In this assignment you will practice putting together a simple image classification pipeline, based on the k-Nearest Neighbor or the SVM/Softmax classifier. The goals of this assignment are as follows:

  • understand the basic Image Classification pipeline and the data-driven approach (train/predict stages)
  • understand the train/val/test splits and the use of validation data for hyperparameter tuning.
  • develop proficiency in writing efficient vectorized code with numpy
  • implement and apply a k-Nearest Neighbor (kNN) classifier
  • implement and apply a Multiclass Support Vector Machine (SVM) classifier
  • implement and apply a Softmax classifier
  • implement and apply a Two layer neural network classifier
  • understand the differences and tradeoffs between these classifiers
  • get a basic understanding of performance improvements from using higher-level representations than raw pixels (e.g. color histograms, Histogram of Gradient (HOG) features)

Setup

Get the code as a zip file here.

You can follow the setup instructions here.

Download data:

Once you have the starter code (regardless of which method you choose above), you will need to download the CIFAR-10 dataset. Run the following from the assignment1 directory:

cd cs682/datasets
./get_datasets.sh

Start IPython:

After you have the CIFAR-10 data, you should start the IPython notebook server from the assignment1 directory, with the jupyter notebook command.

If you are unfamiliar with IPython, you can also refer to our IPython tutorial.

Some Notes

NOTE 1: This year, the assignment1 code has been tested to be compatible with python version 3.6 (it may work with other versions of 3.x, but we won’t be officially supporting them). You will need to make sure that during your virtual environment setup that the correct version of python is used. You can confirm your python version by (1) activating your virtualenv and (2) running which python.

NOTE 2: If you are working in a virtual environment on OSX, you may potentially encounter errors with matplotlib due to the issues described here. In our testing, it seems that this issue is no longer present with the most recent version of matplotlib, but if you do end up running into this issue you may have to use the start_ipython_osx.sh script from the assignment1 directory (instead of jupyter notebook above) to launch your IPython notebook server. Note that you may have to modify some variables within the script to match your version of python/installation directory. The script assumes that your virtual environment is named .env.

Q1: k-Nearest Neighbor classifier (20 points)

The IPython Notebook knn.ipynb will walk you through implementing the kNN classifier.

Q2: Training a Support Vector Machine (25 points)

The IPython Notebook svm.ipynb will walk you through implementing the SVM classifier.

Q3: Implement a Softmax classifier (20 points)

The IPython Notebook softmax.ipynb will walk you through implementing the Softmax classifier.

Q4: Two-Layer Neural Network (25 points)

The IPython Notebook two_layer_net.ipynb will walk you through the implementation of a two-layer neural network classifier.

Q5: Higher Level Representations: Image Features (10 points)

The IPython Notebook features.ipynb will walk you through this exercise, in which you will examine the improvements gained by using higher-level representations as opposed to using raw pixel values.

Submitting your work

There are two steps to submitting your assignment:

1. Submit a pdf of the completed iPython notebooks to Gradescope. If you are enrolled in the course, then you should have already been automatically added to the course on Gradescope.

To produce a pdf of your work, you can first convert each of the .ipynb files to HTML. To do this, simply run

ipython nbconvert --to html FILE.ipynb

for each of the notebooks, where FILE.ipynb is the notebook you want to convert. Then you can convert the HTML files to PDFs with your favorite web browser, and then concatenate them all together in your favorite PDF viewer/editor. Submit this final PDF on Gradescope, and be sure to tag the questions correctly!

Important: Please make sure that the submitted notebooks have been run and the cell outputs are visible.

2. Submit a zip file of your assignment to Gradescope. To do this, run the provided collectSubmission.sh script, which will produce a file called assignment1.zip.