Some of the input images is just annotated with bbox and some are annoted with bbox and mask So while training it throws error

[MadhuriPatil1694]

for example, there are two instances in an image, a person with polygon annotation, and a bicycle with bounding box annotation, because i care person's shape but don't care bicyle's, only care bicycle's location.
is it possible to train with images like this? if yes how to do that?

Originally posted by @mashuai191 in #1190 (comment)

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[MadhuriPatil1694]

@mashuai191

for example, there are two instances in an image, a person with polygon annotation, and a bicycle with bounding box annotation, because i care person's shape but don't care bicyle's, only care bicycle's location. is it possible to train with images like this? if yes how to do that?

Originally posted by @mashuai191 in #1190 (comment)
@mashuai191 were you able to solve this issue

[zhanghang1989]

By default Detectron2 doesn't support this use case. You may change the datamapper to support loading the data and also make changes to the mask_rcnn_loss

detectron2/detectron2/modeling/roi_heads/mask_head.py

Line 32 in cbbc1ce

def mask_rcnn_loss(pred_mask_logits: torch.Tensor, instances: List[Instances], vis_period: int = 0):

[mashuai191]

@MadhuriPatil1694 i tried to change but failed, so i annotated all type's objects finally.

[MadhuriPatil1694]

@MadhuriPatil1694 i tried to change but failed, so i annotated all type's objects finally.

@mashuai191 I converted the bbox coordinates to polygon coordinates, for the images with just bbox annotation and it worked.