Label.ai is an application for companies and researchers in the field of machine learning classification. This project allows companies to validate their classification dataset by having a large number of people label image datasets. The process is simple:

1. Companies will provide Label.ai with labelled images
2. Label.ai will outsource to individuals to verify whether each image actually contains the given labels.
3. Individuals will be compensated respective to how well/how much they label

For example, consider an image with a cat that looks very similar to a dog. If this image was classified by some sort of AI/algorithm (especially the AI is not optimized), it would be likely that this image will be misclassified. Through the procedure above, Label.ai will ensure that the proper classification is provided to the image

The main motivation for creating our platform, Label.ai, is because machine learning and many other techniques in artificial intelligence require large amounts of data, and thus datasets are considered the new gold. The issue is that massive datasets with accurate labelling are extremely time consuming to create and often, datasets are not perfectly annotated. Label.ai aims to connect researchers/companies with independent labelling contractors who will label/verify the data/images for them. We believe that the existence of such a platform comes with many benefits. On one hand, it will improve the quality of datasets for companies who are in the AI / ML field. Moreover, it provides a secondary form of income for individual labellers with minimal training required.

Our application is very simple to use. After starting up the 3 instances (postgresql, frontend, backend), open the frontend interface. The frontend has 2 main dashboards. The main dashboard will allow users to label images (the main functionality of labelai). The other dashboard is a dashboard that shows the different reports (AllConfirmed, MisClassified, Discovered, Controversial, NoVotes). This option is only available to admin users.

• Node.js >= 14
• PSQL
• All packages inside backend/requirements/base.txt
  • python -m pip install backend/requirements/base.txt

1. Open the terminal application on your mac.

2. Install Homebrew. This helps with installing and managing applications on MacOS. Simply run:

   /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

3. Update Homebrew and install postgresql by running:

   brew update
   brew install postgresql

4. Check to make sure you have PostgreSQL by checking its version:

   postgres --version
   # This must print something like:
   # > postgres (PostgreSQL) 12.2

5. Now you can initialize the physical space on your hard-disk to allocate databases. To do this, create a default postgres database on the command line. Run:

   initdb /usr/local/var/postgres
   # You will/might see "initdb: directory "/usr/local/var/postgres" exists but is not empty"
   # this means the database was already created and you are fine for this step

6. Start the database by running:

   pg_ctl -D /usr/local/var/postgres start

1. Create a virtualenv:

   python3.9 -m venv venv

2. Activate the virtualenv you have just created:

   source venv/bin/activate

   Windows

   ./venv/Scripts/activate.bat

3. install requirements:

   pip install -r requirements/local.txt

4. Create a db:

   createdb label_ai -U postgres --password <password>

   Windows

   createdb -U postgres label_ai

5. Set the environment variables for your database:

   export DATABASE_URL=postgres://postgres:<password>@127.0.0.1:5432/label_ai

   Windows

   set DATABASE_URL=postgres://[email protected]:5432/label_ai

6. Apply migrations:

   python manage.py migrate

7. Run the server:

   python manage.py runserver 0.0.0.0:8000

8. Visit 0.0.0.0:8000 and enjoy! The 2 functionalities currently supported are:

   • List all images: 0.0.0.0:8000/images/
   • List all labels: 0.0.0.0:8000/labels/

backend/label_ai/labels/views.py:

1. List all labels

   lists all the labels in the Label table http://localhost:8000/labels/all

backend/label_ai/classifications/views.py:

2. List all classifications

   lists all the classifications in the Classification table http://localhost:8000//all

backend/label_ai/members/views.py:

3. List all members

   Lists all the members in the Member table http://localhost:8000/members/all

backend/label_ai/submissions/views.py:

4. List all submissions

   Lists all the submissions in the Submission table http://localhost:8000/submissions/all

5. Create Submission

   Creates a submission entry in the Submission table http://localhost:8000/submissions/insert (requires the fields of the submission object in the post call)

queries/create_all.sql:

6. Calculation of confidence for classifications (View + Joins)

   For each classification c, calculate the lower bound of the Wilson Confidence interval for ‘votes’ in submission corresponding to ‘c’, using user trust as a weighted vote.

   Confidence is represented in ClassificationView which is the endpoint for our application to make classification queries.

backend/label_ai/images/views.py:

7. List all images:

   Lists all the images in the Image table http://localhost:8000/images/all

8. ImagesByLabelView

   Returns a list of images by a label id. Will only return the images where the confidence / total count matches a certain threshold. http://localhost:8000/images/confirmed?label_id=<label_id>

9. MislabelledImagesView

   Queries all images we believe have one or more classifications with confidence < 0.05 http://localhost:8000/images/mislabelled?count=

10. GetPromptImageClassification

    Queries 1 random,unlabelled image classification prompt that http://localhost:8000/images/prompt

1. Get random classification prompts Parameters for number of prompts and the user User must not have classified previously Prompts must be unconfirmed (actually need classification)

2. Get random classification prompts by label Same 1. With label parameter

3. Submit a user classification - submission Parameters for user, classification and their response How to handle non-unique case? Triggers?

4. Get all images we are confident fall under a label

5. Get all the images with confident classification which were not previously classified

6. Get all the misclassified images which were previously classified

7. Calculate confidence in classifications

Here is the relational schema of our backend architecture for those interested!