- 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】**.
- 2020.11.20: Release 2.0-beta version, fully migrate the dynamic graph programming mode, and upgrade the service deployment Serving capability; add 1 hand key point 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】**.
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@@ -30,9 +30,9 @@ English | [简体中文](README_ch.md)
-**【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
-**【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.