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feat: add yolov10 node #9198

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Description

add yolov10 node

Related links

#8753

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How was this PR tested?

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None.

Interface changes

None.

Effects on system behavior

None.

Signed-off-by: suchang <[email protected]>
@github-actions github-actions bot added type:documentation Creating or refining documentation. (auto-assigned) component:perception Advanced sensor data processing and environment understanding. (auto-assigned) tag:require-cuda-build-and-test labels Oct 30, 2024
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github-actions bot commented Oct 30, 2024

Thank you for contributing to the Autoware project!

🚧 If your pull request is in progress, switch it to draft mode.

Please ensure:

@xmfcx xmfcx added the tag:run-build-and-test-differential Mark to enable build-and-test-differential workflow. (used-by-ci) label Oct 30, 2024
@xmfcx xmfcx marked this pull request as draft October 30, 2024 09:54
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xmfcx commented Oct 30, 2024

Thank you very much for the contribution!

image

Please pass the tests marked as Required then when it is finished, you can mark it as Ready for review.

@storrrrrrrrm storrrrrrrrm marked this pull request as ready for review November 4, 2024 01:57
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storrrrrrrrm commented Nov 4, 2024

2024-11-04.16.59.28.mp4

@@ -0,0 +1,197 @@
// Copyright 2022 TIER IV, Inc.
//
// Licensed under the Apache License, Version 2.0 (the "License");
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@Shin-kyoto Shin-kyoto Nov 18, 2024

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@storrrrrrrrm
[must]
Can you use Apache 2.0?
Did you check the licence of Yolov10? It is AGPL3.0

cf. https://www.reddit.com/r/computervision/comments/1e3uxro/ultralytics_new_agpl30_license_exploiting/?rdt=41888

both the training code and the models produced by that code are covered by AGPL-3.0.

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@liuXinGangChina liuXinGangChina Nov 19, 2024

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Morning Shin san @Shin-kyoto , the cf link you provide can not be open due to net block policy

I read the AGPL3.0 license,which is pretty strict when code used for commercial purpose,at the same time it may also contaminate the project。While Apache 2.0 looks more friendly。

for the inference code ,since we reference the original code from tire4‘s tensorrt-yolo-x, so i believe it should follow the tire4 license。 And for the model and retrain code,we are not sure how to deal with。

I have an idea and would like to discuss with you

  1. create a tensorrt-yolo-v10 node in autoware universe repo using apache 2.0。 Highlight a notice in the readme file which tell the developer that model and retrained model under AGPL 3.0, those who want to use the model in their project shall consider the license issue seriously
  2. and if 1 works ,we may fork the retrain code in a separate repo outside autoware and retrain the original model with tire4 dataset then release the fine-tune model for autoware developer use

What do you think about the above solution,feel free to hear comment from you

Have a nice day!
Xingang

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@xmfcx xmfcx Nov 25, 2024

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Hi @Shin-kyoto and @liuXinGangChina 👋

Yes, this is just the inference code. And the inference code is based on autoware_tensorrt_yolox package which uses Apache 2.0. So this package can also make use of this license.

We should separate the training, model and inference licenses.

For now, @storrrrrrrrm could you update the readme file to include the information about the training code and the model licenses?

Example: https://github.com/autowarefoundation/autoware.universe/tree/main/perception/autoware_lidar_centerpoint#legal-notice


currently, this file is actually a coco label which contains the following labels:

```
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component:perception Advanced sensor data processing and environment understanding. (auto-assigned) tag:require-cuda-build-and-test tag:run-build-and-test-differential Mark to enable build-and-test-differential workflow. (used-by-ci) type:documentation Creating or refining documentation. (auto-assigned)
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4 participants