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.
manage their datasets on V7 Darwin
Save up to 90% of your annotation time while matching human performance. V7's Auto-Annotate tool works on any object, big, small, partial, or compound. See for yourself to believe.
Easily customize workflows by adding multiple annotation stages, reviews, or define chances of random sampling for specific QA steps.
Upload your data and let us take care annotations through experienced workforce partners. You can track its progress and quality and train automation 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, keypoint skeletons, attributes and more are available from the get-go.
V7's toolkit keeps expanding (see our changelog!). You can integrate new tools as plugins.
create pixel-perfect ground truth on V7 Darwin
Vision AI is changing. Leverage an extensively pre-trained backbone to enable your model to learn with very few training samples.
Your product is now your model-in-the-loop. Use your trained model as a labelling stage and let humans fill in the gaps.
Trained your own Pytorch or ONNX model? Host it on V7's NJORD GPU engine to guarantee 99.99% uptime and infinite scalability. Zero dev-ops engineers needed.
Neural networks instantly turn into a REST API to call from any device you wish.
automate their work through V7Darwin
How to Label Medical Images for Machine Learning. Radiologists annotate (or markup) medical images on a daily basis. This can be done in DICOM viewers, which contain basic annotation capabilities such as bounding boxes, arrows, and sometimes polygons.
“There isn't a labeled dataset of this level of scale and diversity out there,’’ says Rizzoli, the co-founder of V7. “What we've seen is companies have rushed to the media to publish results on models that use the whole picture of an X-ray and perform classification.”
