Impact of Grayscale Inference on RGB-trained YOLOv8 (QR-Like object detection): Is it better to train on Grayscale directly?

[Toxite]

@Riccardo_Carraro

• Theoretical Impact: Does passing a “fake RGB” image (3 identical grayscale channels) to a standard RGB-trained YOLO model degrade its feature extraction capabilities in theory? Or does YOLO naturally filters out color information when detecting geometric shapes, focusing mostly on luminance gradients?

YOLO does what it learnt to do during training. There’s nothing fixed that it does. It all depends on what it learnt.

@Riccardo_Carraro

• Training Strategy: Given that my object is inherently monochromatic (black patterns on white paper) and color in the environment acts mostly as noise (mud, grass):
• Should I align my training pipeline to convert all synthetic images to Grayscale (+CLAHE) before training?
• Or is it better to keep training on RGB (to leverage pre-trained COCO weights better) and only use Grayscale as a domain adaptation trick during inference?

You should train the model on images that are closer to what it looks like during inference. If inference is on grayscale, you can train a single channel model. The pretrained weights would largely not be affected.

COCO8-Grayscale Dataset