This was my attempt to create an image segmentation model using
Berkeley's DeepDrive dataset. A more complete writeup documenting the
journey can be found in my medium post.https://medium.com/p/308f8c44305a/edit
To recreate my results you'll need your Linux distro of choice, PyTorch v1 and Python 3.6 or later.
$ conda install -c pytorch -c fastai fastai
From there Berkeley DeepDrive v2.ipynb should run. v1 was created
using an earlier version of FastAI and did not successfully segment.
Included
label_quantify.py
Was used to determine how many categories there were. Companion to test_label_quantify.json
seg_128 folder
A 128x128 bordered version of the segmentation dataset used to train
ResNet34. Could be used as a quick reference to try deeper ResNet or
other pretrained models.