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Why Choose CVAT as Your Data Labeling Service

In the dynamic field of computer vision, the data annotation process is fraught with challenges starting with selecting the right approach: 

  • Outsource: When you are looking for the external team to annotate your data and often face issues with uncertain quality and the potential for scams, making it difficult to know whom to trust. Additionally, the quality of annotations from unknown providers frequently fails to meet expectations. It is also difficult to fix issues within annotations because third-party platforms are usually closed and require an expensive subscription for access.
  • Internal team: On the other hand, relying on an internal team can also pose problems: onboarding, preparing required infrastructure, training of your data annotation team, takes considerable time, and without proper expertise, the process can be inefficient and prone to errors. Many teams rely on the Computer Vision Annotation Tool (CVAT) as the tool of choice, but only a few know how to use the data annotation platform properly.

In both scenarios, there is the risk of missed deadlines, and these approaches can become costly without delivering guaranteed results. Labeling Service stands out as a highly recognized data annotation service. effectively addresses these issues, offering a reliable solution for your annotation tasks without the drawbacks typically associated with either of two approaches. By choosing, you benefit from the expertise of a team that has developed one of the most popular open-source data annotation platforms for computer vision domain and has over 10 years of experience in the field. 

This article answers four question:

  • Why is Labeling Service the best solution for your needs?
  • Short and simple description of Labeling Project Stages with real numbers and timelines.
  • What are the payment estimation models?
  • Next steps?

Why labeling services are available to everyone, whether you are a small team requiring a little assistance within limited resources or a large company with extensive data to annotate.

Below, we outline why stands out among other annotation and labeling services.

Securing Your Projects with Excellence

We proudly own and develop the, renowned as a leading data annotation platform in the computer vision field. Our modern and efficient tool supports all major data annotation scenarios and is compatible with a variety of data import/export formats.

Mature Team and Flawless Project Management team consists of seasoned professionals, each bringing years of expertise in data annotation to ensure that your projects are handled with the utmost proficiency and care. We prioritize direct communication, allowing you to engage with a dedicated manager for personalized service. 

Qualified Annotators

Our team consists of highly skilled annotators, trained and certified directly by us.They are distributed worldwide, and we select the best annotators from various countries, including Kenya, India, Vietnam, Pakistan, Nigeria, and others. With their extensive experience in data annotation, they handle your projects with expertise and precision, tailored to meet your specific needs and requirements.

Scalability of the Team

Our infrastructure allows us to rapidly expand our team of annotators to meet the demands of any project size.  Whether your project needs 5 or 200 annotators, we can adjust our team size to deliver high-quality results on time.

We are qualified to train new annotators, ensuring they meet standards of quality and precision. This flexible scalability means we can efficiently handle significant increases in workload, guaranteeing that we always deliver high-quality results within your project timelines, regardless of the project's scope.

High Quality of Annotations

At, we are committed to maintaining the highest quality of annotation across all projects. Our strict quality control measures ensure that every annotator achieves and upholds specified standards.

We use advanced tools and methodologies to deliver precise, accurate, and consistent data annotations. 

Automated QA

To ensure top quality in our labeling services, we use Automated QA (Quality Assurance). platform uses algorithms to check the annotated data automatically, comparing it against a set of correct answers (“honey pots”) to spot any errors quickly and evaluate annotation quality for a whole dataset statistically.

This method boosts the accuracy of data annotation, cuts down on time and costs for manual checks, and is especially useful for large projects where checking everything by hand isn't practical.

Commitment to Timeliness

At, we maintain high-quality standards and strict adherence to deadlines, which helps us manage urgent projects effectively. For perspective on our timelines: small projects usually take less than one month for annotation. For larger projects, we can adjust to requested deadlines by mobilizing a bigger team of annotators when necessary.

Stages of the Annotation Project

These stages represent a workflow designed to ensure high-quality results in data annotation projects, The workflow is based on the effective communication and collaboration between the customer and CVAT throughout the process.

Stage 1: Annotation Proof of Concept (PoC) 

From you: 

  • You can provide and sign an NDA with us, before we even start working, so your data and information is secure.
  • Provide samples of real data (50-100 images or 1-2 videos)
  • Provide initial specifications, and any additional useful information.

From us: 

  • Conduct precise PoC annotation
  • Clarify any corner cases
  • Provide accurate estimates of project costs and timelines
  • Present a formal proposal.

We are ready to launch a Proof of Concept (PoC) within one day after receiving the data and can provide an accurate estimate and calculations within 3-5 days, depending on the project. Typically, the final budget deviates from the initial estimate by no more than 10% in either direction.

Stage 2: Documentation & Preparation

From you:

  • Correct and approve the Statement of Work (SoW)
  • Send the data.

From us: 

  • Prepare the final SoW
  • Finalize all payment terms and annotation requirements
  • Calculate and agree about quality metrics
  • Assign and train the Data Annotation (DA) team.

It will take up to one week to process documents from our side, assuming there are no delays from your side. For urgent projects, we can begin training the team and annotating data at this stage, without waiting for the completion of bureaucratic procedures.

Stage 3: Annotation

From you:

  •  Address any concerns and communicate with a dedicated manager for any questions.

From us: 

  • Perform the annotation in accordance with the approved specifications and deadlines 
  • Provide intermediate reports through a dedicated manager.

Most projects are completed within one month.

Stage 4: Validation

From you:

  •  Check the provided data, collect comments to fix issues, and review provided metrics.

From us:

  • Conduct manual and cross Quality Assurance (QA) via tools, automate QA for Ground Truth (GT) annotation covering 3-5% of the dataset
  • Make any final corrections for free and deliver the final quality report.
  • Calculate metrics such as Accuracy, Precision, Recall, Dice coefficient, Intersection over Union (IoU) and others and provide a confusion matrix report.

From our side, we will conduct the final validation and provide a final report within one week.

Stage 5: Acceptance

From you:

  • Accept the annotations and reports
  • Make payments (for large projects, payments are preferred in multiple batches after completing each batch).
  • Leave us a feedback about labeling service

Payment estimation models

Here is a detailed description of various estimation and payment models for labeling services, elaborating on the methods and conditions:

Different Estimation and Payment Models:

  • Per Object (the main model): Billing is based on each unit of data annotated, such as per annotated frame, object, or attribute within an image or video. It's most effective for projects with well-defined unit sizes and quantities.
  • Per Image/Video: Billing is based on each image or video file processed. This model is straightforward and suitable for projects where the complexity or time required per image/video is relatively uniform.
  • Per Hour: Billing is based on the amount of time annotators spend on the project. This method is flexible and can adapt to varying project complexities and unexpected changes in scope.

Expected Project Budget Limits:

  • Start from $5K - $9.9K for Annotation Only, Manual and Cross Validation: This budget range is typically for projects focusing solely on manual annotation services, including detailed cross-validation to ensure accuracy and consistency.
  • >$10K for Comprehensive Services Including AI Engineer Engagement, Automated QA: Projects exceeding $10K not only involve basic annotation but also include the engagement of AI engineers who contribute to more complex tasks such as setting up automated quality assurance processes and potentially developing custom AI solutions.


  • 5-30% Depending on Data Volume: We are always open to offering significant discounts to both new and loyal customers, as we are committed to fostering long-term collaborations.

These payment models offer flexibility to accommodate a wide range of projects, from straightforward image annotation to complex projects involving advanced AI technologies and extensive quality assurance. The pricing structure and discounts incentivize larger and longer-term engagements by providing cost benefits as project scopes increase.

Next steps?

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May 16, 2024
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