Create ground truth 10x faster through neural networks and delightful UX.
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 ->
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 ->
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 ->
Generate Ground Truth 10x faster by creating pixel-perfect annotations. Use V7’s intuitive tools to label data and automate your ML pipelines.
The success of your AI projects relies on quality data. Explore the tools at your disposal and learn how they can help you.
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.
Used for finding the contour of an object like a silhouette or defining an amorphous entity like “sky” or “road".
A lightweight vector, but drawn or erased like a painting tool. Handy for creating complex polygons and holes.
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.
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.
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.
Bounding boxes are used for training detectors. They’re less precise than polygon masks but may prove cheaper to compute.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Streamline the labeling process with orientation markers, reference lines, histograms, color maps, or contrast control.
Use V7’s class-agnostic neural network to label your image datasets in a snap.
Enrich your labeled data with sub-annotations and attributes. Add text, instance ID, directional vectors, and more.
Hire professional image labelers who care about your data. We'll manage the entire labeling project for you.
From JPG to PNG to DICOM to NIfTI—V7 supports the entire gamut of image file formats.
Add consensus stages into your data training workflow and automate the QA process. Compare models or labelers with AI consensus.
Use V7’s powerful analytics and customization to track progress and adapt the platform to your specific labeling needs.
Track time spent, annotations per minute, and accuracy
Manage your labeling project with accurate real-time data
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
Inspect annotations created manually for training data, or by models in inference
Smoothly navigate files with thousands of labels from your browser
Solve any labeling task 10x faster, train accurate AI models, manage data, and hire pro labelers that care about your computer vision projects
Yes, V7 offers a free trial version that allows you to test its features and capabilities before making a purchase. However, to unlock all the functionalities and all the potential that the platform offers, it is worth considering one of the paid plans. You can see the full feature breakdown and pricing of V7 here.
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.
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.
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.
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.
Medical image annotation involves labeling the medical imaging data such as X-Ray, Ultrasound, MRI, CT Scan, etc., for training machine learning models.
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.
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: email@example.com.