Error Ensemble

[ccimarp]

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Question

When I tried to do the same example of the Ensemble tutorial, I had this error:

Traceback (most recent call last):
File "val.py", line 398, in
main(opt)
File "val.py", line 371, in main
run(**vars(opt))
File "/usr/local/lib/python3.7/dist-packages/torch/autograd/grad_mode.py", line 28, in decorate_context
return func(*args, **kwargs)
File "val.py", line 166, in run
ncm = model.model.yaml['nc']
File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1178, in getattr
type(self).name, name))
AttributeError: 'Ensemble' object has no attribute 'yaml'

Additional

No response

[github-actions]

👋 Hello @ccimarp, 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.

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[glenn-jocher]

@ccimarp 👋 hi, thanks for letting us know about this possible problem with YOLOv5 🚀. Ensembling works correctly:

We've created a few short guidelines below to help users provide what we need in order to start investigating a possible problem.

How to create a Minimal, Reproducible Example

When asking a question, people will be better able to provide help if you provide code that they can easily understand and use to reproduce the problem. This is referred to by community members as creating a minimum reproducible example. Your code that reproduces the problem should be:

• ✅ Minimal – Use as little code as possible to produce the problem
• ✅ Complete – Provide all parts someone else needs to reproduce the problem
• ✅ Reproducible – Test the code you're about to provide to make sure it reproduces the problem

For Ultralytics to provide assistance your code should also be:

• ✅ Current – Verify that your code is up-to-date with GitHub master, and if necessary git pull or git clone a new copy to ensure your problem has not already been solved in master.
• ✅ Unmodified – Your problem must be reproducible using official YOLOv5 code without changes. Ultralytics does not provide support for custom code ⚠️.

If you believe your problem meets all the above criteria, please close this issue and raise a new one using the 🐛 Bug Report template with a minimum reproducible example to help us better understand and diagnose your problem.

Thank you! 😃

[Audrey528]

@ccimarp Have you solved this problem?
@glenn-jocher How can I make Ensembling works like this: python val.py --weights yolov5n.pt yolov5s.pt --data ../coco.yaml?

[glenn-jocher]

@Audrey528 good news 😃! Your original issue may now be fixed ✅ in PR #7490. We found a bug in Ensemble validation when checking that Ensembled model class counts match dataset class counts. This should now be resolved:

To receive this update:

• Git – git pull from within your yolov5/ directory or git clone https://github.com/ultralytics/yolov5 again
• PyTorch Hub – Force-reload model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True)
• Notebooks – View updated notebooks
• Docker – sudo docker pull ultralytics/yolov5:latest to update your image

Thank you for spotting this issue and informing us of the problem. Please let us know if this update resolves the issue for you, and feel free to inform us of any other issues you discover or feature requests that come to mind. Happy trainings with YOLOv5 🚀!

[Audrey528]

@glenn-jocher Amazing! It works! Thanks for your excellent work.