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FastDeploy/examples/vision/detection/yolov5
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yeliang2258 b42ec302e6 [Doc] Change all PaddleLite or Paddle-Lite to Paddle Lite (#929) 
* [FlyCV] Bump up FlyCV -> official release 1.0.0

* change PaddleLite or Paddle-Lite to Paddle lite

* fix docs

* fix doc

Co-authored-by: DefTruth <qiustudent_r@163.com>
Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
2022-12-21 14:15:50 +08:00
..
a311d
[Doc] Change all PaddleLite or Paddle-Lite to Paddle Lite (#929)
2022-12-21 14:15:50 +08:00
cpp
[Backend] Add KunlunXin XPU deploy support (#894)
2022-12-16 11:22:30 +08:00
python
[Backend] Add KunlunXin XPU deploy support (#894)
2022-12-16 11:22:30 +08:00
quantize
[Other] Update old Api to new ones (#861)
2022-12-14 17:25:58 +08:00
rv1126
[Doc] Change all PaddleLite or Paddle-Lite to Paddle Lite (#929)
2022-12-21 14:15:50 +08:00
serving
[Serving]serving configuration doc add TRT dynamic shape (#920)
2022-12-20 15:15:17 +08:00
README.md
[Model] Update YOLOv5 from 6.0 to 7.0 version (#890)
2022-12-15 17:23:27 +08:00

README.md

YOLOv5准备部署模型

  • YOLOv5 v7.0部署模型实现来自YOLOv5,和基于COCO的预训练模型
    • （1）官方库提供的*.onnx可直接进行部署；
    • （2）开发者基于自己数据训练的YOLOv5 v7.0模型，可使用YOLOv5中的export.py导出ONNX文件后，完成部署。

下载预训练ONNX模型

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）

模型 大小 精度
YOLOv5n 7.6MB 28.0%
YOLOv5s 28MB 37.4%
YOLOv5m 82MB 45.4%
YOLOv5l 178MB 49.0%
YOLOv5x 332MB 50.7%

详细部署文档

版本说明