V7 sped up our labeling 9–10x compared to VGG. The appeal of using V7 is that it's commercial off-the-shelf, very intuitive, and easy to use for non-technical people.Read case study ->
We use V7 to make our annotation and model training workflows more efficient. It's much easier to use than other software thanks to its intuitive UI.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 ->
Talk to our team about solving your training data challenges in 100+ use cases.
Automate labeling and gain unparalleled control of your annotation workflow. Scale your ground truth creation 10x today.
You can rest assured that your data remains protected and private, because the security of your data is our top priority
Workflows is V7’s way of helping you structure your ML pipeline—from uploading your data, labelling and reviewing it, to training accurate AI models in hours instead of weeksExplore V7 Workflows ->
Speed up Ground Truth creation by 10 times with V7's efficient data labeling. Improve your model accuracy and automate your ML pipeline with V7's end-to-end workflow orchestration
Leverage video annotations to use models or smart labeling features to annotate your footage.Try V7 Now ->
Our cloud visualization platform lets you see thousands labels across video frames, whether they’re made by human labelers or our models.Try V7 Now ->
Track players across time and re-identify them when they exit and re-enter the frame.Try V7 Now ->
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.
Host data privately in your enterprise cloud storage. Send tasks to other ML-Ops platforms once completed. Load datasets into your deep learning framework of choice in a single step.
Check out the annotation types that V7 offers
Detect and locate the presence of multiple objects within an image, drawing bounding boxes around them to indicate their position and size. Export your labeled data in the desired format or train object detection models on V7 to label more data.
Leverage V7 annotation tools to detect and delineate individual object instances within an image, and assign a unique label to each pixel that belongs to that instance. Easily create classes with attributes, text, directional vectors, and instance IDs. Train instance segmentation models on V7 in a click.
Use V7 to combine both semantic and instance segmentation to assign a unique label to every pixel in an image, including objects and surrounding context to solve panoptic segmentation tasks. Add and manage classes along with attributes, text, directional vectors, and instance IDs to enrich your annotations.
Use V7 for image tagging—identify and assign a label or multiple labels to images based on the presence or absence of specific features or patterns within the image. Add tags in bulk, and easily filter your data by classes, annotation authors, statuses, and more.
Detect and locate key points or joints within an image, such as human body joints or face features, and estimate the 2D and 3D poses or configurations of those points. Use V7 keypoint skeleton editor in both images and videos, and interpolate your labels to speed up your annotation process.
Solve video classification tasks and use V7’s tags to label whole videos or individual frames based on their content or features. Upload videos at their native frame rate or as separate images. Create and assign tags in bulk, and filter your data by classes, annotation authors, statuses, and more.
Divide an image into distinct regions or segments, and assign labels representing the category of objects or features they belong to, to each individual pixel. Label data manually or use V7 auto annotation to achieve pixel perfect accuracy.
Leverage V7’s OCR (Optical Character Recognition) capabilities to convert scanned images or handwritten text into machine-readable text. Detect text regions within an image and get the AI to the char recognize the characters within those regions. V7 works with any language, any alphabet, any format.
Follow or track the movement of one or more objects within a video sequence by detecting and matching features across frames. Use V7 to create labels manually or using auto annotation, and interpolate between frames to speed up the labeling process. Leverage Instance ID and attributes to enrich your annotations.
Use V7’s in built text models, such as Text Scanner, Passport Scanner, Receipt Scanner, or Invoice Scanner to automate the extraction of information from documents (such as text, images, or tables) and convert it into structured data and machine-readable formats. V7 works with any language, any alphabet, any format.
Use V7’s polyline tool to manually draw lines or curves on an image to highlight or mark specific features or regions of interest. Solve any detection, segmentation, and tracking task.
Identify and classify human actions or movements within a video using action recognition. Leverage V7 keypoint skeleton to annotate your data and add attributes, text, directional vectors, or instance IDs to enrich your annotations.
Label whole slide images (WSI) faster with V7 auto annotate tool. Create classes with attributes, text, and instance IDs to enrich your annotations. Leverage consensus and logic stages to build automated medical workflows. V7 is FDA and HIPAA compliant.
Take advantage of V7’s DICOM annotation features such as orthogonal views, image manipulation, windowing, and consensus stage, among many others, to create pixel-perfect segmentations in CT, MRI, X-Ray, or Ultrasound scans. Build automated medical data workflows. V7 supports both DICOM and NIfTI formats, and is FDA and HIPAA-compliant.
Solve any labeling task 10x faster, train accurate AI models, manage data, and hire pro labelers that care about your computer vision projects
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.