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Train RGB and Test BGR performs better than Train RGB and Test RGB

Guys I've got a weird issue.
I'm trying to solve a Salient Object Detection (SOD) problem, using Encoder+Decoder architecture.
The module's encoder is VGG16, which accepts RGB as input. Also, I'm using a pre-trained model, that's why I need to normalize the input by subtracting the mean through channels.

R_MEAN = 123.68 
G_MEAN = 116.78 
B_MEAN = 103.94 
CHANNEL_MEANS = [R_MEAN, G_MEAN, B_MEAN]

Everything looks fine, but when I test my trained model with BGR input (instead of RGB), it performs better (~1%-3%) by almost all metrics and visual comparison.

Do you have any explanations for this or any idea?

Deep LearningComputer Vision
vggsod
2 votesNN170.00
1 Answers
LP216.00
2

Are you sure your pre-trained network is for RGB? maybe the VGG was trained on BGR version.

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