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I’m trying to detect a single type of boxes from a camera image. Instead of using hand labelled images for training, I want to create the data from a 3D model using blender and a python script.
So far I successfully created a dataset and trained RetinaNet on it. I do apply some augmentation (color shifts, saturation changes, noise, blurring, sharpening).
The results on a validation set (consisting of synthetic data too) are great, but the localization performance on real images is way worse.
What changes should I make to my rendering process to match real images better?
Since it’s a virtual environment, I have pretty much unlimited control over everything, but I have no clue what makes sense to try varying. Some of the detections are flawless, but others are way off and I can’t tell what’s the visual difference that throws the network off.