Solve any labeling task 10x faster with an API for image annotation and dataset management
Help your machine learning teams keep track of their most valuable assets. Ensure your team understands your labels and edge cases.
Perform actions on your assets via a CLI/SDK rather than the UI. Create, manage, export, and import your datasets and data conveniently with a single command.
Upload your video data or ultra-high resolution images—browse and annotate them using one platform. V7 supports JPG, PNG, TIF, MP4, MOV, SVS, DICOM, and more.
Stay on top of your annotation versions. Reference each model to a dataset version as your datasets keep growing.
Instantly preview all images and videos thanks to a highly efficient Elixir architecture. Filter by upload time, labeler, tag, class, or progress.
Create pixel-perfect annotations on any data format with class-agnostic, automated tools. Boost your annotation speed by up to 10x.
Create pixel-perfect polygon masks using V7's class-agnostic neural network — instantly and without prior training. All you need to do is click on object parts to either include or exclude them. Fine-tune your model once you gather enough training data.
Easily annotate all Video, Image, and Text data. Choose from a variety of tools, such as polygons, brush, or bounding boxes. Solve any CV task from image classification to instance segmentation to object tracking and more.
Track the time spent, see how many annotations are created per minute, check the total number of labeled images, monitor accuracy, and more. No matter if you have 10 or 1,000 labelers.
Annotate images in collaboration with other users and add multiple labels to a single video or image simultaneously. Communicate in real time to make sure your labels are accurate and all team members know what the ground truth looks like.
Experience V7’s powerful model training and automated labeling capabilities. Use our pre-trained models or bring your own.
Customize your workflows quickly and flexibly. Add annotations manually, include the model stage to automate parts of or the entire labeling process, decide when reviewers step in—see what works best for you.
Turn your images or datasets into models on V7. At a click of a button.
If you have a repository of your own models, you can easily upload them to the V7 platform’s storage and keep them in order.
Keep all your company models in one place. Deploy them for model-assisted labeling tasks or use them to train new AutoML models.
Automate your workflows while making sure the right task gets assigned to the right person. Eliminate spreadsheets, data leaks, or quality control issues.
Entrust your training data to expert labelers. V7 gives you access to 5,000+ highly trained annotators who know the platform inside out. They’ll help you scale your project while following top-quality standards.
Medical, scientific, agricultural, construction, or autonomous driving projects? You name it! We collaborate with experts from a variety of industries to ensure data accuracy and reliability.
Working on sensitive data? V7 lets you assign tasks to specific labelers and make them only see the task at hand. Our platform is SOC2, HIPAA, and ISO27001 compliant.
Cooperate with a dedicated V7 project manager. Design the most effective and efficient workflows and set your quality standards. The manager will see to it that all deliverables match your expectations.
Discover how other AI-first companies solved knowledge tasks at scale with V7
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“Thanks to V7, the image annotation is 30% faster, but realistically, considering the whole process - transferring files and QA - we more than doubled the number of images we can do in the same span of time.”
Managing our data from one place is particularly important for us. Previously, our data was stored in many different formats and in different places. Having a single source makes our data more robust and also greatly reduces the development time for new algorithms, as the learning curve for developers is small.
"We use V7 to make our workflow for deep learning training and annotation streamlined and efficient. From the pathologist’s point of view, V7 turned out to be much easier to learn and use than other software - I can easily understand what I’m doing."
"The API is very straightforward to use, so we can easily get data into our local computer system. Annotations are formatted in JSON, which is easier to parse. V7 also offers a lot of pre-trained networks that we can utilize."
What I appreciate the most about V7 is flexible UI and API, responsive support, and active development of new features and bug fixes.
"Visibility on metrics and annotators' work in V7 is very helpful to us, and it's something we didn’t have in our internal solution. The option to check past annotations and review the work is also valuable, along with V7’s ability to interactively define the workflows and the flexibility in task assignment."
V7 is super sleek, intuitive, and easy to use. Within a couple of minutes, you're off to the races and can annotate quickly. The team is highly responsive and helpful.
V7 is helping us manage a complicated, intricate, pixel-perfect labeling exercise. Their model-assisted labeling is the best around.
"I like the auto-segmentation feature. To me, that’s a nice AI feature that V7 took beyond the gimmick feature - it’s mature enough to be useful."
"V7 is really easy to use. It looks good and feels good. The other great thing is the customer support - bugs are fixed, email support is very fast, and feature requests are delivered as promised."
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"We needed a tool that could do annotating and data versioning because we distribute our tools to farms, and we need to make sure that they have the same version of data for the same models. V7 met our needs."
"V7 did everything that we wanted—it enabled us to label videos in the way we needed, the turnaround time for new features was really fast, and the reviewing and sorting process was much better."
"We needed a tool that lets us keep the data in one place, annotate, and version it. Having found V7, we decided not to build the internal solution. "
Having accurately annotated datasets was crucial to catch the typical features of malignant melanoma. V7 lets us visualize the balance of this data across populations.
