V7 develops the most comprehensive toolkit for managing datasets, annotating images, training models, and deploying them in production from the same source. You won't have to leave your browser or work your way through compatibilities. Integrate your in-house machine learning pipeline, or start from scratch. V7's technology is built with medical and scientific usage compatibilities, including regulatory compliant annotation 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."
One of our goals at V7 is to automate our own jobs as machine learning scientists. Our platform selects the right hyper-parameters, augmentation flow, data splits, and more to give you the same value as a small team of deep learning engineers.
We know the pain of logging each trained model's stats. V7 does that for you, generating performance metrics, confusion matrices, and allowing you to view the test images where your model performs well or badly.
The user experience of training models isn't something most people think about. We do.
Get notified when your models are ready, share their performance, and never look back at your old ways.
Load the right dataset version with a single command into your favourite framework, or train immediately with Auto-Train.
Compatibility troubleshooting is a thing of the past, claim those hours of your life back.
"Wow, annotation is rad."
It just works - Once your models are trained you can run them directly through your computer's webcam.
Once your models are running, you can obtain rich, live data on what they recognize and use them an API for your service.
Test your model, or port it to mobile to run it through an app.
You will have a portable AI camera to demo your new model with no additional work.
V7 is here to support your AI-enabled product's success. Once your neural networks are running you can integrate them into your products.
"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.
V7 guides you through the creation of your machine learning product with a connected ecosystem of tools. You can make use of one, or all of them as you see fit for your project.
