Understanding Repulsion Loss in Object Detection

In this code snippet, the 'if fg_mask.sum():' condition checks if there are any foreground pixels in the mask. If there are, the target bounding boxes ('target_bboxes') are divided by a tensor ('stride_tensor'). Then, the 'repulsion_loss' function is called with various parameters ('pred_bboxes', 'target_bboxes', 'fg_mask', 'sigma_repgt', 'sigma_repbox', 'pnms', 'gtnms') to calculate the loss values for the bounding boxes.

After calculating the loss values, they are multiplied by different gain values ('self.hyp.box', 'self.hyp.cls', 'self.hyp.dfl') to adjust their contribution to the overall loss. The adjusted loss values are then stored in the 'loss' list at index 0, 1, and 2 respectively.