Annotate videos, image sequences, and volumetric data 10x faster. Generate bounding boxes and segmentation masks with auto-annotate tools and label videos of any length with intuitive UI inspired by the best video editing tools.
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 ->
Building accurate AI starts with high-quality video labels. With V7 you can annotate videos online in their original resolution and at native frame rate to create pixel-perfect training data for AI. Upload and annotate clips of any size or duration and accelerate your AI development.
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.
Get full control and visibility into every aspect of your ML development pipeline. Automate data workflows, collaborate in real-time, and control the quality of your annotated videos and training datasets.
From .mp4 to .mov, .mkv, and .avi—V7 supports all popular video formats. Upload these in their native-frame rate without losing quality, and label multi-hour videos with ease.
Label data 10x faster with V7’s automatic video segmentation models, Meta’s SAM, or custom models fine-tuned for your specific use case. Create pixel-perfect polygon masks for multiple frames in seconds.
Onboard your entire team, assign roles, and start collaborating in real-time. Add and resolve comments and generate performance reports to track the progress of your video annotation projects.
Add consensus stages into your training data workflow and automate the QA process. Compare models or labelers with AI consensus.
Run inference on your videos with public V7 models, including ML models for autonomous driving, object detection, text recognition, and more. Or, connect external models with our intuitive Bring Your Own Model GUI.
Integrate V7 into your existing tech stack with Darwin-py and REST API. Generate advanced reports, export video labels in custom formats, and import existing annotations.
Hire professional labelers with industry expertise specific to your use case. Use V7's video annotation services and we'll manage the entire labeling projects for you. Scale up or down depending on your needs.
Being hugely popular in the medical space, V7 is one of the most secure video annotation apps on the market. We are GDPR, SOC2 and FDA-compliant, and we enable SSO integration. Your video data remains protected and private.
Scalable, production-ready solutions tailored to enterprise-level projects. Explore our video annotation software and discover how to label videos and train more accurate models faster.
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
Send tasks to other ML-Ops platforms, host data privately in your enterprise cloud storage, and load datasets into your deep learning framework of choice. Unleash the potential of your project with a thriving ecosystem at your disposal.
The Consensus stage assesses the degree of agreement among multiple video annotators by analyzing the overlap between independent annotations. In most cases, however, annotators may add or remove video annotations at different points, making frame-level agreement challenging. For instance, determining the exact frame where an object enters the scene can be subjective. Still, the Consensus stage is crucial for evaluating the degree of overlap for specific keyframes in subsequent QA reviews. Make sure to add a Review Stage immediately after using Consensus on videos.
The FPS value corresponds to the number of frames per second. However, by "frames," we are referring to the frames available for annotation, not all frames in general. This means that V7 does not reduce the frame rate of the video itself. All videos are imported and can be previewed at their native frame rate, but not all annotations are editable at every frame if the video has been imported at a reduced FPS value.
V7’s ability to handle various video formats, provide real-time collaboration, and ensure regulatory compliance make it a highly reliable and comprehensive tool for commercial AI development. To compare different functionalities, you can read this guide to best video and image annotation software.
V7 supports all popular video formats including .mp4, .mov, .mkv, and .avi, allowing users to upload videos in their native frame rate without losing quality. Video preprocessing involves mapping imported videos onto the annotation timeline in V7’s annotation panel, but the previews are based on your original file.
Video annotation is the process of tagging or labeling video frames with information that can be used to train machine learning models. This can include generating bounding boxes, segmentation masks, keypoint skeletons, polylines, and other types of labels on videos. You can find more information in our guide to video annotation.
V7 supports both long videos and utilizes proprietary back-end performance boosters for rendering densely annotated videos. At some point, there may be some limitations related to your browser's memory. However, these can be avoided by following best practices, such as designing an optimized annotation class structure.
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 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.
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.
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.