Wei Shengyu
GitHub
commit
5c17de1a8a
|
|
@ -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)。
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
59
README_en.md
59
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.
|
||||
|
||||
<div align="center">
|
||||
<img src="./docs/images/class_simple.gif" width = "600" />
|
||||
|
||||
PULC demo images
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div align="center">
|
||||
<img src="./docs/images/recognition.gif" width = "400" />
|
||||
|
||||
PP-ShiTu demo images
|
||||
</div>
|
||||
|
||||
**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).
|
||||
|
||||
|
||||
|
||||
|
||||
<div align="center">
|
||||
<img src="./docs/images/recognition_en.gif" width = "400" />
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
## Welcome to Join the Technical Exchange Group
|
||||
|
||||
|
|
@ -48,11 +50,13 @@ Four sample solutions are provided, including product recognition, vehicle recog
|
|||
</div>
|
||||
|
||||
## 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)
|
||||
|
||||
<a name="Introduction_to_PULC"></a>
|
||||
## Introduction to Practical Ultra Light-weight image Classification solutions
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/19523330/173011854-b10fcd7a-b799-4dfd-a1cf-9504952a3c44.png" width = "800" />
|
||||
</div>
|
||||
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.
|
||||
|
||||
<a name="Introduction_to_Image_Recognition_Systems"></a>
|
||||
## 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.
|
||||
|
||||
<a name="Demo_images"></a>
|
||||
## Demo images [more](https://github.com/PaddlePaddle/PaddleClas/tree/release/2.2/docs/images/recognition/more_demo_images)
|
||||
## PULC demo images
|
||||
<div align="center">
|
||||
<img src="docs/images/classification.gif">
|
||||
</div>
|
||||
|
||||
<a name="Rec_Demo_images"></a>
|
||||
## Image Recognition Demo images [more](https://github.com/PaddlePaddle/PaddleClas/tree/release/2.2/docs/images/recognition/more_demo_images)
|
||||
- Product recognition
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/18028216/122769644-51604f80-d2d7-11eb-8290-c53b12a5c1f6.gif" width = "400" />
|
||||
|
|
|
|||
Loading…
Reference in New Issue