The world of AI moves fast, don't let ground truth slow you down.
We've spoken to hundreds of ML teams to create a labeling environment that will keep up with the most ambitious projects in AI. V7 automates labelling, enables unparalleled control of your annotation workflow, helps you spot quality issues in your data, and integrates seamlessly into your pipeline. On top of all of that, its user experience matches our maniacal attention to detail coupled with excellent technical support. Deep learning scientists love it, annotation workforces love it, you'll love it too.
Scale your ground truth creation 10x today.
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.
DICOM, .TIF, .MOV, or other uncommon formats are supported. From electron microscopes to 4k cameras, most image data will work on V7.
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.
manage their datasets on V7 Darwin
Save up to 90% of your annotation time while matching human performance. V7's Auto-Annotate tool works on any object, big, small, partial, or compound. See for yourself to believe.
Easily customize workflows by adding multiple annotation stages, reviews, or define chances of random sampling for specific QA steps.
Upload your data and let us take care annotations through our professional workforce. You can track its progress and quality and train models to automate part of the task.
If you have your own team of annotators, you can add them all too!
Tags, bounding boxes, polygons, masks, directional vectors, keypoint skeletons, attributes and more are available from the get-go.
V7's toolkit keeps expanding (see our changelog!).
create pixel-perfect ground truth on V7 Darwin
Vision AI is changing. Leverage an extensively pre-trained backbone to enable your model to learn with very 100 training samples, and automate your task.
Use models to label any object by mapping their classes to your dataset's.
Pick any model to pre-process datasets by adding the classes that matter to you.
V7's GPU orchestration grants 99.99% uptime and infinite scalability. Zero dev-ops engineers needed.
Host hundreds of models and run them on demand in one click.
Neural networks instantly turn into a REST API to call from any device you wish. Combined with workflows, you can develop complex multi-model products in a few clicks.
automate their work through V7Darwin
Millions of medical images were processed by AI and human doctors this year so far. V7 win's the UK's most prestigious AI conference award.
There are a record number of 9,977 machine learning startups and companies in Crunchbase today, an 8.2% increase over the 9,216 startups listed in 2020 and a 14.6% increase over the 8,705 listed in 2019. V7 allows vision-based A.I. systems to learn continuously from training data with minimal human supervision.
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Founded in 2018 by Alberto Rizzoli and Simon Edwardsson, V7 uses Elixir, Phoenix, and Cowboy to power their web platform, responsible for managing large amounts of data and orchestrating dozens of Python nodes to carry out machine learning jobs.
Millions of medical images were processed by AI and human doctors this year so far. V7 win's the UK's most prestigious AI conference award.
Founded in 2018 by Alberto Rizzoli and Simon Edwardsson, V7 uses Elixir, Phoenix, and Cowboy to power their web platform, responsible for managing large amounts of data and orchestrating dozens of Python nodes to carry out machine learning jobs.
There are a record number of 9,977 machine learning startups and companies in Crunchbase today, an 8.2% increase over the 9,216 startups listed in 2020 and a 14.6% increase over the 8,705 listed in 2019. V7 allows vision-based A.I. systems to learn continuously from training data with minimal human supervision.
