Bounding boxes on images during yolo training goes all over the place

[AScythe]

I used roboflow to annotate my images, see example below:

then i download my annotated image dataset from roboflow.

when i train yolo using the the dataset, i check the training and validation images after training, and i saw that the bounding boxes go all over the place, see example below:

i checked the bounding boxes on the dataset that i downloaded to see whether they are on correct places, and it seems they are… but when i run the training, the training shows me that the bounding boxes are not in the right place… and this makes the trained model detect wrongly

if someone knows why this bug is happening and how to solve it, i will be very grateful…

[Toxite]

What format did you use for download?

[BurhanQ]

Looking at your example annotation image, there are annotations that are not boxes.

These would not be supported for a detection model, as they have more than four corners.