The bounding box's position and size is incorrect, how to improve it's accuracy?
I'm using detectron2 for solving a segmentation task,
I'm trying to classify an object into 4 classes,
so I have used COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml.
I have applied 4 kind of augmentation transforms and after training I get about 0.1
total loss.
But for some reason the accuracy of the bbox is not great on some images on the test set,
the bbox is drawn either larger or smaller or doesn't cover the whole object.
Moreover sometimes the predictor draws few bboxes, it assumes there are few different objects although there is only a single object.
Are there any suggestions how to improve it's accuracy?
Are there any good practice approaches how to resolve this issue?
Any suggestion or reference material will be helpful.

I would suggest the following:

Ensure that your training set has the object you want to detect in all sizes: in this way, the network learns that the size of the object can be different and less prone to overfitting (detector could assume your object should be only big for example).
Add data. Rather than applying all types of augmentations, try adding much more data. The phenomenon of detecting different objects although there is only one object leads me to believe that your network does not generalize well. Personally I would opt for at least 500 annotations per class.

The biggest step towards improvement will be achieved by means of (2).
Once you have a decent baseline, you could also experiment with augmentations.
