A class agnostic, pixel perfect automated annotation platform. Built for teams with lots of data, strict quality requirements, and little time. Scale your ground truth creation 10x, collaborate with unlimited team members and annotators, and seamlessly integrate it into your deep learning pipeline. V7 Darwin supports medical and scientific imaging, and video.
Explore V7 Darwin
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.
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.
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.
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.
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.
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.
