Easy to use. Scalable. With world-class support. V7 lets you automate your ML pipeline—annotate data, train models, and create ground truth faster.
We had our own system, but we wanted it to accomplish additional tasks, like create new annotations types or annotate videos—activities that V7 helped us complete.Read case study ->
Thanks to V7, the image annotation is 30% faster, and considering also the QA process, we more than doubled the number of images we can label.Read case study ->
We label terabytes of data and from the operations perspective, the scale of the projects requires a reliable training data platform like V7 to succeed.Read case study ->
Automate labeling and gain unparalleled control of your annotation workflow. Scale your ground truth creation 10x today.
Switch to a fast, intuitive, end-to-end training data platform.
Customize your workflows for consensus. Label your images accurately and consistently to meet strict standards for medical research or clinical purposes.
Label data 10x faster with V7’s class-agnostic auto-annotate tool. Create pixel-perfect polygon masks for any object in seconds, and create Ground Truth 10x faster.
Meet all FDA, SOC2, and HIPAA requirements. Trace the history of data reviews, including annotation authorship.
Label in DICOM, SVS, 16-bit, PDF, and maintain the integrity of your original file in the video or volumetric data.
Use Python to streamline your data processing and model training. Use a flexible CLI & REST API to create advanced pipelines that meet all your needs!
Turn your labeled data into models and use them to label more data. Run and improve models iteratively. Register your own model via REST API.
Connect external storage, collaborate in real-time, label, manage, and push data externally in a few clicks. Integrate easily with our flexible API.
Gain insights into your data at every stage of its journey. Get access to annotators and models performance reports. Spot bottlenecks and resolve them without communication overhead.
Start labeling right away. Thanks to V7’s intuitive UI, you’ll get your bearings the moment you open the app.
V7’s dataset management tool was developed with users in mind. Clean interface, quick preview options, detailed stats—all these make a difference.
Plug in your data, set up a workflow, and start annotating. With V7, you’ll get your labeling project up and running in a matter of minutes.
You will never walk alone. If you experience any technical issues, our support teams are just a click away.
V7 is the ultimate toolbox for your organization’s machine learning needs. Explore the set of features that will streamline your AI projects.
V7 supports SuperAnnotate's native file formats. See how easy it is to switch to V7 in minutes.
It fits our business needs best due to its customizable workflows, simple setup, consensus stages, and the ability to monitor annotator performance and project progress. It's also very intuitive and stable.
Learn why 15.000+ ML engineer chose V7 over SuperAnnotate for their computer vision projects
Add thousands of annotations with V7’s dense annotation support.
Work on high-resolution imagery without performance issues.
Filter your images and videos by status, class, tag, or upload time, and preview them instantly.
Stay organized and access your data with powerful management, analytics, and automation tools.
Customize your workflows for consensus to ensure consistency for medical research and clinical purposes.
Meet strict research standards with V7's advanced workflows and precise image labeling capabilities.
V7 can process various types of data, including images, medical imaging files, videos, volumetric series, and documents. The exact types of data that can be processed may depend on the specific use case and requirements. However, V7 supports the majority of visual data formats.
You can use V7’s Darwin-py SKD to interact with the platform via CLI or use it as a Python library. The full documentation and API reference is available in the V7 resource hub.
If you decide to train your computer vision models on our platform, all you need to do is complete at least 100 annotations of a specific class. Then, you can pick the datasets and classes to train object detection, classification, or instance segmentation models with no additional steps.
V7 offers three pricing plans: Team, Business, and Pro. The team plan starts at USD 5,000/year. For detailed pricing and feature overview, see the V7 pricing page.
Yes, V7 is equipped to handle large volumes of data for annotation. It offers efficient and scalable data management capabilities, making it a great solution for organizations with large datasets.
V7 can perform various types of annotations for tasks, including object detection, semantic segmentation, and image classification. There are multiple annotation classes, such as polygon masks, bounding boxes, keypoint skeletons for human pose estimation, etc.
V7 is a data training platform that focuses on automation and ease of use. It offers advanced machine learning capabilities, and V7’s key feature, Auto-Annotate, utilizes AI to quickly segment objects in an image, reducing the time required to make annotations by up to 10x.
V7 uses a system of credits that are consumed when you use specific tools or perform operations such as model training. When it comes to data labeling services, the cost depends on several factors, such as the size of the dataset, the complexity of the annotation tasks, and your deadline. Therefore, it's best to reach out to V7’s team to discuss your requirements and get a quote.
Customers who have used V7 have generally reported positive experiences with its automation and advanced machine-learning capabilities. V7 receives highly positive reviews on G2, a leading software review platform. Customers praise its features, ease of use, and overall effectiveness.
There is no limit to the amount of in-house labeling that can be performed using V7. It is designed to be flexible and scalable to meet the needs of various organizations and projects.
Yes, V7 can be used for in-house data labeling as it offers advanced data annotation features and can be integrated into the machine-learning team's workflow. In-house labeling is the default mode. It is a flexible and efficient platform for managing your data labeling process. But if you need help, our team can provide professional data labeling services too.
The time required to train a custom model depends on various factors, including the size of your data, the complexity of the model architecture, and the resources available for training. If you want to train or test V7 models on the platform itself, our Models feature allows you to train a model within several minutes.
V7 provides version control capabilities that enable you to manage and track changes to your models and annotated data over time. You can also use the export/import feature to revert to previous versions of your annotations if needed.
V7 takes the security and privacy of sensitive or confidential data very seriously—it’s SOC2, HIPAA, and ISO27001 compliant. It provides robust security features and implements industry-standard encryption techniques to ensure the protection of your files. Additionally, it offers flexible access controls that enable you to manage who can view and access your training data.
Yes, V7 offers a free trial version that allows you to test its features and capabilities before making a purchase. However, to unlock all the functionalities and all the potential that the platform offers, it is worth considering one of the paid plans. You can see the full feature breakdown and pricing of V7 here.
Yes, you can use V7’s data labeling service. Alternatively, you can use our computer vision models for specific tasks. For example, V7 offers several models for document processing to annotate invoices, receipts, and other documents. You can also simplify the annotation process by utilizing V7's AI-powered feature called Auto-Annotate. This feature automatically segments objects in a selected part of your image, reducing the time required to make annotations.
The V7 platform allows you to bring your own custom models, hosted on your own infrastructure. You can use them alongside the models trained using V7's own neural networks. The minimal requirements for the custom models are that they must be exposed via HTTP and make predictions in the form of JSON.
Yes, multiple people can label the same asset in V7, making it a powerful collaboration platform for your data labeling projects. V7 also includes comment tools, user permissions, or consensus stages that measure the level of agreement between different annotators, allowing you to quickly identify any discrepancies in annotations. These features help to improve the quality of your data labeling process and ensure that your annotations are accurate and consistent. With V7, you can manage large-scale data labeling projects with many collaborators.
Yes, V7 can be used with data stored on a server. V7 is designed to be flexible and can be integrated with various types of data storage solutions.