https://github.com/Baiyuetribe/ncnn-models

awesome AI models with NCNN, and how they were converted ✨✨✨
https://github.com/Baiyuetribe/ncnn-models

deep-learning gfpgan image-classification nanodet ncnn ncnn-model pytorch rife yolov5 yolov7 yolox

Last synced: 2 months ago
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awesome AI models with NCNN, and how they were converted ✨✨✨

• Host: GitHub
• URL: https://github.com/Baiyuetribe/ncnn-models
• Owner: Baiyuetribe
• License: mit
• Created: 2022-05-20T02:28:29.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2023-07-07T15:32:23.000Z (over 1 year ago)
• Last Synced: 2024-11-07T17:33:51.672Z (2 months ago)
• Topics: deep-learning, gfpgan, image-classification, nanodet, ncnn, ncnn-model, pytorch, rife, yolov5, yolov7, yolox
• Language: C++
• Homepage: https://github.com/Baiyuetribe/ncnn-models
• Size: 150 MB
• Stars: 253
• Watchers: 10
• Forks: 29
• Open Issues: 15
• Metadata Files:
  • Readme: README-CN.md
  • License: LICENSE

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README

        
##  
 ncnn models 


The collection of pre-trained AI models, and how they were converted, deployed.

![](docs/images/logo.png)

### 关于

ncnn框架借助vulkan实现了全平台部署，我们通过pytorch、tensorflow、飞桨等预训练模型，然后转换成ncnn通用模型，实现Windows、mac、linux、安卓、ios、WebAssembly 以及uni-app的最终部署。然而模型转换并不是一键的，需要手动处理。为了拓展ncnn的边界应用，我们建立此仓库，欢迎提交接收任何转换成功或失败的案例。

### 如何参与贡献？

fork代码，然后按照如下格式提交，最好是c++20 的最小demo，不要内嵌并发。

yolov5  # 项目名称

- models/xx.bin or xx.param # model

- input.png # 输入

- out.png # 输出

- README.md # 模型推理介绍、转换步骤、相关案例

- convert.py # 从pytorch等模型转换具体复现代码

灵感来自[ailia](https://github.com/axinc-ai/ailia-models),鉴于接收失败案例为主，因此失败案例优先排序，成功案例分类排序。

### 一些代表

- [nihui](https://github.com/nihui) ncnn作者

- [飞哥](https://github.com/feigechuanshu) ncnn安卓系列

- [EdVince](https://github.com/EdVince) ncnn自然语言系列

- [baiyue](https://github.com/Baiyuetribe/paper2gui) ncnn在PC桌面GUI系列

- [670***@qq.com](https://ext.dcloud.net.cn/plugin?id=5243) ncnn在uni-app中的应用

- [nihui](https://github.com/nihui/ncnn_on_esp32) ncnn在嵌入式设备上的应用

- [nihui](https://github.com/nihui/ncnn-webassembly-yolov5) ncnn在wasm实现案例

### 跨设备的意义

> 维护一次代码，多设备可用。

- 借助vulkan，无需繁重的cuda驱动

- 借助wasm实现任意设备的模型部署

- 借助uni-app可绕过java实现多端部署