Image annotation plays a pivotal role in machine learning, computer vision, and AI projects. Whether you’re labeling images for object detection, classification, or segmentation, the right app can help streamline the process. While it can seem like a tedious task, these tools make it simpler and faster, especially when working with large datasets. In this article, we’re diving into the best image annotation apps that can elevate your projects, boost productivity, and make your annotation tasks much more efficient.
If you’re someone who wants to speed up the annotation process without compromising on quality, or if you’re simply looking for a tool that helps teams collaborate seamlessly, there’s something in here for you. Let’s explore these apps and see how they can help with your data labeling needs.
1. FlyPix AI
At FlyPix AI, we specialize in making geospatial data accessible and actionable. Our platform is built to help users detect objects, track changes, and spot anomalies in satellite and aerial imagery. This tool is especially valuable for industries that require detailed analysis of the Earth’s surface, such as agriculture, urban planning, and environmental monitoring. When it comes to image annotation apps, we provide a streamlined solution that enhances the precision of labeling and annotating images for machine learning tasks.
We’ve designed FlyPix AI to be user-friendly and no-code, so you don’t need to be an expert to create custom AI models. With support for various data sources like drones, satellites, and LiDAR, FlyPix AI is versatile and adaptable to a wide range of projects. Whether you’re working with a small dataset or a large organization, FlyPix AI provides real-time analytics, collaborative features, and comprehensive data visualization options, making it an effective tool for teams working with annotated images.
Key Features:
- No-code platform for object detection, segmentation, and anomaly detection
- Supports satellite, drone, hyperspectral, LiDAR, and SAR images
- Interactive tools to train custom AI models for image annotation
- Real-time analytics with dashboards, heatmaps, and change tracking
- Enterprise-grade features like API access and multispectral processing
Services:
- Geospatial object detection and localization
- Change and anomaly detection in imagery
- Dynamic tracking of objects over time
- Custom AI model development for tailored analytics
- Seamless integration with existing GIS systems
- Heatmap generation for visualizing patterns in data
Best For:
- Teams working on geospatial data and image annotation tasks
- Industries like agriculture, urban planning, and environmental monitoring
- Users needing a no-code solution for creating custom AI models
- Large-scale projects that require real-time analytics and collaboration
Contact and Social Media Information:
- Website: flypix.ai
- Address: Robert-Bosch-Str. 7, 64293 Darmstadt, Germany
- Phone: +49 6151 2776497
- Email: info@flypix.ai
- LinkedIn: www.linkedin.com/company/flypix-ai
2. Roboflow
Roboflow is an image annotation platform primarily used for machine learning tasks like object detection, image classification, and segmentation. It allows users to annotate images, train models, and prepare datasets for AI projects. The platform supports various annotation types and is compatible with popular machine learning frameworks, including TensorFlow and PyTorch, making it versatile for teams that need a streamlined workflow.
In addition to manual labeling, Roboflow offers AI-assisted tools to help speed up the annotation process. It also provides tools for dataset management and allows users to export labeled data directly for model training. Roboflow is particularly suited for teams working on computer vision projects that require efficient, accurate labeling of large datasets.
Key Features:
- Supports object detection, classification, and segmentation
- Integration with machine learning frameworks like TensorFlow and PyTorch
- AI-assisted annotation tools for faster labeling
- Real-time collaboration features for teams
- Scalable for both small and large datasets
Best For:
- Machine learning and AI teams working on computer vision
- Projects requiring integration with TensorFlow or PyTorch
- Teams needing AI-assisted annotation to speed up the process
- Users handling large datasets for training machine learning models
Contact and Social Media Information:
- Website: roboflow.com
- LinkedIn: www.linkedin.com/company/roboflow-ai
- Twitter: x.com/roboflow
3. CVAT
CVAT (Computer Vision Annotation Tool) is an open-source image and video annotation tool. Developed by Intel, it is designed for tasks like object detection, segmentation, and tracking. It supports various annotation formats, including bounding boxes, polygons, and keypoints. CVAT is primarily used by teams working on computer vision tasks and supports collaborative annotation to allow multiple users to work on a project simultaneously.
One of the strengths of CVAT is its open-source nature, which means it is free to use and can be modified to fit the specific needs of a project. The platform can integrate with machine learning frameworks, allowing teams to quickly transition from annotating data to using it for training AI models. This makes CVAT a flexible tool suitable for a variety of computer vision projects.
Key Features:
- Open-source and free to use
- Supports bounding boxes, polygons, and keypoints for annotation
- Real-time collaboration for team-based annotation
- Integrates with machine learning frameworks
- Customizable for specific annotation needs
Best For:
- Teams working on object detection, segmentation, and tracking
- Collaborative annotation projects requiring multiple users
- Teams needing a free, open-source solution
- Projects that require integration with machine learning workflows
Contact and Social Media Information:
- Website: www.cvat.ai
- LinkedIn: www.linkedin.com/company/cvat-ai
- Facebook: www.facebook.com/cvat.corp
4. LabelMe
LabelMe is a free, open-source annotation tool developed by MIT’s CSAIL. It is designed for image labeling tasks, such as object detection and segmentation, using bounding boxes, polygons, and points. LabelMe is a straightforward tool that can be used directly from a web browser, which makes it easy to access and begin annotating images without the need for installation or complex setup.
Since LabelMe is open-source, it is ideal for users or teams with limited resources or those who prefer using a tool that they can modify. While it may not have all the features of more advanced annotation platforms, its simplicity makes it effective for basic annotation tasks, particularly in research environments or for smaller-scale projects.
Key Features:
- Open-source and free to use
- Supports bounding boxes, polygons, and points
- No installation required, runs directly in the browser
- Simple, intuitive interface
- Best suited for small to medium annotation projects
Best For:
- Small to medium-sized annotation projects
- Users looking for a free, simple tool for image annotation
- Research teams or hobbyists working on object detection and segmentation
- Users who need a lightweight, browser-based tool
Contact and Social Media Information:
5. Label Studio
Label Studio is an open-source data labeling platform that supports a wide range of annotation tasks, including image, text, audio, and video annotations. It is highly customizable, allowing users to tailor the platform to their specific needs, whether that’s for object detection, classification, or even text classification. Label Studio’s flexibility makes it suitable for a variety of machine learning projects, not just computer vision.
In addition to annotation, Label Studio offers collaborative features, enabling multiple team members to work on the same project in real-time. It integrates well with machine learning pipelines, allowing users to quickly export labeled data for model training. Label Studio is open-source, which allows teams to use it for free and modify it to meet their project’s specific requirements.
Key Features:
- Supports image, text, audio, and video annotation
- Customizable interface for different annotation tasks
- Real-time collaboration for teams
- Integration with machine learning workflows
- Open-source and free to use
Best For:
- Teams working with multiple data types (images, text, audio, video)
- Projects requiring customizable workflows
- AI teams integrating annotations into machine learning models
- Users seeking an open-source, flexible annotation tool
Contact and Social Media Information:
- Website: labelstud.io
- LinkedIn: www.linkedin.com/company/heartex
- Twitter: x.com/labelstudiohq
6. Scale AI
Scale AI is a data labeling platform designed to streamline the process of annotating images, videos, and other types of data for machine learning projects. The platform supports a variety of annotation types, including object detection, image segmentation, and classification. Scale AI uses a combination of machine learning models and human annotators to provide high-quality labeled data that is ready for model training.
Scale AI’s focus is on high accuracy and efficiency, providing tools for quality control and real-time project management. It offers integration with several machine learning pipelines, allowing for seamless data export directly into model training workflows. Scale AI is used by teams in industries like autonomous vehicles, e-commerce, and healthcare that require large-scale annotated datasets.
Key Features:
- Supports image segmentation, object detection, and classification
- Human-assisted AI tools for data annotation
- Real-time project management and quality control tools
- Integration with machine learning frameworks
- High-volume data labeling capabilities
Best For:
- Teams working on large-scale image and video annotation projects
- Industries like autonomous vehicles and healthcare needing high-quality labeled data
- Projects that require real-time collaboration and quality control
- Teams needing seamless integration with AI models
Contact and Social Media Information:
- Website: scale.com
- LinkedIn: www.linkedin.com/company/scaleai
- Twitter: x.com/scale_ai
- Facebook: www.facebook.com/scaleapi
7. SuperAnnotate
SuperAnnotate is a tool designed for image annotation tasks such as object detection and segmentation. It provides a comprehensive platform for labeling images with bounding boxes, polygons, and keypoints. One of the platform’s strengths is its ability to scale, making it suitable for both small and large annotation projects. SuperAnnotate’s user interface is designed to help teams collaborate in real-time, making it an effective tool for team-based projects.
The platform also includes AI-powered features that assist with image annotation, allowing users to annotate data faster and with fewer manual inputs. SuperAnnotate is widely used by teams working on computer vision projects, and it provides various integration options to streamline workflows with machine learning models.
Key Features:
- Supports object detection, segmentation, and keypoint labeling
- AI-powered tools for faster annotation
- Real-time collaboration features for team projects
- Integration with machine learning models for seamless workflows
- Scalable for large datasets
Best For:
- Teams working on computer vision tasks like object detection and segmentation
- Projects requiring real-time collaboration between team members
- Teams using machine learning models and seeking seamless integration
- Users needing a scalable tool for large annotation projects
Contact and Social Media Information:
- Website: www.superannotate.com
- LinkedIn: www.linkedin.com/company/superannotate
- Twitter: x.com/superannotate
- Facebook: www.facebook.com/superannotate
8. Dataloop
Dataloop is an image annotation tool designed to support computer vision projects. It helps teams annotate images, videos, and other media types for tasks like object detection, segmentation, and classification. Dataloop is designed for scalability, making it suitable for both small and large datasets. It offers AI-assisted annotation tools to speed up the labeling process, and its platform is customizable to fit the specific needs of different teams and projects.
Dataloop also offers comprehensive data management tools, making it easy to organize and manage labeled datasets. It integrates seamlessly with machine learning workflows, allowing for the smooth transfer of annotated data to model training processes. Dataloop’s user-friendly interface and robust tools make it a valuable platform for teams working on large-scale computer vision projects.
Key Features:
- Supports object detection, segmentation, and classification
- AI-assisted annotation tools for faster labeling
- Customizable workflows for different annotation needs
- Seamless integration with machine learning models
- Comprehensive data management tools for organizing labeled data
Best For:
- Teams working with large datasets for computer vision projects
- Projects that require high-quality labeled data for machine learning models
- Users needing AI-assisted annotation for efficiency
- Teams seeking customizable workflows for specific tasks
Contact and Social Media Information:
- Website: dataloop.ai
- Address: 2 Sapir st, Herzliya, POB 12580, 4685206, Israel
- E‑mail: info@dataloop.ai
- LinkedIn: www.linkedin.com/company/dataloop
9. Supervisely
Supervisely is a platform that provides tools for image and video annotation, designed for tasks like object detection and segmentation. It offers features such as bounding boxes, polygons, and semantic segmentation tools. Supervisely is highly customizable and can be used for both small-scale and large-scale annotation projects. The platform also supports collaborative work, allowing teams to annotate data together in real time.
Supervisely integrates with machine learning frameworks, providing an efficient workflow from annotation to model training. Its robust set of features and scalability make it suitable for teams working on complex computer vision projects that require high-quality labeled data.
Key Features:
- Supports object detection, segmentation, and keypoint annotation
- Real-time collaboration features for team projects
- Integration with machine learning frameworks for seamless workflows
- Customizable for specific annotation tasks
- Scalable for both small and large projects
Best For:
- Teams working on computer vision and machine learning tasks
- Collaborative annotation projects requiring real-time work
- Users integrating annotated data directly into machine learning models
- Projects that require high-quality, scalable annotation tools
Contact and Social Media Information:
- Website: supervisely.com
- E‑mail: hello@supervisely.com
- LinkedIn: www.linkedin.com/company/deep-systems
- Twitter: x.com/supervisely_ai
10. VGG Image Annotator (VIA)
VGG Image Annotator (VIA) is an open-source image annotation tool designed to help with tasks like object detection and segmentation. It is lightweight and browser-based, making it accessible without requiring installation. VIA allows users to annotate images and videos with bounding boxes, polygons, and points, making it a versatile tool for a variety of computer vision tasks.
VIA is particularly useful for small to medium-scale annotation projects. It is simple to use, with a clean interface, and is well-suited for researchers and teams who need a free, open-source tool. Its lack of advanced features makes it a lightweight option for basic tasks, while still providing essential annotation capabilities for machine learning projects.
Key Features:
- Open-source and free to use
- Browser-based with no installation required
- Supports bounding boxes, polygons, and points
- Simple, user-friendly interface
- Best for small to medium-scale annotation tasks
Best For:
- Small to medium-scale annotation projects
- Users looking for a simple, free, open-source tool
- Researchers working on object detection and segmentation
- Teams who need a lightweight annotation tool for basic tasks
Contact and Social Media Information:
- Website: www.robots.ox.ac.uk/~vgg/software/via
- E‑mail: vgg-webmasters@robots.ox.ac.takethisout.uk
- Twitter: x.com/Oxford_VGG
11. V7
V7 is a comprehensive annotation platform that provides tools for object detection, image segmentation, and classification tasks. It allows users to annotate images and videos with various annotation types like bounding boxes, polygons, and keypoints. The platform is designed with flexibility in mind, supporting everything from small-scale projects to large, enterprise-level datasets.
One of V7’s core strengths is its AI-assisted annotation capabilities, which help users speed up the annotation process by automating parts of it. V7 also supports real-time collaboration, allowing teams to work together on large datasets, making it ideal for teams with multiple contributors. Its integration with machine learning frameworks further streamlines the process from annotation to model training.
Key Features:
- Supports object detection, image segmentation, and classification
- AI-assisted annotation to speed up the process
- Real-time collaboration for teams
- Integration with machine learning frameworks
- Scalable for both small and large datasets
Best For:
- Teams working on object detection and image segmentation tasks
- Projects requiring collaboration between multiple team members
- Users looking for AI-assisted tools to improve annotation speed
- Large-scale projects with complex datasets
Contact and Social Media Information:
- Website: www.v7labs.com
- Address: 201 Spear Street, Suite 1100, San Francisco, CA 94105
- LinkedIn: www.linkedin.com/company/v7labs
- Twitter: x.com/v7labs
12. Labelbox
Labelbox is a data labeling platform that provides tools for image annotation and other machine learning tasks. It supports object detection, image classification, and segmentation, offering a variety of annotation tools such as bounding boxes, polygons, and keypoints. Labelbox is designed for high scalability, making it suitable for both small teams and large enterprises.
The platform includes features for collaborative annotation, allowing teams to work together on large projects. Labelbox also offers built-in tools for quality control, ensuring that the annotated data is accurate and consistent. The platform integrates easily with machine learning pipelines, allowing users to move from data labeling to model training seamlessly.
Key Features:
- Supports object detection, image classification, and segmentation
- Collaborative features for team-based annotation
- Built-in quality control tools for accurate data labeling
- Scalable for both small and large projects
- Integration with machine learning workflows
Best For:
- Teams working on object detection, image classification, and segmentation tasks
- Projects requiring high-quality control and accurate data
- Enterprises or teams working on large-scale image annotation
- Users who need seamless integration with machine learning models
Contact and Social Media Information:
- Website: labelbox.com
13. MONAI Label
MONAI Label is a specialized tool focused on the medical imaging sector, designed for annotating medical images like CT scans and MRIs. It provides annotation capabilities for segmentation, classification, and object detection in medical image datasets. MONAI Label is a part of the larger MONAI (Medical Open Network for AI) framework, which is aimed at improving deep learning in healthcare and medical imaging.
One of the unique features of MONAI Label is its seamless integration with existing medical imaging workflows. The tool is designed to be used in healthcare AI research, helping teams annotate medical images accurately and efficiently. It also supports collaborative annotation, allowing teams of medical professionals and AI researchers to work together on complex datasets.
Key Features:
- Specializes in medical image annotation (e.g., CT scans, MRIs)
- Supports segmentation, classification, and object detection
- Part of the larger MONAI deep learning framework
- Seamless integration with medical imaging workflows
- Collaborative annotation features for teams
Best For:
- Teams working with medical imaging data like CT scans and MRIs
- Healthcare professionals and AI researchers annotating medical images
- Users in healthcare AI development
- Projects needing specialized tools for medical image labeling
Contact and Social Media Information:
- Website: monai.io
- LinkedIn: www.linkedin.com/company/projectmonai
- Twitter: x.com/ProjectMONAI
Conclusion
Image annotation is an essential task for many machine learning and computer vision projects. The tools listed here offer a range of features for various annotation needs, from basic object detection and segmentation to complex AI-assisted tasks. Whether you’re working with small datasets or large-scale projects, there’s an annotation tool that can help streamline the process. Selecting the right tool depends on your project’s scale, complexity, and specific requirements, ensuring that you have the right features to create high-quality, labeled data for your machine learning models.