Assignment #3 Image Segmentation

CSC420: Assignment #3

Introduction
This assignment aims to get you started on practical deep learning approaches to computer vision tasks.
Please read the info section below for some guidelines, requirements and suggestions about assignment 3.
Info:
• This assignment is likely to take more time than your first two assignments. Start early!
• It is recommended you use PyTorch or Tensorflow with a Keras back-end for this assignment.
• Include each step you take to solve the problems in your report, include your code in the report
along with input and outputs where needed.
• You are to implement the architectures and functions such as U-NET and data augmentation
from scratch and not use built-in models / libraries that do the job for you.
• Your final submission files should include a complete report, along with working code and saved
weights of your trained network for each step. We should be able to run your model, using your
weights on new input images without any error.
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1 Image Segmentation
In part one of this this assignment, you will familiarize yourself with implementing an image segmentation
architecture based on This paper by Ronnenberger et. al. You will work on the task of segmentation
and improving your model’s performance through different methods. You are given a set of cat images
and masks. All images should must be resized to 128 * 128 in your code. The goals is to improve the
performance of the model on the cat segmentation dataset. The challenge is the size of the dataset which is
quite small. Small amount of training data is a problem in deep learning tasks and in this assignment, you
will work towards practices that try to improve results of models that have access to only small datasets.
1.1 Implement U-NET - 2.5 Points
Read the paper by Ronnenberger and implement the architecture described in the paper to train for the
task of medical image segmentation. You should implement two different loss functions, compare the
performance of the two and rationalize why one might work better or worse than the other. (Both should
be reasonable functions for the task of segmentation). Randomly divide the cat images into (Train - 0.7)
and (Test - 0.3) – It is VERY IMPORTANT that you keep your train and test data separate and do not
train your model on your test data. Report the performance of your trained U-NET (trained on train
images) on the Test set images using Sørensen–Dice-coefficient. We will also test your model on cat
images that are not provided to you.
Hint*: Sørensen–Dice-coefficient could be useful as one of the loss functions you will implement
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1.2 Data Augmentation - 1.25 point
Data augmentation extends the size of your dataset by doing random flips, rotations, zooms, etc. They
can improve performance of the model in exchange for training time and resource allocation. Write a data
augmentation function that performs at least 4 different augmentation techniques on your input images.
Train and test your U-net again on the augmented training set. Report the performance (Dice score). for
images in the test set, include the image, ground truth masks and predicted masks of your model..
For all train - test instances, keep your test set the same for all steps after your first randomized
split into train and test sets so your comparisons between each steps makes sense
1.3 Transfer Learning - 2.75 point
Transfer learning is one of the most practical ways of dealing with small datasets, given we have access
to some other dataset. First, familiarize yourself with transfer learning. Here is a quick guide. In this
part, you will find an online dataset of images and corresponding masks of your own choosing. First,
you train your u-net on this new dataset. Then you perform the task of transfer learning to help your
cat segmentation model by using what was learned from the other dataset. Explain how you perform the
transfer learning and include your reasoning in your report for every step you choose to take in this part,
include your code and results of the model trained using transfer learning on your test dataset. Report
the performance. for images in the test set, include the image, ground truth masks and predicted masks
outputted by your model.
Do not use your augmentation function in this part
1.4 Visualizing segmentation predictions - 0.5 point
Here, you have to write code that takes in the test images of the cat pictures, along with their corresponding
binary masks predicted by your trained model and apply contours around the cat. The final result should
look something like the example below. Include your code and 3 samples from your test set. If you have
failed segmentation, include one as one of the 3 images.
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2 Bounding Box Design
This question is intended to give you end-to-end experience designing a deep learning computer vision solution
for an open-ended problem. There is no one correct solution, and you are encouraged to be as creative as you
like and use any (reasonable) libraries!
Given the following noisy image of a circle, we would like to find the best possible bounding polygon
for the circle. The metric for evaluation will be Intersection over Union (IOU). Examine the starter code
given in
main.py
for more information. Note: make sure you have a clear separation between train set and test set!
2.1 Problem definition - 0.5 point
What are the (input, output) pairs to your neural network? What is your loss function? Why have you
represented the problem this way? Comment: There are many ways to formulate the problem (some of which
might be better than others). It helps to rapidly prototype and try out different implementations!
2.2 Implementation - 2.5 points
Implement and build your network. It must use less than 200 000 training data points, and fewer than
2 million total parameters. Print out a visualization of your network, and explain your architecture. You
will receive most of the mark (2.0 out of 2.5) for achieving reasonable performance (mean IOU 0.5)
and explaining your architecture. A perfect mark on this section is reserved for submissions achieving
significantly better results. If you do not cleanly separate the training and test dataset you will receive
0 on this section.
2.3 IOU Optimization - 0.5 point
Try experimenting with optimizing IOU directly (that is, use IOU as your loss function). Write briefly about
what happens, and explain why these results occur.
2.4 Visualization and error analysis - 1 point
Visualize the images. Plot the predicted bounding circles over the images, along with the IOU for that
prediction. Investigate where your network makes mistakes and comment.
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3 Hot Dog or Not Hot Dog - 0.5 point
A friend says that they have built a binary classifier for hot dog identification using a deep convolutional
neural network architecture. You give them a picture and ask them to run the network on it. The model
outputs a single scalar, c, which is the result of running a final sigmoid layer on the outputs. Was the
picture a hot dog or not? Explain. Hint: it does not matter what the value of c is
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