Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Implement autodistill-yolov9 #139

Open
2 tasks done
Youho99 opened this issue Mar 11, 2024 · 3 comments
Open
2 tasks done

Implement autodistill-yolov9 #139

Youho99 opened this issue Mar 11, 2024 · 3 comments
Labels
enhancement New feature or request

Comments

@Youho99
Copy link

Youho99 commented Mar 11, 2024

Search before asking

  • I have searched the Autodistill issues and found no similar feature requests.

Description

It might be great to implement yolov9 as a plugin in target models
https://github.com/WongKinYiu/yolov9

Use case

A simple detection model, like the tutorial with milk bottles

Additional

No response

Are you willing to submit a PR?

  • Yes I'd like to help by submitting a PR!
@Youho99 Youho99 added the enhancement New feature or request label Mar 11, 2024
@capjamesg
Copy link
Member

Hello @Youho99! Thank you for creating this Issue. We would love to support YOLOv9 training in Autodistill. We would like to use the official repository, as we do in the Roboflow YOLOv9 training notebook. If you'd be interested in creating an Autodistill YOLOv9 repository, feel free to get started!

@Youho99
Copy link
Author

Youho99 commented Mar 11, 2024

The problem is that, apart from being constantly evolving, yolov9 is not packaged in pypi (and probably never will be)

I saw in your other implementations that you implemented the models present on pypi.

Do you have an idea on a development direction, for a non-packaged yolov9 model?

@capjamesg
Copy link
Member

Here is an example of how to train a YOLOv9 model using a fork of the official repository that includes a few fixes: https://github.com/roboflow/notebooks/blob/main/notebooks/train-yolov9-object-detection-on-custom-dataset.ipynb

In cases where software is not distributed as a Python package, we typically add code to manually install the package and ensure it is read from the right path when Autodistill runs. See https://github.com/autodistill/autodistill-detic/blob/main/autodistill_detic/detic_model.py#L72 as an example of how we do this. It isn't ideal, but it works.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
enhancement New feature or request
Projects
None yet
Development

No branches or pull requests

2 participants