Spot damage, scratches, cracks, dents, or missing pieces as small as 0.01% of an image. Detect unforeseeable defects that differ in shape from previously collected examples.
V7 allows developers to turn images and video of your defects into state of the art vision AI with semi-automated tagging, model training, and inference deployment.
Get started today by automating the detection of anomalies in your production line or manufacturing process, and bring the growing value of AI to your supply chain.
Hover over the image below to see what the AI identifies
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No two defects are alike, but they generally fall within broad categories of appearances:
AI performance shouldn't stop at the first training session.
Sync your product with a dataset and continually improve your AI performance by turning its output into new ground truth. Continual learning will allow your models to surpass 95% accuracy barriers and reach 99% and above as more data from your use case or product is supervised by humans, and learnt by your AI.
Bring AI to life, leave the R&D to us. With the fast pace of deep learning's state of the art, V7 ensures that your models are the most accurate on the market. By continually updating our architecture, and leveraging an extensively pre-trained backbone, your models will always outperform competitors whilst saving you costly R&D efforts.
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.
Deploy model in the cloud on V7's scale-agnostic inference engine NJORD, or run them on-premise through a docker image at real-time speeds. Run V7 Neurons on powerful NVIDIA T4 servers in your datacenter to serve multiple cameras, or on versatile NVIDIA Jetson modules for low volume or low-power consumption.