I am currently working with Ultralytics Yolov8 model for object detection. I trained my model with a custom dataset. I annotated my dataset on Roboflow and exported in Yolov8 format. The training result looks good. However there is a mismatch between the coordinates of bounding boxes of my manual annotation on Roboflow and that of prediction.
For example I have this image called image_1.jpg which is annotated on Roboflow and is part of training set. Roboflow defined the coordinates (as the part of the label):
[0 0.36953125 0.39609375 0.54765625 0.7875]
where the last 4 numbers showing the x1,x2,y1,y2 coordinates of the bounding boxes. However when I passed this image into predict method (just for checking) using the following piece of code
model = YOLO(‘best_weights.pt’)
result = model.predict(path_of_example_image, save = True)
boxes = result[0].boxes.xyxy
I got the coordinates:
tensor([[ 22.5558, 9.7429, 619.7601, 345.7614]])
When I draw the bounding box with the predicted coordinates, the area covers the object completely (with .99% accuracy on the (correctly) predicted class). On the other hand, when I draw the bounding box with the coordinates from manual annotation, the object is not covered at all as, obviously, all coordinates are below 1. So I just wonder what kind of preprocessing is applied within the model so that the coordinates from Roboflow's labelling make sense and my training result is actually good?
Yolov8 tensor can output normalised values too. FYI, xyxy is not yolo format, you would need xywh instead. Just change this line:
boxes = result[0].boxes.xyxy
to:
boxes = result[0].boxes.xywh