- PaddleHub aims to provide developers with rich, high-quality, and directly usable pre-trained models.
-**【No need for deep learning background, no data and training process】**，you can use AI models quickly and enjoy the dividends of the artificial intelligence era.
- Covers the four major categories of CV, NLP, Audio, and Video, and supports **one-click prediction**, **one-click service deployment** and **migration learning**
- All models are open source download, **offline can run**.
- 2020.11.20: Release 2.0-beta version, fully migrate the dynamic graph programming mode, and upgrade the service deployment Serving capability; add 1 hand key detection model, 12 image animation models, 3 image editing models, 3 speech synthesis models, syntax Analyzing one, the total number of pre-trained models reaches **【182】**.
- 2020.10.09: Added 4 new OCR multi-language series models, 4 image editing models, and the total number of pre-trained models reached **【162】**.
- 2020.09.27: 6 new text generation models and 1 image segmentation model were added, and the total number of pre-trained models reached **【154】**.
- 2020.08.13: Released v1.8.1, added a segmentation model, and supports EMNLP2019-Sentence-BERT as a text matching task network. The total number of pre-training models reaches **【147】**.
- 2020.07.29: Release v1.8.0, new AI couplets and AI writing poems, jieba word cutting, text data LDA, semantic similarity calculation, new target detection, short video classification model, ultra-lightweight Chinese and English OCR, new pedestrian detection, vehicle Industrial-grade models such as detection and animal recognition support VisualDL visualization training, and the total number of pre-training models reaches **【135】**.
## 精品模型效果展示
## Features
-**【Abundant Pre-trained Models】**: 180+ pre-trained models covering the four major categories of CV, NLP, Audio, and Video, all open source downloads, and can be run offline.
-**【Quick Model Prediction】**: Model calls can be realized through a one-line command line or a minimalist Python API to quickly experience the model effect.
-**【Model As Service】**: A one-line command to build deep learning model API service deployment capabilities.
-**【Ten Lines of Code for Transfer Learning】**: Ten lines of code complete the transfer-learning task of image classification and text classification
-**【PIP installation 】**: Support PIP quick installation and use
-**【Cross-platform Compatibility】**: Can run on Linux, Windows, MacOS and other operating systems
-Contains ultra-lightweight Chinese and English OCR models, high-precision Chinese and English, multilingual German, French, Japanese, Korean OCR recognition.
-If you have any questions during the use of the model, you can join the official WeChat group to get more efficient questions and answers, and fully communicate with developers from all walks of life. We look forward to your joining.
The release of this project is certified by the <ahref="./LICENSE">Apache 2.0 license</a>.
<aname="Contribution"></a>
## Contribution
We welcome you to contribute code to PaddleHub, and thank you for your feedback.
* Many thanks to [Austendeng](https://github.com/Austendeng) for fixing the SequenceLabelReader
* Many thanks to [cclauss](https://github.com/cclauss) optimizing travis-ci check
* Many thanks to [奇想天外](http://www.cheerthink.com/)，Contributed a demo of mask detection
* Many thanks to [mhlwsk](https://github.com/mhlwsk)，Contributed the repair sequence annotation prediction demo
* Many thanks to [zbp-xxxp](https://github.com/zbp-xxxp)，Contributed modules for viewing pictures and writing poems
* Many thanks to [zbp-xxxp](https://github.com/zbp-xxxp) and [七年期限](https://github.com/1084667371),Jointly contributed to the Mid-Autumn Festival Special Edition Module
* Many thanks to [livingbody](https://github.com/livingbody)，Contributed models for style transfer based on PaddleHub's capabilities and Mid-Autumn Festival WeChat Mini Program
- PaddleHub aims to provide developers with rich, high-quality, and directly usable pre-trained models.
-**【No need for deep learning background, no data and training process】**，you can use AI models quickly and enjoy the dividends of the artificial intelligence era.
- Covers the four major categories of CV, NLP, Audio, and Video, and supports **one-click prediction**, **one-click service deployment** and **migration learning**
- All models are open source download, **offline can run**.
**Recent updates**
- 2020.11.20: Release 2.0-beta version, fully migrate the dynamic graph programming mode, and upgrade the service deployment Serving capability; add 1 hand key detection model, 12 image animation models, 3 image editing models, 3 speech synthesis models, syntax Analyzing one, the total number of pre-trained models reaches **【182】**.
- 2020.10.09: Added 4 new OCR multi-language series models, 4 image editing models, and the total number of pre-trained models reached **【162】**.
- 2020.09.27: 6 new text generation models and 1 image segmentation model were added, and the total number of pre-trained models reached **【154】**.
- 2020.08.13: Released v1.8.1, added a segmentation model, and supports EMNLP2019-Sentence-BERT as a text matching task network. The total number of pre-training models reaches **【147】**.
- 2020.07.29: Release v1.8.0, new AI couplets and AI writing poems, jieba word cutting, text data LDA, semantic similarity calculation, new target detection, short video classification model, ultra-lightweight Chinese and English OCR, new pedestrian detection, vehicle Industrial-grade models such as detection and animal recognition support VisualDL visualization training, and the total number of pre-training models reaches **【135】**.
## Features
-**【Abundant Pre-trained Models】**: 180+ pre-trained models covering the four major categories of CV, NLP, Audio, and Video, all open source downloads, and can be run offline.
-**【Quick Model Prediction】**: Model calls can be realized through a one-line command line or a minimalist Python API to quickly experience the model effect.
-**【Model As Service】**: A one-line command to build deep learning model API service deployment capabilities.
-**【Ten Lines of Code for Transfer Learning】**: Ten lines of code complete the transfer-learning task of image classification and text classification
-**【PIP installation 】**: Support PIP quick installation and use
-**【Cross-platform Compatibility】**: Can run on Linux, Windows, MacOS and other operating systems
## Visualization
### Text recognition
- Contains ultra-lightweight Chinese and English OCR models, high-precision Chinese and English, multilingual German, French, Japanese, Korean OCR recognition.
- If you have any questions during the use of the model, you can join the official WeChat group to get more efficient questions and answers, and fully communicate with developers from all walks of life. We look forward to your joining.
The release of this project is certified by the <ahref="./LICENSE">Apache 2.0 license</a>.
<aname="Contribution"></a>
## Contribution
We welcome you to contribute code to PaddleHub, and thank you for your feedback.
* Many thanks to [Austendeng](https://github.com/Austendeng) for fixing the SequenceLabelReader
* Many thanks to [cclauss](https://github.com/cclauss) optimizing travis-ci check
* Many thanks to [奇想天外](http://www.cheerthink.com/)，Contributed a demo of mask detection
* Many thanks to [mhlwsk](https://github.com/mhlwsk)，Contributed the repair sequence annotation prediction demo
* Many thanks to [zbp-xxxp](https://github.com/zbp-xxxp)，Contributed modules for viewing pictures and writing poems
* Many thanks to [zbp-xxxp](https://github.com/zbp-xxxp) and [七年期限](https://github.com/1084667371),Jointly contributed to the Mid-Autumn Festival Special Edition Module
* Many thanks to [livingbody](https://github.com/livingbody)，Contributed models for style transfer based on PaddleHub's capabilities and Mid-Autumn Festival WeChat Mini Program
