Instruments of Creation for Visual Intelligence

The Most Powerful Toolkit for Creating Ground Truth

Built for teams with lots of data, and little time. Create perfect segmentation masks semi-automatically, scale your ground truth creation 10x, and seamlessly integrate it into your deep learning pipeline. V7 Darwin supports medical and scientific usage compatibilities, including regulatory compliance and format supports.


Data become an organized, collaboratively usable resource on the fastest dataset management platform for machine learning.

Butter smooth browsing

We are serious about presenting your data with no loading times. Uploads, filters, and search queries will always be delivered in real-time, no matter how large your dataset.

Format friendly

DICOM, .TIF, .MOV, or other uncommon formats are welcome! From electron microscopes to 4k cameras, most image data will work on V7.

Annotate, train, export, collaborate, and share

Datasets are at the core of your AI projects - use them to train networks, iterate new versions to improve performance, grow them with your team, or share them with the wider community.


V7 was built in Elixir, an Erlang-based language to handle massive scale concurrency between millions of users moving billions of images.
Whether you are uploading, exporting, annotating, or partitioning, your data will always be swiftly available.

"We use this thing every day, it's like, really great. Our data is very important to us."


Tell your AI what to learn, and use generalized models to achieve pixel-perfect results. Annotate yourself or hire a team through V7 when you scale up.


Save up to 80% of your annotation time while matching human performance. V7's Auto-Annotate tool works on any object, big, small, partial, or compound. Try it to believe.

Quality Review

Annotation follows a pipeline where images are marked for review before they are complete. Reviewers can comment on errors and assess each image.

Outsource or BYO

Upload your data and let us take care annotations through experienced annotators. You can track its progress and quality and train models on it as it grows.
If you have your own team of annotators, you can add them all, for free, with no limitations.

Expandable Tools

Tags, bounding boxes, polygons, masks, directional vectors, attributes are available from the get-go.
V7's toolkit keeps expanding, and if you need something special you can add new annotation types and tools as plugins.

"Wow, annotation is rad."


We believe in active learning. Train your AI as your dataset grows, and let it complete your work.


Self explanatory.
V7 Darwin selects the right hyper-parameters, augmentation flow, data splits, and trains the best model to complete your dataset with minimal human input.

Continuous Fine-Tuning

Each label applied and reviewed by a human contributes to a periodic learning of Auto-Annotate and Auto-Complete models.

A Model Library

Every model trained for labelling automation becomes available throughout your team, testable through the browser, and usable in labelling.

To Your Framework

Periodically load new versions of your dataset to your framework of choice and continuously improve your AI.

"Wow, annotation is rad."

Create the Sense of Sight

Explore some of the applications you can develop on V7. Harness AI to turn industries and products into sighted, intelligent systems.

Develop a compliant cancer decision support system

Add oncologists to your team and minimally involve them in the annotation workflow to develop FDA compliant cancer-detecting AI. Once the expert's input is added, use of V7's annotation network to complete pixel-perfect annotations of medical images.

Detect multiple imperfections in real-time for manufacturing quality control

Label imperfections in images and train a robust neural network with Auto-Train. Test it on the production line through a webcam or iOS device and later deploy it on a server to match your production line speeds.

Recognize and track objects in a chemical laboratory

Develop an object detection for items you work with to automatically document their involvement in protocols. Use V7 to understand object presence, orientation, and components.

Analyze and count cells in microscopy images

Load microscopy images into V7 and use Auto-Annotate to quickly create a segmentation dataset of cells and organelles, then train a neural network to instantly detect cell count, shape, and appearance.

Detect Macular Degeneration in Ophthalmological Images to Prevent Blindness

Segment capillaries and retinal spots with Auto-Annotate, then train a robust semantic segmentation model to obtain a mask of the eye's capillaries and a detector for retinal spots. Combine the two to diagnose AMD and diabetic retinopathy, the leading causes of blindness.

Develop your next game changing idea

Use V7 to give the sense of sight to any device or service. Neural networks are trained to learn the data you feed them, meaning you can develop an AI for any scenario that can be captured in images or video.

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Schedule a demo with our team.