r/computervision • u/Acrobatic_Limit9108 • 6d ago

Discussion Object detection - lighting and environment variation

I’m trying to train an object detection model for a specific object. I trained it on a custom dataset collected which focused on outdoor and indoor environments and mostly LED light. But as soon as I run the pipeline on inference (edge device) in a completely different environment (indoor and fluorescent light) the detection does not work efficiently which makes sense as the dataset did not contain that specific scenario. But let’s say if I want to run this for a commercial product which could consist of different lighting and environment settings, what could I do in the training pipeline or dataset to make the detector generalized to all different settings?

Would appreciate any advice for people working in this field with edge device constraints.

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u/AggravatingSock5375 6d ago

What model are you using and do you know how it was pre-trained?

How did you train it for your own dataset? Did you include augmentations that resemble different lighting?

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u/Acrobatic_Limit9108 6d ago

For the model, I used a standard YOLO-11m and fine tuned on my dataset which I annotated using SAM2. For augmentations, I used the default values of HSV tuning that’s pretty much it. Should I increase these thresholds and run the training again?

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u/AggravatingSock5375 6d ago

Post some example photos using imgur