Image Annotation

One Image Annotation Platform for All Your ML Needs

Create ground truth 10x faster through neural networks and delightful UX

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Annotation types

Complete Any Computer Vision Task

With V7, you’ll not only solve any task your ML project requires. You’ll accomplish it with unprecedented accuracy and speed.

annotation tools

The Ultimate Image Annotation Toolkit

The success of your AI projects relies on quality data. Explore the tools at your disposal and learn how they can help you.

Auto-Annotate

Pixel-perfect polygon masks generated via V7's any-object neural network. Click on object parts to include or exclude them, and fine-tune the model once you have enough training data.

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

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
Cuboid

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
Ellipse

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
Line

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
Keypoint

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

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

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
Plugins

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:
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Annotator performance

  • Track time spent, annotations per minute, and accuracy.
  • Manage your labeling project with accurate real-time data.

QA review

  • Enable consensus workflows to only approve labels where annotators agree.
  • Spot labeling errors and inconsistencies in dedicated quality review stages.

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.

Automate with Data Workflows

Build custom data workflows to generate Ground Truth 10x faster. Collaborate in real-time, version control, and manage all your data assets from one place. Train your AI on V7 or export the datasets in your desired format.

Industry-Specific Tools

Industries building AI through V7

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

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Hear it from our customers

"V7 is really easy to use. It looks good and feels good. The other great thing is the customer support - bugs are fixed, email support is very fast, and feature requests are delivered as promised."

"From the operations perspective—unless we did drastic developments, the scale of the projects requires something like V7 to be functional to make projects happen."

V7 is helping us manage a complicated, intricate, pixel-perfect labeling exercise. Their model-assisted labeling is the best around.

"We use V7 to make our workflow for deep learning training and annotation streamlined and efficient. From the pathologist’s point of view, V7 turned out to be much easier to learn and use than other software - I can easily understand what I’m doing."

"The API is very straightforward to use, so we can easily get data into our local computer system. Annotations are formatted in JSON, which is easier to parse. V7 also offers a lot of pre-trained networks that we can utilize."

"I like the auto-segmentation feature. To me, that’s a nice AI feature that V7 took beyond the gimmick feature - it’s mature enough to be useful."

“Thanks to V7, the image annotation is 30% faster, but realistically, considering the whole process - transferring files and QA - we more than doubled the number of images we can do in the same span of time.”

Managing our data from one place is particularly important for us. Previously, our data was stored in many different formats and in different places. Having a single source makes our data more robust and also greatly reduces the development time for new algorithms, as the learning curve for developers is small.

"V7 did everything that we wanted—it enabled us to label videos in the way we needed, the turnaround time for new features was really fast, and the reviewing and sorting process was much better."

V7 is super sleek, intuitive, and easy to use. Within a couple of minutes, you're off to the races and can annotate quickly. The team is highly responsive and helpful.

What I appreciate the most about V7 is flexible UI and API, responsive support, and active development of new features and bug fixes.

"Visibility on metrics and annotators' work in V7 is very helpful to us, and it's something we didn’t have in our internal solution. The option to check past annotations and review the work is also valuable, along with V7’s ability to interactively define the workflows and the flexibility in task assignment."

"We needed a tool that could do annotating and data versioning because we distribute our tools to farms, and we need to make sure that they have the same version of data for the same models. V7 met our needs."

Having accurately annotated datasets was crucial to catch the typical features of malignant melanoma. V7 lets us visualize the balance of this data across populations.

"Annotation systems are playing catch up trying to project where research and cutting-edge development would be and how to make it configurable. Luckily, solutions like V7 came up, which are maturing in the industry, helping companies like ours to scale up and stay ahead with our R&D"

"We needed a tool that lets us keep the data in one place, annotate, and version it. Having found V7, we decided not to build the internal solution. "

API

Built by ML engineers, for ML engineers

Discover how other AI-first companies solved knowledge tasks at scale with V7

Tools for every stack
Leverage the REST API, integrate with the Python library, or quickly apply mass actions via CLI
Prebuilt integrations
Load datasets into Pytorch, connect your cloud storage, and integrate MLOps tools
Data
Quality
Settings
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Frequently
Asked Questions
Reach out to our support team or contact us for further questions
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 pricing plans do you offer?

V7 offers three pricing plans: Team, Business, and Pro. The team plan starts at $5,000/year. For detailed pricing and feature overview see the V7 pricing page.

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: support@v7labs.com.

Label your own datasets on V7
Try our trial or talk to one of our experts.