Fractal

Tutorial: Get a labeled dataset
for object detection

Ready-to-use workflow to get custom human-labeled datasets for training object detection models

Run Toloka's object detection pipeline to get a human-labeled dataset from your images.
No need to set up the image labeling process yourself.
  • Upload your unlabeled image files to any cloud storage
  • Sign up for Toloka as a requester
  • Run the Python code from our Jupyter Notebook tutorial
  • Collect labeled images with super fast turnaround and reliable quality
  • To try it all for free, use our promo code
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Perfect
for any object
detection task

Our tutorial shows how to apply the pipeline to your dataset, using the face recognition task as an example.

  • Retail and e-commerce
    • Product recognition on store shelves
    • Virtual fitting rooms
    • People counting for retail stores
  • Transportation
    • Pedestrian detection
    • Traffic prediction
    • Parking occupancy detection
    • Road condition monitoring
  • ManufacturingPersonal protective equipment detection
  • Biometrics
    • Facial feature detection
    • Iris recognition
  • Agriculture
    • Plant disease detection
    • Object detection in agriculture
  • MarketingLogo recognition

Benefits

  • Get high-quality labeled data
    Toloka provides multiple tools to ensure quality of data labeling (overlap, control tasks, and more). The pipeline comes with these
    tools already set up, and includes a stage dedicated specifically
    to the results verification.
  • Cut time to production
    You don't need to set up the labeling process from scratch.
    Just use the pipeline and get ground truth data to
    improve your ML model.
  • Automate the process
    The pipeline contains Python code which automates the labeling process via the Toloka API. You can use it as-is for face detection
    tasks. If you need to detect any other objects, we will show you
    where and how to tune it to fit your task.
  • Fine-tune to fit your needs
    Our pipeline gives you a quick start, but you can still access all project settings. Tweak it as much and as often as you need to. Our support team can help you along the way.

Success stories

For more than 10 years, Toloka's clients have been successfully solving business challenges. Here are some examples:

Face detection

A startup from Japan labeled 65,000 faces
in 3 weeks, at a fraction of the expected cost.

Learn more

Surrounding objects detection

A self-driving car project uses Toloka to label tens of thousands of mages for training a neural network to detect surrounding objects in an urban environment.

Learn more

License plate detection

Toloka's object detection template has been successfully used to create training datasets for license plate detection models for a parking management system.

Learn more
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Ready to try it out?

Follow our tutorial on GitHub and run the pipeline 
to collect data for your object detection model.
Open on GitHub
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Need help? Our experts are happy to answer your questions about the pipelines and using Toloka.
Get expert help

 FAQ