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* fit yolov7face file path * TODO：添加yolov7facePython接口Predict * resolve yolov7face.py * resolve yolov7face.py * resolve yolov7face.py * add yolov7face example readme file * [Doc] fix yolov7face example readme file * [Doc]fix yolov7face example readme file
46 lines
3.0 KiB
Markdown
46 lines
3.0 KiB
Markdown
# YOLOv7Face准备部署模型
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- YOLOv7Face部署模型实现来自[YOLOv7Face](https://github.com/derronqi/yolov7-face),和[基于WiderFace的预训练模型](https://github.com/derronqi/yolov7-face)
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- （1）[官方库](https://github.com/derronqi/yolov7-face)中提供的*.pt通过[导出ONNX模型](#导出ONNX模型)操作后，可进行部署；
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- （2）开发者基于自己数据训练的YOLOv7Face模型，可按照[导出ONNX模型](#%E5%AF%BC%E5%87%BAONNX%E6%A8%A1%E5%9E%8B)后，完成部署。
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## 导出ONNX模型
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访问[YOLOv7Face](https://github.com/derronqi/yolov7-face)官方github库，按照指引下载安装，下载`.pt` 模型，利用 `export.py` 得到`onnx`格式文件。
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* 下载yolov7模型文件
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| Method | Test Size | Easy | Medium | Hard | FLOPs (B) @640 | Link |
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| -----------------| ---------- | ----- | ------ | ----- | -------------- | ----- |
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| yolov7-lite-t | 640 | 88.7 | 85.2 | 71.5 | 0.8 | [google](https://drive.google.com/file/d/1HNXd9EdS-BJ4dk7t1xJDFfr1JIHjd5yb/view?usp=sharing) |
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| yolov7-lite-s | 640 | 92.7 | 89.9 | 78.5 | 3.0 | [google](https://drive.google.com/file/d/1MIC5vD4zqRLF_uEZHzjW_f-G3TsfaOAf/view?usp=sharing) |
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| yolov7-tiny | 640 | 94.7 | 92.6 | 82.1 | 13.2 | [google](https://drive.google.com/file/d/1Mona-I4PclJr5mjX1qb8dgDeMpYyBcwM/view?usp=sharing) |
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| yolov7s | 640 | 94.8 | 93.1 | 85.2 | 16.8 | [google](https://drive.google.com/file/d/1_ZjnNF_JKHVlq41EgEqMoGE2TtQ3SYmZ/view?usp=sharing) |
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| yolov7 | 640 | 96.9 | 95.5 | 88.0 | 103.4 | [google](https://drive.google.com/file/d/1oIaGXFd4goyBvB1mYDK24GLof53H9ZYo/view?usp=sharing) |
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| yolov7+TTA | 640 | 97.2 | 95.8 | 87.7 | 103.4 | [google](https://drive.google.com/file/d/1oIaGXFd4goyBvB1mYDK24GLof53H9ZYo/view?usp=sharing) |
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| yolov7-w6 | 960 | 96.4 | 95.0 | 88.3 | 89.0 | [google](https://drive.google.com/file/d/1U_kH7Xa_9-2RK2hnyvsyMLKdYB0h4MJS/view?usp=sharing) |
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| yolov7-w6+TTA | 1280 | 96.9 | 95.8 | 90.4 | 89.0 | [google](https://drive.google.com/file/d/1U_kH7Xa_9-2RK2hnyvsyMLKdYB0h4MJS/view?usp=sharing) |
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* 导出onnx格式文件
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```bash
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python ./models/export.py --weights yolov7-tiny-face.pt --grid --simplify --img-size 640 640
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```
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## 下载预训练ONNX模型
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为了方便开发者的测试，下面提供了YOLOv7Face导出的各系列模型，开发者可直接下载使用。
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| 模型 | 大小 |
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|:---------------------------------------------------------------- |:----- |
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| [yolov7-lite-e](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-lite-e.onnx) | 3.2MB |
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| [yolov7-tiny-face](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-tiny-face.onnx) | 30.3MB |
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## 详细部署文档
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- [Python部署](python)
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- [C++部署](cpp)
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## 版本说明
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- 本版本文档和代码基于[YOLOv7Face](https://github.com/derronqi/yolov7-face) 编写
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