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.
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 welcome! 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.
"We use this thing every day, it's like, really great. Our data is very important to us."
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.
Annotation follows a pipeline where images are marked for review before they are complete. Reviewers can comment on errors and assess each image.
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.
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."
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.
Each label applied and reviewed by a human contributes to a periodic learning of Auto-Annotate and Auto-Complete models.
Every model trained for labelling automation becomes available throughout your team, testable through the browser, and usable in labelling.
Periodically load new versions of your dataset to your framework of choice and continuously improve your AI.
"Wow, annotation is rad."
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.
