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# ⚡️FastDeploy
</p>
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<p align="center">
<a href="./LICENSE"><img src="https://img.shields.io/badge/license-Apache%202-dfd.svg"></a>
<a href="https://github.com/PaddlePaddle/FastDeploy/releases"><img src="https://img.shields.io/github/v/release/PaddlePaddle/FastDeploy?color=ffa"></a>
<a href=""><img src="https://img.shields.io/badge/python-3.7+-aff.svg"></a>
<a href=""><img src="https://img.shields.io/badge/os-linux%2C%20win%2C%20mac-pink.svg"></a>
<a href="https://github.com/PaddlePaddle/FastDeploy/graphs/contributors"><img src="https://img.shields.io/github/contributors/PaddlePaddle/FastDeploy?color=9ea"></a>
<a href="https://github.com/PaddlePaddle/FastDeploy/commits"><img src="https://img.shields.io/github/commit-activity/m/PaddlePaddle/FastDeploy?color=3af"></a>
<a href="https://pypi.org/project/FastDeploy-python/"><img src="https://img.shields.io/pypi/dm/FastDeploy-python?color=9cf"></a>
<a href="https://github.com/PaddlePaddle/FastDeploy/issues"><img src="https://img.shields.io/github/issues/PaddlePaddle/FastDeploy?color=9cc"></a>
<a href="https://github.com/PaddlePaddle/FastDeploy/stargazers"><img src="https://img.shields.io/github/stars/PaddlePaddle/FastDeploy?color=ccf"></a>
</p>
<h4 align="center">
<a href=#特性> 特性 </a> |
<a href=#服务器端> 服务器端 </a> |
<a href=#端侧> 端侧 </a> |
<a href=#社区交流> 社区交流 </a>
</h4>
**⚡️FastDeploy**是一款**简单易用**的推理部署工具箱。覆盖业界主流**优质预训练模型**并提供**开箱即用**的开发体验，包括图像分类、目标检测、图像分割、人脸检测、人体关键点识别、文字识别等多任务，满足开发者**多场景**，**多硬件**、**多平台**的快速部署需求。
## News 📢
* 🔥 2022.8.15 [**⚡️FastDeploy v0.2.0**](https://github.com/PaddlePaddle/FastDeploy/releases/tag/release%2F0.2.0)测试版发布！🎉
* 💎 升级服务器端（CPU/GPU/Jetson）SDK代码架构，速度SOTA
* 😊 支持PyTorch模型部署，如YOLOv5、YOLOv6、YOLOv7等热门模型
## 特性
### 📦开箱即用的推理部署工具链，支持云边端、多硬件、多平台部署
- 支持 PIP 安装，一行命令快速下载SDK安装包，开箱即用
- 服务器与云端部署：
- 跨平台：同时支持 Windows、Linux 操作系统
- 多语言：提供 Python、C++ 多种语言部署示例
- 移动与边缘端侧部署：
- 支持 iOS、Android 移动端部署
- 支持 ARM Linux、NVIDIA Jetson 系列等边缘设备
- 覆盖主流AI硬件：
- 支持 Intel CPU 系列（含酷睿、至强等）
- 支持 ARM CPU 全系（含高通、MTK、RK等）
- 支持 NVIDIA GPU 全系（含 A100、V100、T4、Jetson 等）
### 🤗丰富的预置模型与高性能部署示例
#### 服务器与云端（含Jetson）
| <font size=2> 任务场景 | <font size=2> 模型 | <font size=2> 大小(MB) | <font size=2>边缘端 | <font size=2>服务器/云端 | <font size=2>服务器/云端 | <font size=2> 服务器/云端 | <font size=2> 服务器/云端 |
| ------------------------------- | --------------------------------------------------------- | --------------------- | --------------------------------------- | ------------------------------- | -------------------- | ------------------------- | --------------------- |
| ---- | ---- | ---- | <font size=2> [Jetson](./doc/Jetson.md) | <font size=2> [X86 CPU](./doc/) | <font size=2>[GPU]() | <font size=2> [X86 CPU]() | <font size=2> [GPU]() |
| ---- | ---- | ---- | Linux | Windows | Linux | Windows | Linux |
| Classfication | | | | | | | |
| Detection | [NanoDet-Plus](./model_zoo/vision/nanodet_plus/README.md) | 0.95~2.44 | ✅ | ✅ | ✅ | ✅ | ✅ |
| | [YOLOR](./model_zoo/vison/yolor/README.md) | | ✅ | ✅ | ✅ | ✅ | ✅ |
| | [YOLOX](./model_zoo/vison/yolox/README.md) | | ✅ | ✅ | ✅ | ✅ | ✅ |
| | [Scaled-YOLOv4](./model_zoo/vison/scaledyolov4/README.md) | 4.9 | ✅ | ✅ | ✅ | ✅ | ✅ |
| | [YOLOv5](./model_zoo/vison/yolov5/README.md) | | ✅ | ✅ | ✅ | ✅ | ✅ |
| | [YOLOv5_Lite](./model_zoo/vison/yolov5lite/README.md) | 94.6 | ✅ | ✅ | ✅ | ✅ | ✅ |
| | [YOLOv6](./model_zoo/vison/yolov6/README.md) | 4.4 | ✅ | ✅ | ✅ | ✅ | ✅ |
| | [YOLOv7](./model_zoo/vison/yolov7/README.md) | 23.3 | ✅ | ✅ | ✅ | ✅ | ✅ |
| <font size=2>Face Detection | [UltraFace](./model_zoo/vison/ultraface/README.md) | 1.04~1.1 | ✅ | ✅ | ✅ | ✅ | ✅ |
| | [YOLOv5Face](./model_zoo/vison/yolov5face/README.md) | | ✅ | ✅ | ✅ | ✅ | ✅ |
| <font size=2>Face Localisation | [RetinaFace](./model_zoo/vison/retinaface/README.md) | 1.7M | ✅ | ✅ | ✅ | ✅ | ✅ |
| <font size=2>Face Recognition | [ArcFace](./model_zoo/vison/arcface/README.md) | 1.7 | ✅ | ✅ | ✅ | ✅ | ✅ |
| <font size=2>Keypoint Detection | [SCRFD](./model_zoo/vison/scrfd/README.md) | 5.5 | ✅ | ✅ | ✅ | ✅ | ✅ |
| <font size=2>Segmentation | [PP-Seg](./model_zoo/vison/ppseg/README.md) | 32.2 | ✅ | ✅ | ✅ | ✅ | ✅ |
| <font size=2>OCR | [PP-OCRv1](./model_zoo/vison/ppocrv1/README.md) | 2.3+4.4 | ✅ | ✅ | ✅ | ✅ | ✅ |
| | [PP-OCRv2](./model_zoo/vison/ppocrv2/README.md) | 2.3+4.4 | ✅ | ✅ | ✅ | ✅ | ✅ |
| | [PP-OCRv3](./model_zoo/vison/ppocrv3/README.md) | 2.4+10.6 | ✅ | ✅ | ✅ | ✅ | ✅ |
#### 快速开始（服务器与云端部署）
开发者可以通过pip安装`fastdeploy-python`来获取最新的下载链接
- 环境依赖
python >= 3.6
- 安装方式
```
pip install fastdeploy-python --upgrade
```
- 使用方式
- 列出FastDeploy当前支持的所有模型
```
fastdeploy --list_models
```
- 下载模型在具体平台和对应硬件上的部署SDK以及示例
```
fastdeploy --download_sdk \
--model PP-PicoDet-s_320 \
--platform Linux \
--soc x86 \
--save_dir .
```
- 参数说明
- `list_models`: 列出FastDeploy当前最新支持的所有模型
- `download_sdk`: 下载模型在具体平台和对应硬件上的部署SDK以及示例
- `model`: 模型名，如"PP-PicoDet-s_320"，可通过`list_models`查看所有的可选项
- `platform`: 部署平台，支持 Windows/Linux/Android/iOS
- `soc`: 部署硬件，支持 x86/x86-NVIDIA-GPU/ARM/Jetson
- `save_dir`: SDK下载保存目录
### 📱轻量化SDK快速实现端侧AI推理部署
| <font size=2> 任务场景 | <font size=2> 模型 | <font size=2> 大小(MB) | <font size=2>边缘端 | <font size=2>移动端 | <font size=2> 移动端 |
| ------------------ | ---------------------------- | --------------------- | --------------------- | ---------------------- | --------------------- |
| ---- | --- | --- | <font size=2> Linux | <font size=2> Android | <font size=2> iOS |
| ----- | ---- | --- | <font size=2> ARM CPU | <font size=2> ARM CPU | <font size=2> ARM CPU |
| Classfication | PP-LCNet | 11.9 | ✅ | ✅ | ✅ |
| | PP-LCNetv2 | 26.6 | ✅ | ✅ | ✅ |
| | EfficientNet | 31.4 | ✅ | ✅ | ✅ |
| | GhostNet | 20.8 | ✅ | ✅ | ✅ |
| | MobileNetV1 | 17 | ✅ | ✅ | ✅ |
| | MobileNetV2 | 14.2 | ✅ | ✅ | ✅ |
| | MobileNetV3 | 22 | ✅ | ✅ | ✅ |
| | ShuffleNetV2 | 9.2 | ✅ | ✅ | ✅ |
| | SqueezeNetV1.1 | 5 | ✅ | ✅ | ✅ |
| | Inceptionv3 | 95.5 | ✅ | ✅ | ✅ |
| | PP-HGNet | 59 | ✅ | ✅ | ✅ |
| | SwinTransformer_224_win7 | 352.7 | ✅ | ✅ | ✅ |
| Detection | PP-PicoDet_s_320_coco | 4.1 | ✅ | ✅ | ✅ |
| | PP-PicoDet_s_320_lcnet | 4.9 | ✅ | ✅ | ✅ |
| | CenterNet | 4.8 | ✅ | ✅ | ✅ |
| | YOLOv3_MobileNetV3 | 94.6 | ✅ | ✅ | ✅ |
| | PP-YOLO_tiny_650e_coco | 4.4 | ✅ | ✅ | ✅ |
| | SSD_MobileNetV1_300_120e_voc | 23.3 | ✅ | ✅ | ✅ |
| | PP-YOLO_ResNet50vd | 188.5 | ✅ | ✅ | ✅ |
| | PP-YOLOv2_ResNet50vd | 218.7 | ✅ | ✅ | ✅ |
| | PP-YOLO_crn_l_300e_coco | 209.1 | ✅ | ✅ | ✅ |
| | YOLOv5s | 29.3 | ✅ | ✅ | ✅ |
| Face Detection | BlazeFace | 1.5 | ✅ | ✅ | ✅ |
| Face Localisation | RetinaFace | 1.7 | ✅ | ❌ | ❌ |
| Keypoint Detection | PP-TinyPose | 5.5 | ✅ | ✅ | ✅ |
| Segmentation | PP-LiteSeg(STDC1) | 32.2 | ✅ | ✅ | ✅ |
| | PP-HumanSeg-Lite | 0.556 | ✅ | ✅ | ✅ |
| | HRNet-w18 | 38.7 | ✅ | ✅ | ✅ |
| | PP-HumanSeg-Server | 107.2 | ✅ | ✅ | ✅ |
| | Unet | 53.7 | ❌ | ✅ | ❌ |
| OCR | PP-OCRv1 | 2.3+4.4 | ✅ | ✅ | ✅ |
| | PP-OCRv2 | 2.3+4.4 | ✅ | ✅ | ✅ |
| | PP-OCRv3 | 2.4+10.6 | ✅ | ✅ | ✅ |
| | PP-OCRv3-tiny | 2.4+10.7 | ✅ | ✅ | ✅ |
#### 边缘侧部署
- ARM Linux 系统
- [C++ Inference部署（含视频流）](./docs/ARM-Linux-CPP-SDK-Inference.md)
- [C++ 服务化部署](./docs/ARM-Linux-CPP-SDK-Serving.md)
- [Python Inference部署](./docs/ARM-Linux-Python-SDK-Inference.md)
- [Python 服务化部署](./docs/ARM-Linux-Python-SDK-Serving.md)
#### 移动端部署
- [iOS 系统部署](./docs/iOS-SDK.md)
- [Android 系统部署](./docs/Android-SDK.md)
#### 自定义模型部署
- [快速实现个性化模型替换](./docs/Replace-Model-With-Anther-One.md)
## 社区交流
- **加入社区👬：** 微信扫描二维码后，填写问卷加入交流群，与开发者共同讨论推理部署痛点问题
<div align="center">
<img src="https://user-images.githubusercontent.com/54695910/175854075-2c0f9997-ed18-4b17-9aaf-1b43266d3996.jpeg" width = "200" height = "200" />
</div>
## Acknowledge
本项目中SDK生成和下载使用了[EasyEdge](https://ai.baidu.com/easyedge/app/openSource)中的免费开放能力，再次表示感谢。
## License
FastDeploy遵循[Apache-2.0开源协议](./LICENSE)。
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