Course Project

Update: Spring Quarter 2017 projects have been posted!


The Course Project is an opportunity for you to apply what you have learned in class to a problem of your interest. There are two project options you can pick from:

Option 1: Your own project (Encouraged)

Your are encouraged to select a topic and work on your own project. Potential projects usually fall into these two tracks:

One restriction to note is that this is a Computer Vision class, so your project should involve pixels of visual data in some form somewhere. E.g. a pure NLP project is not a good choice, even if your approach involves ConvNets.

To inspire ideas, you might look at recent deep learning publications from top-tier vision conferences, as well as other resources below.

For applications, this type of projects would involve careful data preparation, an appropriate loss function, details of training and cross-validation and good test set evaluations and model comparisons. Don't be afraid to think outside of the box. Some successful examples can be found below:

ConvNets also run in real time on mobile phones and Raspberry Pi's - feel free to go the embedded way. You may find DeepBeliefSDK helpful. This particular project might be slightly out of date, but it may help you find more like it.

For models, ConvNets have been successfully used in a variety of computer vision tasks. This type of projects would involve understanding the state-of-the-art vision models, and building new models or improving existing models for a vision task. The list below presents some papers on recent advances of ConvNets in the computer vision community.

You are welcome to come to our office hours to brainstorm and suggest your project ideas. We also provide a list of popular computer vision datasets:

Option 2: Tiny ImageNet Challenge

If you are unable to come up with a project idea, you can fall back to working on the Tiny ImageNet Challenge which we will run similar to the ImageNet challenge. The goal of the challenge will be for you to do as well as possible on the Image Classification problem. You will submit your final predictions on a test set to our evaluation server and we will maintain a class leaderboard.

Important Dates

Unless otherwise noted, all project items are due by 11:59 pm Pacific Time.

Grading Policy

  Final Project: 40%
  milestone: 5%
  write-up: 10%
   •  clarity, structure, language, references: 3%
   •  background literature survey, good understanding of the problem: 3%
   •  good insights and discussions of methodology, analysis, results, etc.: 4%
  technical: 12%
   •  correctness: 4%
   •  depth: 4%
   •  innovation: 4%
  evaluation and results: 10%
   •  sound evaluation metric: 3%
   •  thoroughness in analysis and experimentation: 3%
   •  results and performance: 4%
  poster: 3% (+2% bonus for best few posters)

Project Proposal

The project proposal should be one paragraph (200-400 words). If you work on your own project, your proposal should contain:

If you choose to work on Tiny ImageNet Challenge, emphasize the last three bullet points on the list above.

Submission: One member on your team should submit your project proposal using the form provided on Piazza. If you previously submitted your proposal via email, please resubmit using the form.

Project Milestone

Your project milestone report should be between 2 - 3 pages using the provided template. The following is a suggested structure for your report:

Submission: Please upload a PDF file to the assignments tab on Canvas. Please have one person on your team submit your milestone. If you submit your milestone late, all team members will be charged late days.

Poster Session

We will hold a poster session in which you will present the results of your projects is form of a poster.
Students: We will provide foam poster boards and easels. Please print your poster on a 20 inch by 30 inch poster in either landscape or portrait format. Posters larger than 24 inch by 36 inches may not fit on our poster boards. All students are required to submit a PDF of their poster on Canvas before the event. See Piazza for details. Caution: Do not wait until Tuesday (or even Monday) to print your poster. Many on-campus printers (e.g., EE, BioX) run out of paper or toner during the last week of classes. Many other courses also have poster presentations or academic conferences take place during this week and there is no guarantee they will be able to rush-print your order.

Frequently Asked Questions

Final Submission

Your final write-up is required to be between 6 - 8 pages using the provided template. Please use this template so we can fairly judge all student projects without worrying about altered font sizes, margins, etc. After the class, we will post all the final reports online so that you can read about each others' work. If you do not want your writeup to be posted online, then please let us know at least a week in advance of the final writeup submission deadline.

Submit your final submission through Canvas. You will submit one or two files:
  1. A PDF file of your final report
  2. (OPTIONAL) zip file (or pdf file) with Supplementary Materials

Report. The following is a suggested structure for the report:

We have listed additional guidelines on Piazza to help you structure your report.

Additional Submission Requirements
New for 2017, we have additional metadata requirements.

In summary, include all contributing authors in your PDF; include detailed non-231N co-author information in the Canvas comments box; tell us if you submitted to a conference, cite any code you used, and submit your dual-project report (e.g., CS 231A, CS 234).

Supplementary Material is not counted toward your 6-8 page limit.
Examples of things to put in your supplementary material: Examples of things to not put in your supplementary material:

Example Project Reports

Your project reports should structure like a computer vision conference paper (CVPR, ECCV, ICCV, etc.). You can find publications from Stanford Vision Lab from here. In addition, you may also take a look at some previous projects from other Stanford CS classes, such as CS221, CS229 and CS224W
Projects from previous years
You can see project reports from previous years here:

Collaboration Policy

You can work in teams of up to 3 people. We do expect that projects done with 3 people have more impressive writeup and results than projects done with 2 people. To get a sense for the scope and expectations for 2-people projects have a look at project reports from previous years.

Honor Code

You may consult any papers, books, online references, or publicly available implementations for ideas and code that you may want to incorporate into your strategy or algorithm, so long as you clearly cite your sources in your code and your writeup. However, under no circumstances may you look at another group’s code or incorporate their code into your project.

If you are doing a similar project for another class, you must make this clear and write down the exact portion of the project that is being counted for CS231n.