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[P] How can I make my rendered training data match real data better?

[P] How can I make my rendered training data match real data better?

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.

An example for a rendered image (training set)

Excellent results on validation set (halfway hidden boxes are supposed to be not detected)

Localization problems on real images

submitted by /u/Single_Blueberry
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.