Finetuning capabilities?

[th0mas-codes]

Is it possible to finetune this model based on a custom dataset if we meet the COCO panoptic segmentation format standard ?
What I am hoping to achieve is being able to train on a set of very domain specific data I have.

I am currently trying to set up a Colab Notebook where I can try this out.
I have managed to sort my data correctly and generated the following segmentation.png files but I am running into some problems when trying to run the model and i'm unsure where to define the checkpoint .pt provided as a base for the finetuning if so.

I have followed the steps provided in the readme with the setup.
I have gotten so far that when I try and start the training i get this error:

Traceback (most recent call last): File "/content/OpenSeeD/train_net.py", line 466, in <module> launch( File "/usr/local/lib/python3.10/dist-packages/detectron2/engine/launch.py", line 82, in launch main_func(*args) File "/content/OpenSeeD/train_net.py", line 442, in main trainer._trainer.model.module = trainer._trainer.model.module.from_pretrained(cfg.MODEL.WEIGHTS) File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1269, in __getattr__ raise AttributeError("'{}' object has no attribute '{}'".format( AttributeError: 'BaseModel' object has no attribute 'module'. Did you mean: 'modules'?

Not sure where to go from here or if what I am trying to do is even possible. Any help or guidance would be very much appreciated!
Really interesting to see what you guys have achieved with this model, great work!