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IMAGE LABELING SERVICES

Training Data offers Image Labeling Services, providing accurate and comprehensive labeling of images to enhance object recognition, classification, and analysis for various industries and applications. Our expert annotators meticulously annotate images with descriptive labels, bounding boxes, and semantic segmentation masks, ensuring high-quality data for training machine learning models and powering computer vision applications.

What is Image Labeling?

Image labeling in data training services involves the process of annotating images with descriptive metadata, such as class labels, bounding boxes, or segmentation masks, to identify and localize objects or features of interest within the images. This annotated data serves as ground truth for training machine learning models to recognize and understand visual content in applications like object detection, image classification, and semantic segmentation.

Types of Image Labeling Services

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Bounding Box Annotation

Bounding box annotation entails drawing rectangles or bounding boxes around objects of interest within images. This type of annotation provides spatial localization information for objects, enabling tasks such as object detection, tracking, and localization in computer vision applications.
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Polygon Annotation

Polygon annotation entails outlining the contours or boundaries of objects with polygonal shapes within images. This annotation type is useful for capturing the precise shape and outline of irregularly shaped objects, facilitating tasks such as semantic segmentation and instance segmentation.
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Semantic Segmentation

Semantic segmentation annotation entails labeling each pixel in an image with a corresponding class label to segment the image into distinct semantic regions. This type of annotation enables pixel-level understanding of image content, supporting tasks such as scene understanding, image segmentation, and image editing.
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Instance Segmentation

Instance segmentation annotation extends semantic segmentation by distinguishing between individual instances of objects within images. Each object instance is assigned a unique identifier or mask, enabling precise segmentation and identification of multiple objects of the same class within an image.
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Landmark Annotation

Landmark annotation involves identifying and labeling specific points or landmarks within images, such as key points on human faces or anatomical landmarks in medical images. This annotation type is useful for tasks such as facial recognition, pose estimation, and anatomical analysis.
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Line Annotation

Line annotation entails drawing lines or polylines to annotate linear features or structures within images, such as roads, boundaries, or contours. This annotation type provides spatial information about the orientation, length, and curvature of linear features, supporting tasks such as map digitization and infrastructure planning.
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Text Annotation

Text annotation involves labeling text regions or individual characters within images, enabling optical character recognition (OCR) and text extraction tasks. This annotation type is useful for digitizing text from images, enabling tasks such as document analysis, text detection, and translation.
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Attribute Annotation

Attribute annotation involves annotating images with descriptive attributes or properties associated with objects or regions within images. This annotation type provides additional contextual information about image content, supporting tasks such as image retrieval, content-based image search, and image understanding.
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Color Annotation

Color annotation entails labeling regions or pixels within images with specific color categories or attributes. This annotation type is useful for tasks such as color recognition, image segmentation, and color-based image retrieval in applications such as fashion, design, and image processing.
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Depth Annotation

Depth annotation involves estimating and annotating depth or distance information for pixels or objects within images. This annotation type enables tasks such as depth estimation, 3D reconstruction, and augmented reality applications, enhancing the spatial understanding and realism of images.
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How we Deliver Image Labeling Projects

At Training Data, we are committed to delivering Image Labeling Projects with precision, efficiency, and client satisfaction as our top priorities. Our process comprises several key stages, each meticulously designed to ensure accuracy, quality, and timely delivery.

Project Scoping and Requirements Gathering

/ 01
We start by conducting thorough consultations with our clients to grasp their project needs, precise labeling tasks, and desired results. This phase allows us to tailor our approach and define clear objectives for the project.

Data Collection and Preparation

/ 02
Once the project scope is defined, we collect the image data required for labeling and preprocess it as necessary. This may involve data cleaning, formatting, and augmentation to ensure optimal quality and compatibility with our labeling tools.

Annotation Methodology Selection

/ 03
Based on the project requirements and data characteristics, we select the most appropriate annotation methodologies and tools. Whether it involves bounding box annotation, semantic segmentation, or landmark annotation, we choose the optimal approach to achieve accurate and consistent labeling.

Annotation Execution

/ 04
Our skilled annotators meticulously annotate the images according to the predefined guidelines and criteria. This involves labeling objects, outlining regions, or assigning attributes, ensuring the accurate representation of image content for downstream tasks.

Quality Control and Assurance

/ 05
Quality is paramount in our process. Before finalizing the annotations, we conduct rigorous quality control checks to detect and rectify any errors or inconsistencies. This includes manual inspections, validation against ground truth data, and automated validation tools to ensure data integrity.

Validation and Review

/ 06
Once the annotations are completed, our team conducts thorough validation and review processes. We verify the accuracy and completeness of the annotations, ensuring they meet the client's specifications and adhere to industry standards. Any discrepancies or issues that are identified are promptly addressed.

Delivery and Formatting

/ 07
Upon validation, we deliver the annotated image data in the client's preferred format and specifications. Whether it's compatible with machine learning frameworks, image processing libraries, or custom applications, we ensure seamless integration with the client's workflow for further analysis and processing.

Client Feedback and Iteration

/ 08
We value client feedback throughout the process. We urge clients to examine the annotations provided and suggest any needed revisions or modifications. We strive to ensure that the final deliverables meet or surpass the client's expectations and requirements.

Post-Delivery Support

/ 09
Our support doesn't end with delivery. If clients have any inquiries or need additional assistance, our team is readily available to offer ongoing support and guidance. We aim to be a trusted partner in leveraging annotated image data for our clients' projects and initiatives.
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Image Labeling Use Cases

E-commerce and Retail

E-commerce platforms use image labeling data to categorize and tag products, enabling accurate product search and recommendation systems. Labels such as brand, color, size, and style help improve product discovery, enhance user experience, and increase sales conversion rates.

Healthcare and Medical Imaging

Healthcare institutions use image labeling data to annotate medical images, such as X-rays, MRIs, and CT scans, with anatomical landmarks, abnormalities, and pathologies. This facilitates medical diagnosis, treatment planning, and patient monitoring, leading to improved clinical outcomes and patient care.

Autonomous Vehicles and Transportation

Autonomous vehicle companies depend on image labeling data to train computer vision algorithms for object detection, lane detection, and traffic sign recognition. Labels such as pedestrians, vehicles, and road markings enable self-driving cars to perceive and navigate the environment safely and autonomously.

Agriculture and Crop Monitoring

Agriculture companies utilize image labeling data to analyze satellite and drone imagery for tasks such as crop monitoring, disease detection, and yield estimation. Labels such as crop types, pest infestations, and irrigation patterns support precision farming techniques, optimize resource allocation, and increase agricultural productivity.

Security and Surveillance

Security firms utilize image labeling data to annotate surveillance footage with objects of interest, such as people, vehicles, and suspicious activities. This enables automated video analysis, real-time threat detection, and proactive security measures in public spaces, airports, and critical infrastructure facilities.

Manufacturing and Quality Control

Manufacturing companies employ image labeling data to inspect and classify products on assembly lines for defects, damages, or deviations from specifications. Labels such as defects, dimensions, and surface abnormalities enable quality control checks, reduce product defects, and ensure compliance with quality standards.

Smart Cities and Urban Planning

Urban planning agencies use image labeling data to analyze aerial and street-level imagery for urban development, traffic management, and infrastructure planning. Labels such as buildings, roads, parks, and utilities support city modeling, land use analysis, and sustainable urban growth initiatives.

Environmental Monitoring and Conservation

Environmental organizations leverage image labeling data to analyze satellite imagery for monitoring ecosystems, habitat mapping, and biodiversity assessment. Labels such as vegetation types, land cover changes, and wildlife habitats support environmental conservation efforts and ecosystem management strategies.

Entertainment and Gaming

Entertainment companies use image labeling data to annotate images and videos for content creation, virtual set design, and character animation in movies, TV shows, and video games. Labels such as characters, props, and backgrounds enhance storytelling, visual effects, and immersive gaming experiences.

Education and EdTech

Educational institutions utilize image labeling data to create educational materials, interactive learning resources, and computer-based assessments. Labels such as educational concepts, objects, and diagrams facilitate personalized learning, student engagement, and knowledge retention in online and digital learning platforms.

Stages of work

  • Application

    /01
    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

    /02
    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

    /03
    We prepare a contract and all necessary documentation upon the request of your accountants and lawyers
  • Workflow customization

    /04
    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

    /05
    Data uploads for verification are done iteratively, allowing your team to review and approve collected/annotated data
  • Post-payment

    /06
    You pay for the work after receiving the data in agreed quality and quantity

Timeline

  • 24 hours
    Application
  • 24 hours
    Consultation
  • 1 to 3 days
    Pilot
  • 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

Why
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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