[SOLVED] does is affect NN training accuracy if the color format of images is BGR not RGB?

does is affect NN training accuracy if the color format of images is BGR not RGB?

I'm training Neural Network with ImageNet dataset and I noticed that images are in BGR color format when I read them using OpenCV function cv2.imread(), so does is affect training accuracy?, if yes then how can I change it to RGB in pytorch?

Solution

It will not affect your NN's accuracy, in general. However, if you are using a pre-trained CNN, then it likely expects RGB images as input, and will not do as well on BGR images initially and will have to re-learn its weights for BGR.

You can convert BGR to RGB using cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB).

You can also consider the following alternatives for reading images:

torchvision.io.read_image(path) (https://pytorch.org/vision/stable/io.html#image)
torch.from_numpy(np.array(PIL.Image.open(path)))
torchvision.transforms.functional.pil_to_tensor(PIL.Image.open(path)) (https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.functional.pil_to_tensor)