In this assignment, you will first learn how to use PyTorch on Google Colab environment. You will then
practice putting together a simple image classification pipeline, based on the k-Nearest Neighbor, and
finally will learn how to use Autograder for evaluating what you implement. The goals of this assignment
are as follows:
Develop proficiency with PyTorch tensors
Gain experience using notebooks on Google Colab
Understand the basic Image Classification pipeline and the data-driven approach (train/predict
Understand the train/val/test splits and the use of validation data for hyperparameter tuning
Implement and apply a k-Nearest Neighbor (kNN) classifier
Learn how to test your implementation on Autograder
This assignment is due on Friday, September 11, at 11:59 PM EDT.
Q1: PyTorch 101 (60 points)
The notebook pytorch101.ipynb will walk you through the basics of working with tensors in PyTorch. You
are required to write code on pytorch101.py.
Q2: k-Nearest Neighbor classifier (40 points)
The notebook knn.ipynb will walk you through implementing a kNN classifier. Your implementation will go
1. Download the zipped assignment file
Click here to download the starter code
2. Unzip all and open the Colab file from the Drive
Once you unzip the downloaded content, please upload the folder to your Google Drive. Then, open each
*.ipynb notebook file with Google Colab by right-clicking the *.ipynb file. No installation or setup is
required! For more information on using Colab, please see our Colab tutorial.
3. Open your corresponding *.py from Google Colab and work on the
Next, we recommend editing your *.py file on Google Colab, set the ipython notebook and the code side
by side. Work through the notebook, executing cells and implementing the codes in the *.py file as
indicated. You can save your work, both *.ipynb and *.py, in Google Drive (click “File” -> “Save”) and
resume later if you don’t want to complete it all at once.
While working on the assignment, keep the following in mind:
The notebook and the python file have clearly marked blocks where you are expected to write code.
Do not write or modify any code outside of these blocks.
Do not add or delete cells from the notebook. You may add new cells to perform scratch
computations, but you should delete them before submitting your work.
EECS 498-007 / 598-005
Deep Learning for Computer Vision
p , y g y
Run all cells, and do not clear out the outputs, before submitting. You will only get credit for code that
has been run.
4. Evaluate your implementation on Autograder
Once you want to evaluate your implementation, please submit the *.py and *.ipynb files to
Autograder for grading your implementations in the middle or after implementing everything. You can
partially grade some of the files in the middle, but please make sure that this also reduces the daily
submission quota. Please check our Autograder tutorial for details.
5. Download .zip file
Once you have completed a notebook, download the completed uniqueid_umid_A1.zip file, which is
generated from your last cell of the knn.ipynb file.
Make sure your downloaded zip file includes your most up-to-date edits; the zip file should include
pytorch101.ipynb, knn.ipynb, pytorch101.py, knn.py for this assignment.
6. Submit your python and ipython notebook files to Autograder
When you are done, please upload your work to Autograder (UMich enrolled students only). Your ipynb
files SHOULD include all the outputs.
Website for UMich EECS course
EECS 498-007 / 598-005: Deep Learning for Computer Vision