V7 is built to handle any vision output with equal performance, whether it's simple classification, object detection, keypoint skeleton fitting, or all of the above.
Each training session tests hyper-parameters and image augmentation techniques to find the optimal solution to an open-ended problem.
Accuracy doesn't stop at the first training session. Capture more data in production, route it to a dataset for annotation, and add more knowledge to your model for its next training session.
Learn how to create training data ➜
Run models in the cloud on the scale-agnostic NJORD engine, switch on a webcam, and view the results right from your browser. Testing and running neural networks has never been easier.
Bring AI to life from day 1. With the fast pace of deep learning's state of the art, V7 ensures that on-platform models are the most accurate on the market. By continually updating our architecture, and leveraging an extensively pre-trained backbone, your models will always maximize precision and recall.
Benchmarks were performed on the MS-COCO dataset. COCO is largely composed of large, frame-filling objects that are easy to detect by modern neural network architectures. V7 Neurons outperformed Mask-RCNN primarily on the detection of small and uncommon objects, which matter most in industry cases.
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 labelling 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.
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NJORD is a cloud-based routing system to manage AI model training and inference pipelines for thousands of concurrent requests within V7. Once your models are running, NJORD will monitor their load as users grow. If models are close to their server's capacity, it will spin up a new instance (or back down) to keep them running at any scale.
NJORD also handles the training of V7 models, by allocating GPU resources that match the size of your dataset. As your project grows and relies on more visual data to learn, you won't have to worry about upscaling infrastructure.
V7 is fortunate to be working with partners to bring cognition to the edge at scale with decades of industry experience and world-leading support.
V7's edge deployment is available natively through select partner platforms. Enquire with our team to learn which may best suit your system.
