Train a SegFormer on a Custom Dataset about notebooks HOT 11 CLOSED

This is because pytorch-lightning dependency is not stated, and running now will install '2.0.1.post0' which has breaking changes from 1.X. In 2.X lightning will discover available devices, so this arg is not needed.

On commenting out the GPU arg, you can run but get error:

NotImplementedError: Support for `validation_epoch_end` has been removed in v2.0.0. `SegformerFinetuner` implements
this method. You can use the `on_validation_epoch_end` hook instead. To access outputs, save them in-memory as 
instance attributes.

Note there is also an attribute error from feature_extractor.reduce_labels = False

Therefore this notebook needs updating for lightning 2.0

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Hi @jhayle21, @mazatov and @robmarkcole 👋🏻! I just pushed a fixed version of our SegFormer notebook. It is not ideal, but it works as expected. What I did was:

• pytorch-lightning<2.0.0 - fix the version of PyTorch Lightning to be the latest version below 2.0.0. In the future, we need to update API used in the notebook. But for now, it should be fine.
• allow for latest version of roboflow to enable new authentication API
• update transformers API reduce_labels -> do_reduce_labels

I'm closing the issue but feel free to reopen it in the future if needed.

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I've tried Yolov7/8 and they do alright. I was just hoping to get better results with segformer or mask2former.

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👋 Hello @jhayle21, thank you for leaving an issue on Roboflow Notebooks.

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@Jacobsolawetz could you take a look at this bug report?

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I had the same error just now. Were you able to figure it out, @jhayle21 ?

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@robmarkcole, Thank you 100% agree. It also applies to test_epoch_end ---> on_test_epoch_end

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Thanks @SkalskiP , I was able to run it. I wonder if you can provide some input on the outputs I'm getting. I'm training a model on soccernet dataset with segmented soccer pitch lines. The output of the model is a bit strange.

While it's obvious that it is learning something it seems to struggle to learn background class. I wonder if I have to do something special for that as in the dataset, background is the largest class. Currently it's labeled as 0, and has a special name in the class list.

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Hi, @mazatov 👋🏻!

This looks like a very interesting project. How large is the dataset? How long did you train?

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The main problem here is the fact that the objects you try to detect are thin. In the past, I trained two models like that. And I managed to do that with Mask R-CNN and YOLOv7.

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