Contents
Why Image Annotation Tools Matter for AI
Top AI Image Annotation Tools Compared
1. Encord – Best for Multimodal Annotation and Enterprise-Grade Workflows
2. V7 – Powerful UI and Automation
3. CVAT – Top Open-Source Image Annotation Tool
4. Labelbox – Well-Rounded With Model Integration
5. Roboflow – Developer-Friendly with Built-In Preprocessing
6. Lightly – Best for Data Curation (Not Annotation First)
How to Choose the Best Image Annotation Tool for Your AI Project
Final Thoughts: What’s the Best Image Annotation Tool in 2025?
Encord Blog
Top Image Annotation Tools for AI Projects in 2025
5 min read
Whether you're training a computer vision model, labeling datasets for healthcare AI, or building autonomous vehicle systems, image annotation is the backbone of your project’s success. With the surge of multimodal AI applications in 2025, choosing the right image annotation tool is more important than ever.
In this article, we'll break down the best AI image annotation tools, compare their key features, and help you determine which platform best suits your workflow - whether you're working with millions of images or specialized data like DICOM files.
Why Image Annotation Tools Matter for AI
AI models are only as good as the data they’re trained on. Annotation tools are essential for transforming raw visual data into labeled datasets that machine learning models can understand. The right platform will:
- Reduce manual labeling time
- Improve label accuracy and consistency
- Support diverse data types (e.g., images, video, geospatial, medical)
- Offer collaboration, QA, and integration with your ML pipeline
With hundreds of tools on the market, choosing the best image annotation software for your use case can be difficult. Below, we break down the top tools that stand out in 2025.
Top AI Image Annotation Tools Compared
| Tool | Best For | Key Features | Supports Multimodal? | Pricing |
| Encord | Enterprise, healthcare, multimodal AI | Advanced annotation workflows, QA, model evaluation | Yes | Custom plans |
| V7 | Enterprise teams | Auto-annotation, workflows, model-assisted labeling | Yes | Free + paid plans |
| CVAT | Open-source teams | Open-source, customizable UI | Image/video only | Free (open source) |
| Labelbox | Enterprise + startups | Model integration, analytics, QA tools | Yes | Usage-based pricing |
| SuperAnnotate | Teams needing scalable workflows | Annotation + data management | Yes | Tiered pricing |
| Roboflow | Developers and startups | Annotation, dataset hosting, preprocessing tools | Limited multimodal | Free + paid tiers |
| Lightly | Model training data curation | Focused on self-supervised learning and filtering | Not annotation-first | Custom |
1. Encord – Best for Multimodal Annotation and Enterprise-Grade Workflows
Encord stands out in 2025 as the most comprehensive annotation platform for teams working with complex or high-volume datasets. Whether you're labeling medical imagery, satellite data, or video, Encord offers unmatched support for multimodal annotation and production-level MLOps integration.
Why Encord is a Top Image Annotation Tool:
- Supports all major data types: Images, video, DICOM, geospatial, audio, and documents
- Customizable annotation workflows with expert review and QA tools
- Built-in model evaluation and monitoring to improve data quality iteratively
- Programmatic access via robust APIs and SDKs
- Secure and compliant (SOC2, HIPAA, GDPR)
Example Use Case:
Stanford Medicine's healthcare teams use Encord to annotate DICOM data and build AI models for clinical imaging, benefiting from role-based controls and high-precision tools.
2. V7 – Powerful UI and Automation
V7 offers fast, AI-assisted annotation with an intuitive user interface. It supports both manual and automatic labeling and provides powerful workflow automations for model-assisted labeling.
- Built-in model training and versioning
- Highly visual, collaborative interface
- Strong QA features and customizable pipelines
- Ideal for image, video, and document data
While V7 is great for visual annotation workflows, it may lack some of the deeper evaluation tooling Encord offers for model testing and curation.
3. CVAT – Top Open-Source Image Annotation Tool
CVAT (Computer Vision Annotation Tool) is the go-to choice for teams seeking an open-source solution with complete control over their annotation pipeline. Built by Intel, it offers robust manual annotation tools and plugin support for custom functions.
- Free and community-supported
- Designed for technical teams comfortable with self-hosting
- Works well for image and video annotation
- Limited support for non-visual modalities (e.g., audio, documents)
Best suited for internal teams with dedicated DevOps resources.
4. Labelbox – Well-Rounded With Model Integration
Labelbox combines data labeling, model integration, and analytics under one roof. It supports various file types and integrates with leading cloud services and ML tools.
- End-to-end annotation and training workflows
- Active learning support
- Usage-based pricing that can scale quickly
- Strong SDK and API support
Labelbox is a solid middle-ground option between Encord’s enterprise-grade platform and open-source tools like CVAT.
5. Roboflow – Developer-Friendly with Built-In Preprocessing
Roboflow is popular among developers and ML researchers thanks to its simple interface and strong dataset management tools. While its core focus is on preprocessing and deployment, it includes image annotation features.
- Auto-labeling using pre-trained models
- Public dataset hosting and export
- Great for prototyping models quickly
- More limited in advanced QA or multimodal support
6. Lightly – Best for Data Curation (Not Annotation First)
While Lightly isn't an annotation tool in the traditional sense, it helps teams select the most informative data for labeling by using self-supervised learning. It integrates with annotation tools but focuses on optimizing training datasets.
- Useful for reducing labeling volume
- Prioritizes complex examples and underrepresented classes
- Works alongside platforms like Encord or Labelbox
Use Lightly as a complement to your annotation pipeline.
How to Choose the Best Image Annotation Tool for Your AI Project
When deciding which tool is right for you, consider the following:
| Criteria | Why It Matters |
| Data Modalities Supported | Do you need image only, or also video, text, audio, or medical? |
| Automation & AI Assistance | Tools like Encord and V7 speed up annotation with AI assistance |
| Security & Compliance | Crucial for healthcare and financial services |
| Custom Workflows & QA | Can you manage annotator tasks, reviews, and error tracking? |
| Model Evaluation Integration | Does the tool help you test model performance? |
| API Access & Toolchain Fit | Do you need integration with your cloud or MLOps stack? |
If you're building models in healthcare, robotics, or any multimodal environment, a tool like Encord provides annotations and full data curation, QA, and model evaluation capabilities.
Final Thoughts: What’s the Best Image Annotation Tool in 2025?
The best AI image annotation tool depends on your project size, domain, and required precision. For large-scale enterprise teams working with complex datasets—especially those involving medical imagery, video, or multimodal inputs—Encord leads the way in 2025.
With its all-in-one approach to data curation, annotation, QA, and evaluation, Encord empowers ML teams to move faster, reduce labeling errors, and confidently put models into production.
Whether you're a startup looking for automation or a hospital labeling DICOM scans for diagnostics, investing in the right annotation tool will save you time, improve model performance, and future-proof your AI pipeline.
Explore the platform
Data infrastructure for multimodal AI
Explore product
Explore our products
Index
Manage & curate your data
Understand and manage your visual data, prioritize data for labeling, and initiate active learning pipelines.
Annotate
Supporting your labeling needs
Super charge your data annotation with AI-powered labeling — including automated interpolation, object detection and ML-based quality control.
Active
Find & fix data issues with ease
Monitor, troubleshoot, and evaluate the data and labels impacting model performance.
Encord is the most comprehensive tool for enterprise-grade image annotation, especially in medical, geospatial, and multimodal domains.
CVAT is the top open-source image annotation tool. It’s free, widely supported, and great for teams who want control over their infrastructure.
Yes. Platforms like Encord and V7 use AI-assisted labeling and model predictions to speed up annotation and reduce manual effort.
Encord supports DICOM and NIfTI formats, making it a leading choice for medical imaging annotation.
Encord offers built-in support for images, video, audio, documents, and geospatial data, making it ideal for multimodal annotation.
Encord offers robust AI-assisted labeling features that help automate the annotation process, particularly for identifying and classifying objects, such as staff and patrons in video data. This capability can significantly reduce bottlenecks in annotation workflows by leveraging machine learning models to pre-label data.
Encord facilitates in-house annotation by offering an intuitive platform that allows teams to annotate data directly alongside deep learning developers. This collaborative approach helps address specific annotation challenges early in projects, improving the overall quality and relevance of the annotations.
AI tagging in Encord significantly accelerates the annotation process, reducing the time and effort needed to tag large volumes of video content. This feature enhances productivity and allows teams to focus on deeper analysis and content creation, leveraging AI to maintain high accuracy in annotations.
Encord provides an easy-to-use annotation tool that simplifies the process for users. While some users may require scripting for more complex workflows, Encord focuses on making annotation accessible and efficient, allowing teams to concentrate on data quality and model training.
Encord offers various modules designed to streamline the transition from prototyping to production. These features focus on optimizing data selection and annotation processes, allowing teams to move from M0 to M1 faster, thereby enhancing the efficiency of AI model development.
In Encord, you can create various workflows tailored to your project's needs. This includes multiple annotation review stages, consensus processes, and the integration of AI-assisted tools for tasks such as pre-labeling, ensuring a structured approach to annotation.
Encord deals with various data types involved in AI development, including images, videos, and multimodal data. Our platform ensures that the data is effectively curated and annotated to support the training of machine learning models.
Encord provides a comprehensive data management platform designed to manage, curate, and annotate AI data quickly and at scale. It supports teams at every stage of development, from early adopters to mature production environments.
Encord offers an annotation part of the platform designed for specific workflows, which is particularly useful when dealing with hundreds of millions of images. This ensures efficient management and curation of large datasets.
Encord features AI-assisted annotation tools that facilitate the annotation process by providing auto annotations and pre-labels. This helps streamline workflows and increase efficiency in large-scale annotation projects.