Berkeley DeepDrive Practice


#1

Hello all, to practice my skills with part 2 I wanted to attempt a U-Net on Berkeley’s DeepDrive dataset. However, I am getting very bad results with a mask accuracy of 13%! One of the concerns I had was that when I tried to overlay the training images and masks they weren’t matching. Could that be why the accuracy is so low?

Link to notebook:

Link to data:


(Kerem Turgutlu) #2

It’s almost always necessary to do pre and post image plotting for sanity checks while working with vision problems. If your ground truth is somehow incorrect you would get bad segmentations in my opinion.

By looking at your notebook I assume you are using fastai v 0.7, I would suggest you to use v1.0.

Here is the notebook for camvid (SOTA results), an almost identical task to yours.


#3

Oh wow I didn’t know this existed! This looks very promising! Do you know if there’s a video associated with this notebook? I’ve been trying to implement a Mask R-CNN but I’m excited to know that there’s a FastAI notebook I can work off of.

I will take your recommendations as well regarding regarding 1.0. Thanks again for pointing me in the right direction!