Projects

We are committed to teaching you the practical skills needed to successfully complete any computer vision project you might encounter professionally. This usually implies being able to work with data collection and annotations as much as training machine learning models. While the latter is discussed a lot, the former is rarely mentioned in courses. Therefore, we decided to design projects that emphasise data processing skills as much as the ability to build powerful computer vision systems.

You can get up to 40 points for the project + bonuses.

Project key milestones:

1. Fill in the profile assessment questionnaire that will help us to assign you into teams. Please, make sure to complete the survey until 15.02.2026.
2. The team formation deadline is Feb 22 (23:59, Sunday)
3. The dataset preparation deadline is March 22 (23:59, Sunday). You should upload a short 2-page report to the courses homeworks page.
4. The Kaggle setup deadline is April 12 (23:59, Sunday). You should send a link to your Kaggle page by the deadline in slack to the instructors.
5. Participate in other teams’ Kaggles and try to improve upon their benchmarks and beat other teams. The deadline is May 17 (23:59, Sunday). Upload a short 2-page report to the https://courses.cs.ut.ee/2026/DL4CV/spring/Main/Homeworks page summarising your performance.
6. Present your overall experience during the final class on May 27th.
7. Fill out the team’s self-assessment questionnaire to estimate each member’s contribution by May 29 (Friday, 23:59).

What to do?

There are a number of milestones to deliver for the project, but worry not, we will guide you through them gradually, and it all should start making sense.

1. Profile assessment (Feb 15 @ 23:59)

Please complete the this profile questionnaire (already closed) to help us form the initial teams. This is a very important step, because without it, you cannot get a team, and without a team, you cannot complete the project.

2. Team formation (Feb 22 @ 23:59)

Objective: You will be assigned to a team by the course instructors. All teams will be notified via Slack. Establish a connection with your team via Slack.

Tasks:

• You have been automatically a team based on your responses to the profile assessment questionnaire.
• All you have to do is connect with your team, for example, by creating a separate channel in Slack.
• Ideally, meet, get to know each other, and decide on the roles inside your team.

You will find the team assignment in the following document: link

Deliverable: nothing to deliver for this milestone. It, therefore, gives 0 points out of 40.

3. Dataset preparation (March 22 @ 23:59)

Objective: Walk in the shoes of Fei-Fei Li (the creator of ImageNet). Substantially modify existing or create a new computer vision dataset.

Tasks:

• Decide on the type of computer vision problem (classification, object detection, or segmentation) and gather relevant data. If you go for object detection or segmentation, your dataset may be smaller than for classification.
• Ensure your dataset is balanced and diverse.
• Preprocess the data (filtering out poor quality data, resizing, etc.). Leave normalisation and augmentation to the teams who will work with your competition.
• If this is a novel dataset, label the data accurately. Document the labeling procedure.
• Split the dataset into training and test subsets.

Deliverable: Submit a 2-page report (with an Appendix that includes images) describing the dataset you have created, motivation, sources, preprocessing techniques, labeling procedure, and any challenges faced. Please clearly indicate whether the dataset is new or a modification of an existing dataset. If it is a modification, how was it modified? If it is a new dataset, how was it collected and labeled? Please, make it clear what kind of CV problem this dataset is built around (classification, segmentation, or object detection). Describe train and test splits. Add examples of images and labels to your report. Provide relevant summary statistics. Upload this report to the course's homework page by March 22 (23:59, Sunday). This part is worth 10 out of 40 points.

Bonus points. Teams can get:

• up to three bonus points (depending on the quality of the dataset) if the team creates a completely new dataset rather than reusing the existing one. Keep in mind that the new dataset should be large enough to train deep learning models.
• up to a bonus point if the team creates a dataset around object detection, and up to two points if it is a segmentation problem.

The above bonus points are additive.

4. Kaggle setup (April 12 @ 23:59)

Objective: Create a Kaggle competition on the basis of the prepared dataset.

Tasks:

• Create a new Machine Learning competition via https://www.kaggle.com/competitions/new (make it public)
• Upload your dataset to Kaggle.
• Define clear competition rules, evaluation criteria, and splits between public and private leaderboards. Please, make 10 daily submissions and 2 best private entries as defaults.
• Create a small starter notebook that would serve as a starting point for other teams. This notebook should contain code for generating a random submission file as well as some basic exploratory data analysis for your data.
• Set the start and end dates to April 13 and May 17 (midnight), 2026, respectively.
• Establish a strong baseline by training a model as a benchmark - other teams will try to beat your baseline.

Where to get help: check out these guidelines from Kaggle to learn more about hosting your own competition on Kaggle.

Deliverable: Set up your Kaggle competition page with all necessary details and share the link with the instructors by April 12 (23:59, Sunday). Send a link to your Kaggle page by the deadline in slack to the instructors. This part is worth 10 out of 40 points.

5. Participation in Kaggle competitions (May 17 @ 23:59)

Objective: Participate in at least 4 other teams' competitions, aiming to improve upon the benchmarks.

Tasks:

• Choose to participate in at least four competitions. We will select those four for each team separately.
• Analyze each competition's problem and dataset.
• Develop and train models to solve the given problems.
• Submit your solutions to the competitions and iterate to improve your results.

Where to get help: Do not stress too much about your team’s final performance, instead, try to practice as many as possible concepts and models that we have studied in the course. If you manage to beat a few benchmarks on the way - the better 🙂

Deliverable: Submit a 2-page report summarizing your approaches, methodologies, and performance in the competitions. Upload this report to the course's homework page by May 17 (23:59, Sunday). This part is worth 10 out of 40 points.

Bonus points At this stage, teams can get:

• One bonus point for every benchmark (private leaderboard) surpassed;
• Two bonus points if your own benchmark was not surpassed;

6. Final presentations (during the final class on May 27th)

Objective: Share your overall experience, learnings, and achievements in the course project via a presentation.

Tasks:

• Prepare a 10-minute presentation summarizing your journey through the project phases.
• Highlight key learnings, challenges faced, and how your team overcame them.
• Share insights from both setting up and participating in competitions.

Deliverable: deliver a presentation on May 27th, during the final class. This part gives the final 10 points out of 40.

Please, add your slides to this folder before the event: https://drive.google.com/drive/folders/1byAEzGqXyx_5Eh425PiXOunkCtf6E2zQ.

7. Team self-assessment (by May 29 @ 23:59)

Objective: so far, you have been graded as a team. Now, this is the time to talk about individual contributions.

Task: Fill out a questionnaire assessing your teammates’ involvement in the project's overall success.

Deliverable: Filled out Google form with questions by May 29 (23:59, Friday)

Additional Guidelines

• Employ the best project management practices: make a plan of what should be done and when; assign individual team members to each task; set up weekly meetings to review the progress and solve problems.
• Ethical Considerations: When creating new datasets, ensure ethical data collection practices, especially regarding privacy and consent.

This project is designed to enhance your technical skills in machine learning and computer vision and develop your abilities in managing and processing data effectively – a key skill in any data-driven field. Remember, the quality of your data and your understanding of the problem are just as important as the sophistication of your models.