Whether building robotic systems, creating datasets for specific application scenarios, or training and continuously optimizing computer vision models, image annotation is a core link supporting project success. Choosing the right image annotation tool offers value far beyond improving engineering efficiency—it frees annotation teams from tedious and repetitive manual operations, allowing them to focus more on optimizing annotation rules and controlling data quality. Additionally, through standardized annotation processes and intelligent auxiliary functions, it reduces the probability of incorrect annotations from the source, laying a solid data foundation for subsequent model training.
Based on feedback from industry partners using different data annotation tools in workflows such as dataset annotation and model training, this article summarizes the 5 best data annotation tools so far in 2025 and compares their core functions to help you find the most suitable option.
I. Comparison of Data Annotation Tools
1. Roboflow
As a leading industry platform, Roboflow is favored by developers and machine learning researchers for its simple interface and powerful dataset management capabilities. While its core strengths lie in data preprocessing and model deployment, its built-in image annotation features are also noteworthy: automatic annotation via pre-trained models significantly reduces manual work; it also supports hosting and exporting public datasets, facilitating resource sharing and reuse—ideal for quickly building model prototypes and accelerating idea validation.
Pricing：Offers a free basic version and paid advanced versions to meet the needs of projects of different scales.
Website: https://roboflow.com/?source=2025
2. Encord
For teams handling high-complexity or large-scale datasets, Encord is a top choice with strong comprehensive capabilities. Whether for medical imaging, satellite data, or video annotation, its support for multimodal annotation and production-grade MLOps integration sets industry benchmarks: it is compatible with all mainstream data types (images, videos, DICOM medical files, geospatial data, audio, and documents); supports custom annotation workflows, equipped with professional review and quality inspection tools to ensure data accuracy; features built-in model evaluation and monitoring functions to iteratively optimize data quality, forming a closed loop of "annotation-training-feedback"; and provides robust APIs and SDKs for programmatic access, flexibly adapting to existing workflows.
Pricing：Uses a customized package model, priced based on team size and specific needs.
Website: https://encord.com/?source=2025
3. Labelbox
Labelbox is a typical representative of one-stop data and model management, integrating data annotation, model integration, and data analysis functions. It supports multiple file formats and seamlessly connects with mainstream cloud services and machine learning tools. The tool covers the entire workflow from annotation to training, reducing tool-switching costs; supports active learning to prioritize annotating data most critical to model improvement, enhancing efficiency; adopts an elastically scalable architecture to quickly expand with business growth, ensuring strong cost controllability; and offers comprehensive SDK and API support for secondary development and integration by technical teams.
Pricing：Provides a free basic version and paid advanced versions to meet the needs of projects of different scales.
Website: https://labelbox.com/?source=2025
4. T-Rex Label
T-Rex Label is a standout annotation tool in 2025, dedicated to helping users quickly build datasets for complex scenarios. Featuring efficient and user-friendly AI-assisted annotation, paired with an intuitive interface, it supports both manual and automatic annotation. Equipped with state-of-the-art vision models T-Rex2 and DINO-X, it has rapidly become a top choice for annotators.
Its core advantages include:
(a) Out-of-the-box functionality with no download or installation required—operated directly in the browser, significantly lowering the barrier to use;
(b) Automatic annotation powered by the cutting-edge DINO-X vision model, balancing precision and efficiency;
(c) Exclusive T-Rex2 model with visual prompt support, excelling in rare object recognition and dense scene detection, addressing pain points of traditional tools.
Currently, T-Rex Label focuses on bbox and mask annotation, supports image and video data annotation, and is highly compatible with mainstream COCO and YOLO data formats.
Pricing：The T-Rex2 model is freely available; some models are priced based on usage.
Website：https://www.trexlabel.com/?source=2025
5. CVAT
CVAT (Computer Vision Annotation Tool), a leader in the open-source field, is suitable for teams seeking full control over annotation workflows. It provides solid manual annotation tools and supports plugin extensions to meet personalized needs; is completely free and community-supported, with no concerns about copyright or usage costs; is designed for technical teams familiar with self-hosted deployment, allowing in-depth customization of server and data storage solutions.
Pricing：Completely free and open-source, with requirements for team technical capabilities.
Website: https://github.com/cvat-ai/cvat
II. How to Evaluate if an Annotation Tool is Suitable
When choosing an annotation tool, comprehensive consideration should be given to project scale, data types, team collaboration needs, and budget. In summary, a suitable annotation tool typically needs to meet the following criteria:
1. Improve efficiency and significantly reduce manual annotation time
For example, some tools offer automatic pre-annotation: for large numbers of images containing common objects, these tools can automatically identify and annotate object positions based on user-provided "label" names, requiring annotators only to review and fine-tune. For rare long-tail targets, tools with "visual prompt" models can significantly reduce operational difficulty.
2. Support multiple data types (e.g., images, videos)
In addition to common image formats like JPG and PNG, high-quality tools should smoothly support video file import and processing of more specialized data types to adapt to diverse annotation needs across industries.
3. Annotation accuracy
This depends on the tool’s ability to provide richer, high-quality vision models. By integrating high-precision vision models adapted to different application scenarios, tools can offer more reliable intelligent assistance during annotation, meeting strict precision requirements in various fields.
4. Provide cost-effective solutions
This refers not only to reasonable procurement or subscription prices but also cost control during use. For example, some tools charge based on annotation volume, effectively reducing initial investment for small and medium-sized teams or startups; others offer free versions with built-in common annotation functions that cover a significant portion of basic needs, avoiding extra payments for unnecessary advanced features.
III. Conclusion
The performance of AI models largely depends on the quality of their training data. Data annotation tools are key to converting raw visual data into labeled datasets understandable by machine learning models. For the 5 data annotation tools mentioned above, individuals or teams can choose based on the following guidelines:
(a) Prioritize Roboflow for rapid prototype development;
(b) Consider Encord for complex multimodal data processing;
(c) Choose Labelbox for one-stop full-process management;
(d) Opt for T-Rex Label for a cost-effective solution emphasizing usability and AI assistance efficiency;
(e) Rely on T-Rex Label for quickly iterating datasets for complex scenarios;
(f) Select CVAT as the ideal free solution for technical teams needing full control.