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Training Data provides a full range of services for working with images to create high-quality training datasets.

Image annotation in machine learning

Image annotation in data training services is a crucial process in the field of machine learning, particularly in computer vision. It involves the manual or automated addition of metadata or labels to images to make them understandable and usable by AI algorithms.

The annotated images serve as training data for machine learning models, allowing them to learn and make predictions or classifications based on the provided annotations. This data plays a significant role in various applications, including object detection, image classification, semantic segmentation, and more.

Types of Image Annotation Services



In bounding box annotation, annotators draw rectangles (bounding boxes) around objects or regions of interest within an image. This is commonly used for object detection and localization tasks

Polygon Annotation

Polygon annotation involves outlining objects or areas using complex shapes like polygons or freeform curves. It's useful when objects have irregular shapes

Semantic Segmentation

Semantic segmentation annotates each pixel in an image with a class label, allowing models to understand object boundaries and segment images into meaningful regions

3-D Lidar

For 3D object detection or reconstruction tasks, annotators label 3D points, volumes, or bounding boxes in a 3D space to provide depth and positional information


Key point annotation involves annotating specific points or landmarks on objects within an image. This is used for tasks like pose estimation or facial landmark detection


A set of connected points forming a continuous contour with coordinates for describing curves or boundaries of objects, as well as marking the path of movement

3D cuboids

Rectangular volumes used for marking objects in three-dimensional space and with information about the size, position and orientation of the object

Image Classification

Annotators assign a single class label to an entire image. It's commonly used for categorizing images into predefined classes or categories


Creating a mask or masking area around an object in an image or video for video editing, special effects, AR applications

Instance Segmentation

Instance segmentation not only labels pixels by class but also assigns a unique identifier to each object instance within a class. It's helpful for distinguishing between multiple instances of the same object in an image

Text Annotation

Text annotation involves identifying and transcribing text in images, which is essential for tasks like optical character recognition (OCR) or document processing

Landmark Annotation

Landmark annotation is used for identifying specific points of interest within an image, often for facial recognition, gesture detection, or human pose estimation

Time Series Annotation

Time series annotations are made on sequences of images or videos to track objects or phenomena over time, such as in video object tracking or motion analysis

Data Augmentation

Data augmentation services involve creating augmented versions of images by applying various transformations like rotation, scaling, and noise addition. These augmented images are used to increase the diversity of training data

How we Deliver Image Annotation Projects

Our comprehensive and structured approach ensures that your image annotation projects are executed seamlessly, meeting your specific requirements and objectives

Project Consultation and Planning

/ 01
We begin every project with a thorough consultation to understand your needs and goals. During this stage, we work closely with you to define project scope, annotation types (e.g., bounding boxes, segmentation masks, keypoints), and volume requirements.

Annotation Guidelines and Specifications

/ 02
Our experienced team collaborates with your experts to create detailed guidelines and specifications. These guidelines serve as the foundation for the annotation process, ensuring consistency and accuracy throughout the project

Data Preparation and Sharing

/ 03
You provide us with your raw image data, which we securely handle and prepare for annotation. Data privacy and confidentiality are our top priorities, and we take every measure to protect your sensitive information during this stage

Annotation Execution

/ 04
Our skilled annotators, trained according to your guidelines, perform the annotation tasks with precision. We use industry-leading tools and software to ensure efficient and accurate results

Quality Control and Review

/ 05
Quality control is a cornerstone of our process. Our dedicated quality assurance team conducts thorough reviews to verify the accuracy and consistency of annotations. Any identified errors are promptly corrected to meet the highest quality standards

Data Management and Security

/ 06
We maintain rigorous data management practices, including secure storage and transfer, adhering to strict security protocols and compliance with privacy regulations

Progress Tracking and Reporting

/ 07
Throughout the project, we provide regular progress reports, keeping you informed about milestones and timelines. We value open communication and address any project-related issues promptly to ensure a smooth workflow

Data Validation and Verification

/ 08
To guarantee the reliability of your annotated dataset, we conduct validation checks, verifying the quality and accuracy of annotations through random sampling and cross-checks

Data Delivery

/ 09
Once the annotation process is complete, we deliver the annotated dataset to you in the agreed-upon format, ensuring it aligns perfectly with your project requirements


/ 10
We maintain comprehensive documentation of the entire project, including guidelines, communication records, and quality control reports. After successful project closure, we seek your feedback to continually improve our services

Data Annotation Piloting

/ 11
This phase typically occurs before the full-scale implementation of annotated datasets. The primary purpose of the piloting stage is to test and refine the annotation processes, tools, and workflows before committing to a large-scale annotation effort

Image Annotation Use Cases  

Healthcare and Medical Imaging

Companies in healthcare use image annotation to label medical images, such as X-rays, MRIs, and CT scans, for disease detection, organ segmentation, and tumor localization. This aids in diagnosis and treatment planning

Automotive and Autonomous Vehicles

In the automotive industry, image annotation is essential for autonomous vehicle development. Companies annotate images to identify and classify objects, detect road signs, and ensure safe navigation

Retail and E-commerce

E-commerce companies use image annotation to categorize and tag products in their catalogs. This enables visual search, recommendation systems, and efficient inventory management

Agriculture and Precision Farming

Precision agriculture companies use image annotation to identify crop health, detect pests and diseases, and optimize irrigation and fertilization based on aerial and satellite imagery

Manufacturing and Quality Control

Manufacturers annotate images to inspect products for defects, monitor production lines, and ensure quality control. This reduces manufacturing errors and improves product quality

Security and Surveillance

Security companies use image annotation for facial recognition, object detection, and event analysis in surveillance cameras to enhance security and threat detection

Geospatial and Environmental Analysis

In geospatial applications, image annotation is used for land cover mapping, urban planning, and monitoring environmental changes through satellite and aerial imagery

Entertainment and Gaming

Companies in the entertainment and gaming industry use image annotation for character recognition, motion capture, and gesture recognition, enhancing immersive experiences

Real Estate and Property Management

Real estate companies annotate images to identify property features, estimate property values, and create virtual property tours for listings

Food and Agriculture Inspection

Food processing companies employ image annotation to inspect food products for quality, defects, and contamination, ensuring food safety

Wildlife Monitoring and Conservation

Organizations involved in wildlife conservation use image annotation to track and monitor wildlife populations, identify species, and study animal behavior

Energy and Infrastructure

Energy companies use image annotation to inspect infrastructure assets like power lines, pipelines, and wind turbines, detecting damage and optimizing maintenance

Sports Analytics

Sports teams and organizations use image annotation to analyze player movements, ball trajectories, and game statistics for performance analysis and coaching

Document Processing and OCR

Companies in document management use text annotation to recognize handwritten or printed text in documents, enabling optical character recognition (OCR) and data extraction

Fashion and Apparel

Fashion companies use training data outsourcing to tag clothing items, styles, and accessories for visual search and personalized recommendations

Artificial Intelligence Research and Development

In the field of AI research, image annotation is fundamental for training and evaluating machine learning models, advancing the development of AI technologies

Stages of work

  • Application

    Leave a request on the website for a free consultation with an expert. Th e acco unt manager will guide you on the services, timelines, and price
  • Free pilot

    We will conduct a test pilot project for you and provide a golden set, based on which we will determine the final technical requirements and approve project metrics
  • Agreement

    We prepare a contract and all necessary documentation upon the request of your accountants and lawyers
  • Workflow customization

    We form a pool of suitable tools and assign an experienced manager who will be in touch with you regarding all project details
  • Quality control

    Data uploads for verification are done iteratively, allowing your team to review and approve collected/annotated data
  • Post-payment

    You pay for the work after receiving the data in agreed quality and quantity


  • 24 hours
  • 24 hours
  • 1 to 3 days
  • 1 to 5 days
    Conducting a pilot
  • 1 day to several years
    Carrying out work on the project
  • 1 to 5 days
    Quality control
You pay for the work after you have received the data
in the established quality and quantity

Training Data

  • Quality Assurance:
  • Enhanced Data Accuracy
  • Consistency in Labels
  • Reliable Ground Truth
  • Mitigation of Annotation Biases
  • Cost and Time Efficiency
  • Data Security and Confidentiality:
  • GDPR Compliance
  • Non-disclosure agreement
  • Data Encryption
  • Multiple data storage options
  • Access Controls and Authentication
  • Expert Team:
  • 6 years in industry
  • 35 top project managers
  • 40+ languages
  • 100+ countries
  • 250k+ assessors
  • Flexible and Scalable Solutions:
  • 24/7 availability of customer service
  • 100% post payment
  • $550 minimum check
  • Variable Workload
  • Customized Solutions

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