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Nikita Manovich edited this page Feb 5, 2024 · 25 revisions

CVAT Google Summer of Code 2024

GSoC 2024 Homepage

CVAT accepted projects

Timeline

Date Description Comment
February 6, 2024 Mentoring organization application deadline 👍

Resources

CVAT project ideas list

Mailing list to discuss: cvat-gsoc-2024 mailing list

Index to Ideas Below

  1. Load and visualize 16-bit medical images
  2. Keyboard shortcuts customization
  3. Quality control: consensus
  4. Quality control: honeypot
  5. Internationalization and localization

Idea Template

All work is in Python and TypeScript unless otherwise noted.

Ideas

  1. IDEA: Load and visualize 16-bit medical images

    • Description: All digital projection X-ray in DICOM is more than 8 bits and hence encoded in two bytes, even if not all 16 bits are used. Right now CVAT converts 16-bit images into 8-bit. For medical images it leads to losing important information and it isn't possible to annotate such data efficiently. A doctor should adjust the contract of some regions manually to annotate such visual data.
    • Expected Outcomes:
      • Upload digital projection X-ray in DICOM and convert it to 16-bit PNG.
      • Visualize 16-bit PNG image in the browser using WebGL.
      • Implement brightness, inverting, contract, saturation using WebGL.
      • Import/Export datasets in CVAT format.
      • Add functional tests and documentation
    • Resources:
    • Skills Required: Python, TypeScript, WebGL
    • Possible Mentors: Boris Sekachev
    • Difficulty: Hard

  2. IDEA: Keyboard shortcuts customization

    • Description: In many case to have good data annotation speed users need to use mouse, keyboard, and other input devices effectively. One way is to customize keyboard shortcuts and adapt them for a specific use case. For example, if you have several labels in your task, it can be important to assign a shortcut for each label and use them to switch quickly between them and annotate faster. Other users want to lock/unlock an object quickly.
    • Expected Outcomes:
      • It should be possible to configure shortcuts in settings and save them per user.
      • Add functional tests and documentation
    • Resources:
    • Skills Required: TypeScript, React
    • Possible Mentors: Maria Khrustaleva
    • Difficulty: Medium

  3. IDEA: Quality control: consensus

    • Description: If you use crowd to annotate an image, the easiest way to get high quality annotations for a task is to annotate the same image multiple times. After that you can compare labels from multiple annotators to produce high-quality results. Let's say you try to estimate age of people. The task is very subjective. An averaged answer from multiple annotators can help you predict more precise age for a person.
    • Expected Outcomes:
      • It should be possible to create multiple jobs for the same segment of images (https://github.com/opencv/cvat/issues/125)
      • Support a number of built-in algorithms to merge annotations for a segment: voting, averaging, raw (put all annotations as is)
      • Add functional tests and documentation
    • Resources:
    • Skills Required: Python, Django
    • Possible Mentors: Maxim Zhiltsov
    • Difficulty: Medium

  4. IDEA: Quality control: honeypot

    • Description: Data quality is the crucial property of any dataset. It is important to understand how well your data is annotated. One way is to have some pre-labeled data and compare provided annotations with known annotations. You can have 1% of annotated data only (ground truth) and use statistics to predict how well all images in your dataset are annotated.
    • Expected Outcomes:
      • It should be possible to create hidden jobs in your tasks and upload ground truth annotations into the job.
      • Support a number of built-in algorithms to score the provided annotations.
      • Add functional tests and documentation
    • Resources:
    • Skills Required: Python, Django
    • Possible Mentors: Maxim Zhiltsov
    • Difficulty: Medium

  5. IDEA: Internationalization and localization

    • Description: Typical users of CVAT are data annotators from different countries without good knowledge of English. It is very difficult for them to work with a tool which cannot show them messages, hints on their native language. The goal of internationalization and localization is to allow a single web application to offer its content in languages and formats tailored to the audience.
    • Expected Outcomes:
      • CVAT supports one more language. It should be easy to add a new language for a non-technical person.
      • It should be possible to choose a language in UI (e.g., en/fr).
      • Add functional tests and documentation
    • Resources:
    • Skills Required: Python, TypeScript
    • Possible Mentors: Andrey Zhavoronkov
    • Difficulty: Hard

Idea Template

1. #### _IDEA:_ <Descriptive Title>
   * ***Description:*** 3-7 sentences describing the task
   * ***Expected Outcomes:***
      * < Short bullet list describing what is to be accomplished >
      * <i.e. create a new module called "bla bla">
      * < Has method to accomplish X >
      * <...>
   * ***Resources:***
         * [For example a paper citation](https://arxiv.org/pdf/1802.08091.pdf)
         * [For example an existing feature request](https://github.com/opencv/cvat/pull/5608)
         * [Possibly an existing related module](https://github.com/opencv/cvat/tree/develop/cvat/apps/opencv) that includes OpenCV JavaScript library.
   * ***Skills Required:*** < for example mastery plus experience coding in Python, college course work in vision that covers AI topics, python. Best if you have also worked with deep neural networks. >
   * ***Possible Mentors:*** < your name goes here >
   * ***Difficulty:*** <Easy, Medium, Hard>

Potential mentors list

Nikita Manovich
Boris Sekachev
Maxim Zhiltsov
Roman Donchenko
Andrey Zhavoronkov

Admins

Nikita Manovich
Boris Sekachev
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