Knowledge Base
Find success with our helpful guides and step-by-step instructions.
Hands-on tutorials
Launch projects successfully by following our step-by-step instructions. Use our datasets for practice projects, or run projects on your own datasets and adapt the settings to your needs. You can also run the projects programmatically using the Toloka-Kit.
Image classification
A multiple choice task. Examples are moderating content or grouping images by category.
Learn moreObject recognition and area selection (example with decomposition)
An example of dividing a task for selecting objects into three projects: filtering out inappropriate images, selecting objects, and reviewing assignments.
Learn moreSide-by-side
The annotator compares pairs of images, illustrations, or interfaces.
Learn moreVideo classification
A multiple choice task. For example, moderation of content, evaluation of video for noise and defects, or video classification by specified categories.
Learn more
Keys to clean and accurate training data
Our methodology based on years of research and unique industry expertise can help you successfully tap into the wisdom of the crowd on a large scale. If you want to efficiently use the knowledge of thousands of people to get clean and accurate data for your ML needs, follow our tips for each of these essential steps.
1. Decomposition
Break your task down into steps until each separate level is clear enough for any performer to handle.3. Interfaces
A good interface makes it easy for users to perform the same repeated actions quickly and correctly.4. Quality control
Carefully plan and configure a quality control system to ensure high-quality results.
Research Benchmarks
Use our examples as benchmarks to achieve the described levels of quality on popular research datasets.
Python library
We have an open-sourced library with a client that covers all API functionalities.Public datasets
Use our datasets for your projects or collect your own data that meets your needs.