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Deploy with OpenCV DNN module #1043

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MohamedAliRashad opened this issue Sep 25, 2020 · 5 comments
Closed

Deploy with OpenCV DNN module #1043

MohamedAliRashad opened this issue Sep 25, 2020 · 5 comments
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enhancement New feature or request Stale

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@MohamedAliRashad
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🚀 Feature

The ability to deploy yolov5 with the dnn module in opencv

Motivation

Consistency with the previous yolo models and ease of deployment with opencv

Pitch

We need a script that can produce the cfg file and the weights file of a trained yolo model

Alternatives

ONNX can be used but its is still very buggy on opencv and yolov5

@MohamedAliRashad MohamedAliRashad added the enhancement New feature or request label Sep 25, 2020
@github-actions
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github-actions bot commented Sep 25, 2020

Hello @MohamedAliRashad, thank you for your interest in our work! Please visit our Custom Training Tutorial to get started, and see our Jupyter Notebook Open In Colab, Docker Image, and Google Cloud Quickstart Guide for example environments.

If this is a bug report, please provide screenshots and minimum viable code to reproduce your issue, otherwise we can not help you.

If this is a custom model or data training question, please note Ultralytics does not provide free personal support. As a leader in vision ML and AI, we do offer professional consulting, from simple expert advice up to delivery of fully customized, end-to-end production solutions for our clients, such as:

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@github-actions
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This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.

@XiaoLaoDi
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@MohamedAliRashad have you found a way to deploy the yolov5 in opencv dnn module ? would you mind to share ?

@hzitoun
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hzitoun commented Apr 20, 2021

Did anyone convert YOLOV5 to cfg and weights files we can use with opencv dnn module please?

@OlivierLDff
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We managed to load yolov5 onnx file with opencv/opencv#19546
Result is ok with cpu backend, but cuda backend doesn't work as expected.
(Output is scaled x2, and not exactly matching cpu results)

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4 participants