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30 lines
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[English](README.md) | 简体中文
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# YOLOv5准备部署模型
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- YOLOv5 v7.0部署模型实现来自[YOLOv5](https://github.com/ultralytics/yolov5/tree/v7.0),和[基于COCO的预训练模型](https://github.com/ultralytics/yolov5/releases/tag/v7.0)
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- (1)[官方库](https://github.com/ultralytics/yolov5/releases/tag/v7.0)提供的*.onnx可直接进行部署;
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- (2)开发者基于自己数据训练的YOLOv5 v7.0模型,可使用[YOLOv5](https://github.com/ultralytics/yolov5)中的`export.py`导出ONNX文件后,完成部署。
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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YOLOv5导出的各系列模型,开发者可直接下载使用。(下表中模型的精度来源于源官方库)
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| 模型 | 大小 | 精度 | 备注 |
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|:---------------------------------------------------------------- |:----- |:----- |:---- |
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| [YOLOv5n](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5n.onnx) | 7.6MB | 28.0% | 此模型文件来源于[YOLOv5](https://github.com/ultralytics/yolov5),GPL-3.0 License |
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| [YOLOv5s](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s.onnx) | 28MB | 37.4% | 此模型文件来源于[YOLOv5](https://github.com/ultralytics/yolov5),GPL-3.0 License |
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| [YOLOv5m](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5m.onnx) | 82MB | 45.4% | 此模型文件来源于[YOLOv5](https://github.com/ultralytics/yolov5),GPL-3.0 License |
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| [YOLOv5l](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5l.onnx) | 178MB | 49.0% | 此模型文件来源于[YOLOv5](https://github.com/ultralytics/yolov5),GPL-3.0 License |
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| [YOLOv5x](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5x.onnx) | 332MB | 50.7% | 此模型文件来源于[YOLOv5](https://github.com/ultralytics/yolov5),GPL-3.0 License |
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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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- [服务化部署](serving)
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## 版本说明
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- 本版本文档和代码基于[YOLOv5 v7.0](https://github.com/ultralytics/yolov5/tree/v7.0) 编写
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