Assignment 5 Semantic Segmentation


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ELEC/COMP 447/546
Assignment 5
Problem 1: Semantic Segmentation (7 points)
In this problem, you will train a simple semantic segmentation network. Recall that in
semantic segmentation, the algorithm must assign each pixel of an input image to one
of K object classes. We have provided you with a Colab notebook with skeleton code to
get you started.
We will use a portion of the CityScapes dataset for this problem, consisting of 2975
training images and 500 validation images. The second cell in the notebook will
automatically download the dataset into your local Colab environment.
Each image also comes with annotations for 34 object classes in the form of a
segmentation image (with suffix ‘labelIds.png’). The segmentation image contains
integer ids in [0, 33] indicating the class of each pixel. This page provides the mappings
from id to label name.
a. Fill in the init and forward functions for the Segmenter class, which will
implement your segmentation network. The network will be a convolutional
encoder-decoder. The encoder will consist of the first several ‘blocks’ of layers
extracted from the VGG16 network pretrained on ImageNet (the provided Colab
notebook extracts these layers for you). You must implement the decoder with
this form:
Layer Output channels for Conv
3 x 3 Conv + ReLU 64
Upsample (2 x 2) X
3 x 3 Conv + ReLU 64
Upsample (2 x 2) X
3 x 3 Conv + ReLU 64
Upsample (2 x 2) X
3 x 3 Conv n_classes (input to init)
Use PyTorch’s Upsample function. Remember that the size of the image should not
change after each Conv operation (add appropriate padding).
b. Train your model for 7 epochs using the nn.CrossEntropy loss function. Using the
GPU, this should take about 30 minutes.
c. Using the final model, report the average intersection-over-union (IoU) per class
on the validation set in a table. For more on IoU, see this page. Which class has
the best IoU, and which has the worst? Comment on why you think certain
classes have better accuracies than others, and what factors may cause those
d. For each of the following validation images, show three images side-by-side: the
image, the ground truth segmentation, and your predicted segmentation. The
segmentation images should be in color, with each class represented by a
different color.
i. frankfurt_000000_015389_leftImg8bit.jpg
ii. frankfurt_000001_057954_leftImg8bit.jpg
iii. lindau_000037_000019_leftImg8bit.jpg
iv. munster_000173_000019_leftImg8bit.jpg
e. Look at the lines of code for resizing the images and masks to 256 x 256. We use
bilinear interpolation when resizing the image, but nearest neighbor interpolation when
resizing the mask. Why do we not use bilinear interpolation for the mask?
f. Look at the __getitem__ function for the CityScapesDataset class and notice that we
apply a horizontal flip augmentation to the image and mask using a random number
generator. Why do we apply the flip in this way instead of simply adding
T.RandomHorizontalFlip to the sequence of transforms in im_transform and
mask_transform (similar to what you did in Homework 4)?
Submission Instructions
All code must be written using Google Colab (see course website). Every student must
submit a zip file for this assignment in Canvas with 2 items:
1. An organized report submitted as a PDF document. The report should contain all
image results (intermediate and final), and answer any questions asked in this
document. It should also contain any issues (problems encountered, surprises)
you may have found as you solved the problems. Please add a caption for
every image specifying what problem number it is addressing and what it is
showing. The heading of the PDF file should contain:
1. Your name and Net ID.
2. Names of anyone you collaborated with on this assignment.
3. A link to your Colab notebook (remember to change permissions on your
notebook to allow viewers).


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