Suggestions on Yolov5 license issue

[wangxd-xlnx]

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Hi @glenn-jocher ,

Hello!

We're planning to redistribute this model based on an open source sharing agreement Apache 2.0, so that more researchers and users can use our open source toolchain to achieve quick deploy machine learning models on FPGA.
We hope choose yolov5 as a member of reference models, because of the good accuracy and performance of it on FPGA platform. Of course, we made some modification and compression on it to make it more easy to deploy on FPGA platform.

But then we ran into a legal problem because it is under GPLv3 liscense. Legal affairs told us we can’t release it with our toolchain product as a reference model. GPL liscense mandate all products that used GPL code to compliance with GPL liscense. ( Even if only a small part of the product used GPL code)

So we suggest whether you could consider changing the liscense of github repo. For example, Apache or MIT is more open and more convenient for the open source products to quote and spread it.

Best Regards

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[github-actions]

👋 Hello @wangxd-xlnx, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

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 training ❓ Question, please provide as much information as possible, including dataset images, training logs, screenshots, and a public link to online W&B logging if available.

For business inquiries or professional support requests please visit https://ultralytics.com or email Glenn Jocher at glenn.jocher@ultralytics.com.

Requirements

Python>=3.6.0 with all requirements.txt installed including PyTorch>=1.7. To get started:

$ git clone https://github.com/ultralytics/yolov5
$ cd yolov5
$ pip install -r requirements.txt

Environments

YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Google Colab and Kaggle notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Status

If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training (train.py), validation (val.py), inference (detect.py) and export (export.py) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.

[github-actions]

👋 Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs.

Access additional YOLOv5 🚀 resources:

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Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!

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