| # Current algorithm | |
| ## Choice of mask objects | |
| For identification of the objects which are suitable for mask obtaining, panoptic segmentation model | |
| from [detectron2](https://github.com/facebookresearch/detectron2) trained on COCO. Categories of the detected instances | |
| belong either to "stuff" or "things" types. We consider that instances of objects should have category belong | |
| to "things". Besides, we set upper bound on area which is taken by the object — we consider that too big | |
| area indicates either of the instance being a background or a main object which should not be removed. | |
| ## Choice of position for mask | |
| We consider that input image has size 2^n x 2^m. We downsample it using | |
| [COUNTLESS](https://github.com/william-silversmith/countless) algorithm so the width is equal to | |
| 64 = 2^8 = 2^{downsample_levels}. | |
| ### Augmentation | |
| There are several parameters for augmentation: | |
| - Scaling factor. We limit scaling to the case when a mask after scaling with pivot point in its center fits inside the | |
| image completely. | |
| - | |
| ### Shift | |
| ## Select | |