Multi label training without overlap

Hello,

I succeeded to train a multi class model when I had one annotation per image.
I found out I have more than one class for some of the images so I added annotations with a unique id and same image_id that reflect multiple classes bounding boxes in the same image. There are no overlaps.

The training fails now due to some exceeding index isuue.

I think this problem comes from the fact that my data originally was organised as:

root folder
--> category 1 folder
--> category 2 folder
--> category 3 folder

but actually now images from category 3 folder has bounding boxes of category 2 in the annotation file.

Does yolox supports that ?
If yes, what have I missed ? and how can I debug that ? Thanks!

The report provides no file, test, or traceback to identify the failing path. Start by reproducing training with multiple non-overlapping class annotations for one image, then inspect the resulting index error and dataset annotation handling; done means the supported data format is confirmed or the failure is isolated with a reproducible case.