r/computervision • u/sns13 • 11h ago

Help: Project YOLXO small/tiny - custom model training - false-positives - need advice

Hi,

I've been trying to train custom model (3 classes) for yolox small (416px) on my dataset and no matter what I try - I get too many false positives. Is there something very basic I'm missing? What could be the route to figure out why is so?

Trained with mixup, without mixup, with augmentations, little augmentations, added more backgrounds (even those that cause false positives), trained for 100 epochs, 300 epochs, exact official config used.

Dataset is of mostly coco images (person, truck/car-vehicle) and drones. Checked added images/bboxes/etc.

here's my dataset data
train (instances_train2017.json)

Images: 43803

Background images: 1985

Images per class:

drone: 7758

person: 24103

vehicle: 19772

val (instances_val2017.json)

Images: 4831

Background images: 192

Images per class:

drone: 1066

person: 2159

vehicle: 2431

Still can't figure out why it tends to give so much false positives (with quite high confidece of 80+).

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u/Jobemias 10h ago

Without extra context I'd recommend checking the object confidence, from my experience yolox has a tendency to throw high class confidence FPs with virtually zero object confidence

1

u/agju 10h ago

  • Are you using augmentations? Check them

  • Check labels, even if you think they are ok

  • Cherrypick a small dataset with perfect annotations, try to overfit it on training (95+ conf)

  • Check channels order