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yet-another-anime-segmenter's Issues

How to delete the text boxes of output image?

Hi, thanks for your work and I want to know how to delete the text boxes of output image.
I find that there is a text boxed on the leftup of image with info(tensor(0) 80%), how to delete it?Thanks!

how to "transfer learn"?

Hi, your work is great and very interesting.
I'm wondering if I could segment only head and hairs of the anime character.
I want to train with my own dataset, but can it be realized by doing the way this link says? https://github.com/aim-uofa/AdelaiDet/tree/master/configs/SOLOv2#installation--quick-start
Or did you build your own model and trained with pretrained models? If so, can you teach me further details how to do these things?
I just started studying deeplearnings, so I apolgize if this is a dumb question.

AssertionError

assert torch.__version__.startswith("1.7")
!pip install detectron2 -f
https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.7/index.html
clear_output()


AssertionError Traceback (most recent call last)
in
3 # See https://detectron2.readthedocs.io/tutorials/install.html for instructions if you
4 # want to switch to an older version
----> 5 assert torch.version.startswith("1.7")
6 get_ipython().system('pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.7/index.html')
7 clear_output()

AssertionError:

I am getting this error in that code. What should i do to solve this problem?
I ran this in colab.

No object named 'SOLOv2' found in 'META_ARCH' registry!

Hey, I'm kinda new to ai. I'm trying to use YAAS in my personal project and I'm stuck on this error. Do you have any idea what am I doing wrong?

class AnimeSegmentator:
    def __init__(self):
        config_path = "./YAAS/SOLOv2.yaml"
        model_path = "./YAAS/SOLOv2.pth"
        cfg = get_cfg()
        cfg.merge_from_file(config_path)
        cfg.MODEL.WEIGHTS = model_path
        cfg.MODEL.DEVICE = "cpu" #torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
        cfg.freeze()
        self.model = DefaultPredictor(cfg)
        self.model.score_threshold = 0.1
        self.model.mask_threshold = 0.5
    
    def run(self, image):
        preds = self.model(image)
        print(preds)
        return image

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