RGB Input

[YiMingDiuRen]

Hello,
Suppose I have datasets with RGB channels, should I use the 3d models? I simply changed in_chns to 3 and feed data with shape (bs, 3, 256, 256) but it would give errors, for both unet_3d and attention unet:
RuntimeError: Given groups=1, weight of size [16, 3, 3, 3, 3], expected input[32, 1, 9, 256, 256] to have 3 channels, but got 1 channels instead

This error came from conv1 = self.conv1(inputs)
Is there anything that I missed? It would be greatly appreciated if you could provide training/testing scripts for RGB images since they are more usual.
Thank you very much.

[Xiangde Luo]

Hi,
Firstly, you should use the 2D network to segment your RGB images rather than 3D networks, like here using in_chns=3 to replace in_chns=1 is enough. Secondly, we do not plan to provide training/testing scripts for RGB images, as there is no difference between Gray images and RGB images except the input channels. Finally, do not forget to star or cite our repository/papers if it is useful for your research or study :)
Best,
Xiangde.