Image Annotation and Labeling Services
Unidata provides image processing and annotation services, delivering high-quality datasets for your machine learning and AI projects. Our team ensures precise annotations to boost model performance, offering full support for building robust datasets
- 95%+ annotation accuracy
- 1,000+ domain-matched annotators
- Pilot launched within days
Industries
Healthcare
Medical imaging analysis for tumor detection, pathology diagnosis, and faster disease identification.
Retail & E-commerce
Product categorization, visual search, and virtual try-on for enhanced shopping experiences.
Automotive Systems
Road object recognition, lane navigation, and pedestrian detection for safer self-driving.
Agriculture
Crop monitoring, pest detection, and autonomous farming through satellite and drone imagery.
Real Estate
Property analysis, virtual staging, and structural damage assessment for better valuations.
Customer Service & Support
Chatbot training, product recognition, and inquiry handling for improved customer experience.
Security & Surveillance
Facial recognition, behavior detection, and threat identification in crowded environments.
Manufacturing
Defect detection, quality control, and predictive maintenance for efficient production lines.
Data Annotation Vs Labeling Tasks
| Image Data Annotation | Image Data Labeling | |
|---|---|---|
| Definition | Detailed marking of spatial information, object boundaries, and relationships within images | Assigning classification labels or simple tags to entire images or basic regions |
| Work Coverage | Comprehensive spatial understanding: pixel-level detail, object shapes, occlusions, and scene composition | Image-level or basic region categorization without detailed spatial boundaries |
| Common Tasks |
|
|
| Complexity Level | High complexity: requires spatial reasoning, understanding of perspective, and pixel-level precision | Low to medium complexity: primarily category selection or binary decisions |
| ML Impact | Enables: object detection, instance segmentation, pose estimation, depth prediction, scene understanding | Enables: image classification, content filtering, basic recognition, catalog categorization |
Image Data Annotation Types
The best software for image annotation tasks
Labelbox
Labelbox is a versatile data labeling platform designed for image, video, text, and sensor fusion annotation. It is well-suited for large-scale projects, offering robust collaboration tools and AI-assisted labeling features that enhance productivity.
Best For:
Enterprises and teams requiring a scalable, end-to-end data annotation solution with strong project management capabilities.
Key Features
- Supports multiple annotation types such as bounding boxes, polygons, and semantic segmentation.
- AI-powered automation to speed up repetitive annotation tasks.
- Integrated project management tools for tracking progress and performance.
- Extensive API support for seamless integration with machine learning workflows.
CVAT (Computer Vision Annotation Tool)
CVAT is an open-source tool developed by Intel that provides a powerful environment for annotating images and videos. It is particularly well-suited for detailed and complex annotations, supporting a variety of formats and extensive customization options.
Best For:
Developers and researchers looking for a free, customizable tool for complex image and video annotation tasks.
Key Features
- Free and open-source, with active community support.
- Supports multiple annotation types, including bounding boxes, polygons, and 3D cuboids.
- Advanced features for tracking objects across video frames.
- Highly customizable with scriptable automation options.
LabelImg
LabelImg is a simple, open-source graphical image annotation tool that is ideal for quick and straightforward bounding box annotations. It is user-friendly and widely used for creating datasets for object detection tasks.
Best For:
Individuals and small teams needing a straightforward, no-frills tool for bounding box annotations.
Key Features
- Easy-to-use interface for quick bounding box annotation.
- Supports output in PASCAL VOC and YOLO formats, commonly used in machine learning models.
- Lightweight and requires minimal setup.
- Active development and community support.
V7
V7 is a cutting-edge annotation platform that integrates AI and automation to streamline the labeling process. It excels in handling large datasets, offering sophisticated tools for image and video annotation with a focus on collaboration and scalability.
Best For:
Large teams and enterprises looking for a comprehensive, AI-driven annotation platform that supports extensive collaboration.
Key Features
- AI-powered auto-annotation to accelerate labeling tasks.
- Advanced collaboration tools for managing large teams and complex projects.
- Supports a wide range of annotation types, including object tracking and keypoint annotation.
- Real-time quality control and workflow automation.
RectLabel
RectLabel is a macOS-based image annotation tool focused on simplicity and efficiency. It offers features tailored to creating bounding boxes and polygon annotations, making it an excellent choice for users working within the Apple ecosystem.
Best For:
MacOS users looking for a simple and effective tool for bounding box and polygon annotations.
Key Features
- Intuitive macOS interface with support for bounding boxes and polygons.
- Customizable shortcuts and tools for efficient annotation.
- Supports YOLO, COCO, and VOC formats for machine learning integration.
- Lightweight and optimized for quick use on Mac devices.
Prodigy
Prodigy is a machine learning-powered annotation tool designed to help data scientists and developers create high-quality training data faster. It is particularly well-suited for iterative, active learning workflows.
Best For:
Data scientists and developers looking for an advanced, machine learning-integrated annotation tool that supports active learning and iterative model training.
Key Features
- Active learning features that suggest annotations based on model predictions.
- Supports a wide range of annotation types, including image classification, object detection, and more.
- Integration with Python and major ML libraries for seamless workflows.
- Flexible and customizable to suit specific project needs.
VoTT (Visual Object Tagging Tool)
VoTT is an open-source annotation tool by Microsoft that offers a simple and flexible solution for creating datasets for object detection. It supports a variety of export formats and integrates well with Azure services.
Best For:
Teams and developers needing a free, Azure-integrated tool for creating and managing object detection datasets.
Key Features
- User-friendly interface for creating bounding boxes and exporting in formats like YOLO, TFRecord, and CSV.
- Supports Azure ML and other cloud-based services for seamless integration.
- Open-source and customizable for specific project needs.
- Batch processing capabilities for efficient annotation.
How Unidata Provide Data Labelling Process
A Clear, Controlled Workflow From Brief to Delivery
- You
- Share your raw data, annotation requirements, and quality standards
- Unidata
- We analyze your data, define the methodology, and assign a dedicated project lead. The right annotation type and domain-matched annotators are confirmed before anything starts.
- You
- Review annotated samples, validate quality, and approve scope before full-scale work begins.
- Unidata
- We annotate a small representative sample and deliver a clear cost estimate broken down by complexity, hours, and validation rounds.
- You
- Review and sign. Scope, quality thresholds, and deadlines are all defined in writing upfront.
- Unidata
- We prepare a full confidentiality agreement covering your data, guidelines, and any proprietary model details.
- You
- Share existing guidelines and format requirements. No guidelines yet? We build them together.
- Unidata
- We configure the right annotation platform for your data type: Labelbox, SuperAnnotate, CVAT, or Label Studio. Workflows, label taxonomy, and quality benchmarks are set before a single label is applied.
- You
- Review sample batches at each milestone and share feedback with your project lead.
- Unidata
- Trained, domain-matched annotators work through your dataset. No batch moves forward without passing internal quality checks.
- You
- Review edge cases and confirm acceptance criteria before final delivery.
- Unidata
- Every batch goes through automated validation and human review. Inter-annotator agreement (IAA) is tracked throughout. Inconsistencies are caught and resolved before the dataset moves forward.
- You
- Receive your annotated dataset in the format you need: COCO, Pascal VOC, JSON, CoNLL, PCD, or custom. Full quality report included.
- Unidata
- Clean, validated, training-ready data delivered on schedule. Final invoice aligned to the scope agreed at Step 02.
Have questions about the process? Every project starts with a free consultation — no commitment required.
Data Annotation Challenges? Value You Get with Unidata
Real Challenges
- No annotators, tools, or workflow to process collected data
- No quality check on labeled data before it hits the pipeline
- No way to ensure two annotators label the same object consistently
- Can’t find annotators with LiDAR, medical, or financial expertise
- Scope creep and rework cycles exhaust the budget before delivery
Value with Unidata
- Project lead assigned and pilot launched within days
- Every batch validated before delivery, 95%+ accuracy via multi-stage QA
- Label consistency tracked per batch, issues caught before training fails
- 1,000+ annotators matched by domain — the right expert, every time
- Pilot-first pricing, fixed scope, zero hidden rework charges
Data Annotation Files Example
Working with annotation data from CVAT and JSON formats, you'll receive optimized code that seamlessly processes both file types, complete with practical examples and visual representations of your data structure.
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What our clients are saying
UniData
Data purchase
Our team got in touch with UniData for purchasing video data. The team at UniData was transparent, timely, and pleasant to communicate and negotiate with. Their samples and descriptions aligned well with the data we received. We will certainly reach out to UniData again if we're in search of 3rd party video data.
Data is well organized and easy to…
Data is well organized and easy to consume. We could download and use it for training within few hours of receiving the data links.
FAQ
By accurately labeling elements such as objects, boundaries, and features, image annotation enables AI systems to learn and improve their performance in tasks such as object detection, image segmentation, and facial recognition.
We track key metrics such as Error Rate, IAA (Inter-Annotator Agreement), and IoU (Intersection over Union), and use benchmark (“golden”) samples to evaluate performance.
Why Companies Trust Unidata’s Services for ML/AI
Share your project requirements, we handle the rest. Every service is tailored, executed, and compliance-ready, so you can focus on strategy and growth, not operations.
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Tell us what you need — we’ll reply within 24h with a free estimate
- Andrew
- Head of Client Success
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