Net-1k dataset and the Res2Net200_vd pre-training model achieved a Top-1 accuracy of 85.1%. - -- Data augmentation: Provide 8 data augmentation algorithms such as AutoAugment, Cutout, Cutmix, etc. with detailed introduction, code replication and evaluation of effectiveness in a unified experimental environment. +PaddleClas release PP-HGNet、PP-LCNetv2、 PP-LCNet and **S**imple **S**emi-supervised **L**abel **D**istillation algorithms, and support plenty of +image classification and image recognition algorithms. +Based on th algorithms above, PaddleClas release PP-ShiTu image recognition system and [**P**ractical **U**ltra **L**ight-weight image **C**lassification solutions](docs/en/PULC/PULC_quickstart_en.md). - - -
- -
- +![](https://user-images.githubusercontent.com/19523330/173347904-f2998e00-7b86-4adf-b546-23c684fc67b9.png) ## Welcome to Join the Technical Exchange Group @@ -48,11 +50,13 @@ Four sample solutions are provided, including product recognition, vehicle recog ## Quick Start -Quick experience of image recognition：[Link](./docs/en/tutorials/quick_start_recognition_en.md) +Quick experience of PP-ShiTu image recognition system：[Link](./docs/en/tutorials/quick_start_recognition_en.md) +Quick experience of **P**ractical **U**ltra **L**ight-weight image **C**lassification models：[Link](docs/en/PULC/PULC_quickstart.md) ## Tutorials - [Quick Installation](./docs/en/tutorials/install_en.md) +- [Practical Ultra Light-weight image Classification solutions](./docs/en/PULC/PULC_quickstart_en.md) - [Quick Start of Recognition](./docs/en/tutorials/quick_start_recognition_en.md) - [Introduction to Image Recognition Systems](#Introduction_to_Image_Recognition_Systems) - [Demo images](#Demo_images) @@ -83,6 +87,14 @@ Quick experience of image recognition：[Link](./docs/en/tutorials/quick_start_r - [License](#License) - [Contribution](#Contribution) + +## Introduction to Practical Ultra Light-weight image Classification solutions +
+ +
+PULC solutions consists of PP-LCNet light-weight backbone, SSLD pretrained models, Ensemble of Data Augmentation strategy and SKL-UGI knowledge distillation. +PULC models inference within 3ms on CPU devices, with accuracy comparable with SwinTransformer. We also release 9 practical models covering pedestrian, vehicle and OCR. + ## Introduction to Image Recognition Systems @@ -97,8 +109,13 @@ Image recognition can be divided into three steps: For a new unknown category, there is no need to retrain the model, just prepare images of new category, extract features and update retrieval database and the category can be recognised. - -## Demo images [more](https://github.com/PaddlePaddle/PaddleClas/tree/release/2.2/docs/images/recognition/more_demo_images) +## PULC demo images +
+ +
+ + +## Image Recognition Demo images [more](https://github.com/PaddlePaddle/PaddleClas/tree/release/2.2/docs/images/recognition/more_demo_images) - Product recognition