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FastDeploy/examples/vision/detection/yolov5seg/README.md
WJJ1995 aa6931bee9 [Model] Add YOLOv5-seg (#988) 
* add onnx_ort_runtime demo

* rm in requirements

* support batch eval

* fixed MattingResults bug

* move assignment for DetectionResult

* integrated x2paddle

* add model convert readme

* update readme

* re-lint

* add processor api

* Add MattingResult Free

* change valid_cpu_backends order

* add ppocr benchmark

* mv bs from 64 to 32

* fixed quantize.md

* fixed quantize bugs

* Add Monitor for benchmark

* update mem monitor

* Set trt_max_batch_size default 1

* fixed ocr benchmark bug

* support yolov5 in serving

* Fixed yolov5 serving

* Fixed postprocess

* update yolov5 to 7.0

* add poros runtime demos

* update readme

* Support poros abi=1

* rm useless note

* deal with comments

* support pp_trt for ppseg

* fixed symlink problem

* Add is_mini_pad and stride for yolov5

* Add yolo series for paddle format

* fixed bugs

* fixed bug

* support yolov5seg

* fixed bug

* refactor yolov5seg

* fixed bug

* mv Mask int32 to uint8

* add yolov5seg example

* rm log info

* fixed code style

* add yolov5seg example in python

* fixed dtype bug

* update note

* deal with comments

* get sorted index

* add yolov5seg test case

* Add GPL-3.0 License

* add round func

* deal with comments

* deal with commens

Co-authored-by: Jason <jiangjiajun@baidu.com>
2023-01-11 15:36:32 +08:00

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YOLOv5Seg准备部署模型

  • YOLOv5Seg 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YOLOv5Seg 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YOLOv5Seg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 备注
YOLOv5n-seg 7.7MB 27.6% 此模型文件来源于YOLOv5，GPL-3.0 License
YOLOv5s-seg 30MB 37.6% 此模型文件来源于YOLOv5，GPL-3.0 License
YOLOv5m-seg 84MB 45.0% 此模型文件来源于YOLOv5，GPL-3.0 License
YOLOv5l-seg 183MB 49.0% 此模型文件来源于YOLOv5，GPL-3.0 License
YOLOv5x-seg 339MB 50.7% 此模型文件来源于YOLOv5，GPL-3.0 License

详细部署文档

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