Image Annotation

One Image Annotation Platform for All Your ML Needs

Create training data 10x faster through generative AI and delightful UX.

Andrew Achkar
Andrew Achkar
Technical Director at Miovison
Visibility on metrics and annotators' work in V7 is very helpful to us. The option to check past annotations and review the work is also valuable.
Read case study ->
Francesca Donadoni
Francesca Donadoni
Data Scientist at CattleEye
Having a single source of data makes it more robust and greatly reduces the development time for new algorithms, as the learning curve for developers is small.
Read case study ->
Mark Robson
Mark Robson
Technical Specialist, MTC
V7 sped up our labeling 9–10x compared to VGG. The appeal of using V7 is that it's commercial off-the-shelf, very intuitive, and easy to use for non-technical people.
Read case study ->

Model Types You Can Train

Pick a model type, train, and run your models in the cloud on V7’s scale-agnostic engine or your own instance. Switch on a webcam, and view the results right from your browser.

Image Classification
Label or classify images and whole datasets in the blink of an eye.
Multi-label classification
Assign multiple labels or classes to a single image.
Object Detection
Add bounding boxes to any image, video, or medical format.
Semantic Segmentation
Automatically segment objects with pixel-perfect accuracy.
Instance Segmentation
Distinguish between different instances of similar objects.
Panoptic Segmentation
Perform instance and semantic segmentation simultaneously.
Volumetric Medical Segmentation
Segment structures or objects from volumetric medical data, such as CT or MRI scans.
Pose Estimation
Determine the position, orientation, and pose of objects or people in images and videos.
Text Recognition
Convert images of typed, handwritten, or printed text into machine-readable text.
Annotation tools

The Ultimate Suite of Image Annotation Tools

Scalable, production-ready solutions tailored to enterprise-level projects. Explore the available tools and discover how they can assist you.


Click on an object to segment it with V7's pixel-perfect polygon masks. Use auto-labeling to detect and segment multiple instances in seconds.

Good for:
Semantic/instance/volumetric segmentation
Any visible object, known or unknown
Object detection
Composite objects

Used for finding the contour of an object like a silhouette or defining an amorphous entity like “sky” or “road".

Good for:
Semantic/instance/volumetric segmentation
Semantic objects, like floor surfaces
Any visible object
Object detection
Composite objects
Brush and Eraser Tool

A lightweight vector, but drawn or erased like a painting tool. Handy for creating complex polygons and holes.

Good for:
Composite objects, thin objects, or objects with holes
Semantic objects, like floor surfaces
Semantic/instance segmentation
Object detection

Used for 3D and 6 degree-o-freedom object detectors. Cuboids are 3-dimensional bounding boxes that can enclose an item, defining its dimension and pose in a captured scene.

Good for:
Objects on flat planes that need to be navigated, such as cars
Objects that require robotic grasping
3D cuboid and 6DoF pose estimation
Object detection

An oval that appears as a two-diameter circle or multi-point polygon on export. Can be used as a round polygon or as an ellipse to mark circular objects that may distort with perspective.

Good for:
Circular or oval objects
Items that can be regressed with 2-3 values (diameters, centroid)
Instance and semantic segmentation
Ellipsoid regression

A series of points forming a line that can be used for defining a slope, direction, or edge. This tool comes in handy for lane markings or trajectories.

Good for:
Linearly defined objects with no volume, such as edges
Non-visual objects such as mid-points or trajectories
Regressor networks
Keypoint detectors
Bounding Box

Bounding boxes are used for training detectors. They’re less precise than polygon masks but may prove cheaper to compute.

Good for:
Uniformly shaped objects
Objects that don’t overlap
Low-compute projects

Defines a point that may represent an object or marker. Keypoints can be used individually or in groups to form a point map to define the pose of an object.

Good for:
Object markers, such as face points or corners
Non-visual objects, such as the inner midpoint of an object
Keypoint detection and mapping
2D and 6DoF object pose
Keypoint Skeleton & Custom Polygons

A network of keypoints connected by vectors. Used for defining the 2D or 3D pose of a multi-limbed object. Keypoint skeletons use a defined, moveable set of points that adapt to the object’s appearance.

Good for:
Human, animal, or object pose
Objects with a defined number of marker points
Pose estimation
Classification Tags

Tags describe the whole image for classification purposes. Unlike other annotations, they don’t apply to an image area. They describe features of an image as a whole, or define an image category.

Good for:
Visual features that are present but not positional, such as “overexposed”
Objects taking up 50% or more than an image
Image categories, such as “indoor”
Image or video classifiers

Attributes refer to tags on an annotation. They describe objects in greater detail than the class name itself. They can define discrete or continuous features, such as color or age. Attributes help AI classify objects after detecting them.

Good for:
Any object with relevant variable traits
Any model that can support an attribute-defining head
Instance Tracking IDs

Instance IDs (also referred to as “object IDs” or “tracking IDs”) let you re-identify a specific object throughout a dataset. Each annotation is given a numeric ID so the object may be tracked or distinguished from other objects of the same class in an image.

Good for:
Datasets where tracking is important
Instance/3D volumetric segmentation
Video object tracking

Text can be used to train OCR or to include unique textual information about an annotation. Unlike attributes, the text isn’t saved to be re-applied to other annotations.

Good for:
Entering freeform text, such as in clinical notes or transcribed text
Optical Character Recognition (OCR)
Text detection
Directional Vector

Defines a value between 0 and 360 to indicate the 2D pose or direction of an object. These vectors can be used to predict movement or define an object’s tilt.

Good for:
Solid objects with a relevant direction or pose
Object Direction or 2D object pose

When your team's needs go beyond the standard tools, you can write plugins to add new functionalities and annotation types, giving you the freedom to expand the platform.

Good for:
With V7 workflows, it’s very easy to see the project's status - I know how far along the labelers are.

I know what’s in review and what’s completed. We can see all status changes happening in real-time. That is probably my favorite V7 feature.

Maleeha Nawaz
Manager of Quality and Data Curation
Try V7 Now->
Ryan Watson
Segmentation Manager at Intelligent Ultrasound

Transform Your ML Training Data Ops

Create and automate data workflows, collaborate in real-time, QA review, version control your datasets, and get full visibility into every aspect of your ML pipeline.

Request a demo
ML-assisted labeling

Leverage model-in-the-loop to label your data faster. Use V7’s model tool to invoke your own model or pick one from V7’s public models library.

Custom Data Workflows

Build and automate data workflows to streamline your annotation projects. Add multiple review, model, consensus, logic, webhooks, and annotation stages, and assign user roles to efficiently manage your resources.

Advanced Image Manipulation

Streamline the labeling process with orientation markers, reference lines, histograms, color maps, or contrast control.

Auto-Annotate Tool

Use V7's foundation models to generate annotations on any object in one click.

Annotation Properties

Enrich your labeled data with sub-annotations and attributes. Add text, instance ID, directional vectors, and more.

Labeling Services

Hire professional image labelers who care about your data. We'll manage the entire labeling project for you.

Support for All Image Types

From JPG to PNG to DICOM to NIfTI—V7 supports the entire gamut of image file formats.

Automated QA

Add consensus stages into your data training workflow and automate the QA process. Compare models or labelers with AI consensus.


Manage and Monitor Your Labeling Projects on One Platform

Use V7’s powerful analytics and customization to track progress and adapt the platform to your specific labeling needs.

Annotator performance

Track time spent, annotations per minute, and accuracy

Manage your labeling project with accurate real-time data

Custom layouts

Bundle files into one task, or import DICOM files as hanging protocols

Combine files of different types into one UI to enable multi-modal data labeling

Balance your data

Inspect annotations created manually for training data, or by models in inference

Smoothly navigate files with thousands of labels from your browser

Industry-Specific Tools

Industries Building AI through V7

Find out whether your industry's image formats can be used to automate tasks through V7.


Manage Your AI Data in a Single Ml-Ops Platform

Solve any labeling task 10x faster, train accurate AI models, manage data, and hire pro labelers that care about your computer vision projects.

Video Annotation
Play any format, with hundreds of annotations - without compromising on performance.
See Video Annotation ->
DICOM Annotation Tools
Speed up medical image annotation with specialized features for CT/MRI scans, ultrasound videos, and X-rays.
See DICOM Annotation Tools ->
Document Processing
Bring your IDP to 99% with intelligent document processing.
See Document Processing ->
Enhanced Auto Annotation
Create pixel-perfect polygon masks in seconds - powered by our enhanced auto annotate feature.
See Auto Annotation ->
Dataset Management
We allow you to manage your training data securely and simply.
See Dataset Management ->
Image Annotation
Powerful features, simple automations, and reliable real-time performance.
See Image Annotation ->
Model Management
Train models on V7 or connect your own, and experience the impact of a powerful data engine.
See Model Management ->
Flexibly route your training data for maximum efficiency and accuracy.
See Workflows ->
Slack conversation of annotation projects team members
Annotation Services
Hire human experts for maximum accuracy, with the support of our team.
See Annotation Services ->
Ready to get started?
Try our trial or talk to one of our experts.
Asked Questions
Reach out to our support team or contact us for further questions
Can I test V7 before making a purchase?

Yes, V7 provides a complimentary free tier that lets you explore some of its basic features and capabilities. However, to access the full suite of advanced functionalities and harness the platform’s complete potential, it is worth considering one of the paid plans. You can see the full feature breakdown and pricing of V7 here.

Can you integrate the trained model into our existing system?

The V7 platform allows you to bring your own custom models, hosted on your own infrastructure. You can use them alongside the models trained using V7's own neural networks. The minimal requirements for the custom models are that they must be exposed via HTTP and make predictions in the form of JSON.

Can multiple people label the same asset in V7?

Yes, multiple people can label the same asset in V7, making it a powerful collaboration platform for your data labeling projects. V7 also includes comment tools, user permissions, or consensus stages that measure the level of agreement between different annotators, allowing you to quickly identify any discrepancies in annotations. These features help to improve the quality of your data labeling process and ensure that your annotations are accurate and consistent. With V7, you can manage large-scale data labeling projects with many collaborators.

What type of data does V7 support?

V7 supports image, video, and text data. The file formats you can use with V7 include: JPG, PNG, MP4, MOV, AVI, BMP, SVS, TIFF, DCM, ZIP, DICOM, NIfTI.

What is medical image annotation?

Medical image annotation involves labeling the medical imaging data such as X-Ray, Ultrasound, MRI, CT Scan, etc., for training machine learning models.

Does V7 offer labeling services?

Yes. V7 works with a trusted network of partners and professional annotators who will help you turn your data into ground truth. Go to V7 Labeling Services to submit the form and we will send you a proposal within hours.

What type of support does V7 offer?

We offer in-app chat support and email technical support to all of V7 users. We will make sure to take good care of you and your team. You can get in touch with us at: