Error when training custom detection: bounding box values should be in the range 0 - 1

[toolhater]

I'm trying to use the something similar to the example that was given for training a model for custom image detection. I annotate the files in Label Studio and export in yolo format. If I just have one class I dont have a problem. However when I add more than one class, even after update the object_names_array, I get the error in the subject line.

Before we get too far into the weeds I should mention that some of the images I have have more than one class inside them. So if I open the annotation for an image I might see something like this:

1 0.5884861407249463 0.6983273596176822 0.823027718550106 0.4074074074074073
0 0.5127931769722814 0.5173237753882914 0.9744136460554371 0.9653524492234169

The first number is the I think represents a mapping to the classes. Indeed, there is a notes.json file that has this info:

{
"categories": [
{
"id": 0,
"name": "dogs"
},
{
"id": 1,
"name": "cats"
},
{
"id": 2,
"name": "birds"
}
],
"info": {
"year": 2023,
"version": "1.0",
"contributor": "Label Studio"
}
}

Should I be trying to export this in another format or does imageAI only expect one class per annotation file? Also ran a loop and read all the annotations and didn't find one value in the box, not the class, that had a value less than 0 or greater than 1.

Any help would be appreciated.

[toolhater]

I've found the error. After taking out some of the code and running it against the list of items I was trying to load, I found two items that had values =1. In those cases, when I looked at the labeling, the line was exactly on the end of the image, Once I closed the rectangle a tad the problem was fixed.

Looping through the folder where my annotations were I used this code:

l = np.loadtxt(f'{directory}{file_name}').reshape(-1, 5)

#     try:
#         assert (l[:, 1:] <= 1).all(), f"bounding box values should be in the range 0 - 1 {file_name}"
#     except AssertionError as ex:
#         print(file_name)
#         print(l)