Week 3 - Car Detection Anchor Boxes

[ai_curious]

@Pixies

that clearly is not the format that the loss function needed, so i think that there could be a function or something that convert that format

The ground truth bounding box labels are created completely independent of what algorithms and loss functions will be applied to them later. So you will likely need to do some work for whatever application you are using. For YOLO, first you have to compute the optimal number of and shape of the anchor boxes. Then, you have to compute where in the image the center of the ground truth label is, what grid cell it will fall into, and which anchor box has the highest IOU. You also need to convert the coordinates, and assign them to the correct grid cell + anchor box ‘detector’ location. I think I put a code fragment showing these steps into older threads you can find with search. Below are 2 that might be useful. HTH

Deriving YOLO anchor boxes

Deriving Anchor Boxes for YOLO One area of recurring questions about YOLO is anchor boxes, introduced with the second 2016 paper, YOLO9000 aka YOLO v2, as an optimization to help improve model stability during training. I’ll defer discussion of the role they play for now and just cover how they are created. Once you see what they are, I think it will be easier to understand how they are used. The Dataset I worked with the Berkeley DeepDrive 100K dataset (BDD). To keep the size manageable, I u…

Applying YOLO anchor boxes

Applying YOLO Anchor Boxes A previous thread provides an introduction to extracting from labelled ground truth data a small set, on the order of 3 <= k <= 10 , of shapes for what is referred to in the YOLO 9000 paper as dimension clusters or priors, but in this class as anchor boxes . NOTE: Just to make things more confusing, the YOLO 9000 paper uses anchor boxes to refer instead to the hand-picked shapes described in the Faster R-CNN paper, not the ones generated by K-Means. The YOLO 9000 pap…