yolov5 anchors

YOLOv5 uses anchor boxes to improve object detection accuracy. Anchor boxes are predefined bounding boxes of various sizes and aspect ratios that are placed over the image at different positions. These anchor boxes act as reference points for the network to predict the final bounding boxes for the detected objects.

YOLOv5 uses a predefined set of anchor boxes based on the dataset being used. The anchor boxes are calculated using the k-means clustering algorithm on the ground-truth bounding boxes of the training dataset. The number of anchor boxes is usually set to be the same as the number of classes to be detected.

During training, the network learns to adjust the anchor boxes to better fit the objects in the image. This helps to improve the accuracy of the object detection.