CUlane: Half the input-size

[andy-96]

Hi!

To reduce training time as I only have access to a 1080 Ti, I have reduced the input image size using the resize function you provided in the Dataloader from (1664, 576) to (832, 256). During inference, it always returns that the "Lane too small" as the fitted spline has only less than 10 values. Now, I wonder, what h_samples = (589, 240) in get_lanes_culane really represents and how you got to this value?

I have set h_samples = (589 / 2, 240 / 2) to see what happens and now, I get splines with values around 15-25, so I'll probably set the threshold to 20 to see how the accuracy will be.

Or would you just scale the input back to its original, i.e. (832 * 2, 256 * 2) during inference and keep everything as it is?

Thanks in advance!

[arangesh]

We already downsample the input image by a factor of two (see here) so that input image size is (288, 832).

If you want to reduce the image size further, you will also have have to change a few other settings in the dataloader, specifically, here, here, here, and here.

For example, if you wanted to reduce the input image size by a factor of 4 instead of the default 2, you should make the following changes in the lines above:

self.input_size = (144, 416) # line 77
self.samp_factor = 4./self.output_scale # line 79
tf.GroupRandomScale(size=(0.25, 0.3), interpolation=(cv2.INTER_LINEAR, cv2.INTER_NEAREST)) # line 89
tf.GroupRandomScale(size=(0.25, 0.25), interpolation=(cv2.INTER_LINEAR, cv2.INTER_NEAREST)), # line 97

That being said, I would highly suggest you use one of the lighter models for training (for example ENet) instead of reducing the image size further as I anticipate this to affect the performance significantly.

[andy-96]

Sorry, I should've closed this issue as it is not relevant for me anymore. I have tried downsampling the image more and as you have predicted, the performance decreases significantly.
I have used the multi-GPU version suggested by @qinjian623 which improved training speed a lot!
Thanks for your reply!

[qinjian623]

Sorry, I should've closed this issue as it is not relevant for me anymore. I have tried downsampling the image more and as you have predicted, the performance decreases significantly.

I have used the multi-GPU version suggested by @qinjian623 which improved training speed a lot!

Thanks for your reply!

You don't need keep the ratio of image. Resolution like 512x512 may be a balance between speed and accuracy.