Need help to do inference for gray scale image

[mkothule]

Want to run the network for gray scale images (single channel)

I get this error while running network on gray images Traceback (most recent call last): File "demo_imgs.py", line 100, in demo(args) File "demo_imgs.py", line 50, in demo image1 = load_image(imfile1) File "demo_imgs.py", line 29, in load_image img = torch.from_numpy(img).permute(2, 0, 1).float() RuntimeError: number of dims don't match in permute

I tried copying same gray values for all 3 channel but results are not very good.

I see eth3d is gray scale image dataset so I also tried with eth3d network shared. But I still get above error.

Can you please share what change is needed to adapt network to gray images?

[gangweiX]

can you give me your gray images?

[mkothule]

Currently I am using KITTIT images (RGB2Gray converted) for experimentation.

[gangweiX]

You can use KITTI pretrained model, that will perform well.

[mkothule]

thanks gangweiX. I see sensible output with kitti2015 pre-trained network for above images.