V7 Labs
AI training data platform for computer vision teams - manages annotation, auto-labeling, and dataset versioning across images, video, and medical scans.
V7 Labs is an AI training data platform that helps computer vision and machine learning teams manage the full data pipeline from raw media to labeled, version-controlled training datasets. Founded in 2018 in London by Alberto Rizzoli and Simon Edwardsson, V7 has raised $33M in total funding including a Series A led by NVIDIA Ventures in 2022. The platform supports annotation and auto-labeling across images, video, PDFs, 3D point clouds, and DICOM medical imaging formats, with AI-assisted labeling that reaches over 80% automation rate on trained annotation models. V7 Darwin, their flagship product, provides dataset versioning, model evaluation, and workflow orchestration for annotation teams with task assignment, QA review cycles, and quality analytics. V7 is notably strong in healthcare AI, offering HIPAA-compliant workflows and specialized DICOM tools for radiology, pathology, and surgical video datasets - a capability absent from most competitors. Paid Team plans start at $100 per seat per month, with a free plan available for up to 3 users.
Key Features
- Multi-modal annotation - labels images, video frames, PDFs, 3D point clouds, and DICOM medical imaging files in one platform
- AI auto-labeling - trains on small labeled sets to automate 80%+ of subsequent annotation work with model-assisted labeling
- DICOM medical imaging support - specialized annotation tools for CT scans, MRI, X-ray, and surgical video with HIPAA-compliant workflows
- Dataset versioning - tracks every change to annotations and metadata so any training run can be reproduced exactly from data snapshots
- Workflow orchestration - manages annotation team tasks, QA review cycles, inter-annotator agreement, and quality metrics in one dashboard
- Model evaluation tools - integrates model performance metrics with dataset analytics to identify the data issues causing prediction errors
Use Cases
- Computer vision teams at tech companies building annotation pipelines for object detection, segmentation, and classification models
- Medical AI startups annotating radiology and pathology imaging datasets for diagnostic model development with compliance requirements
- Autonomous vehicle companies managing large-scale video annotation workflows for object detection and scene understanding
- ML engineers diagnosing model performance issues by correlating prediction errors with data quality problems in the training set
Pros
- DICOM and medical imaging support is a genuine differentiator for healthcare AI teams that Roboflow and Label Studio cannot match
- Auto-labeling reaches 80%+ automation on trained models, which dramatically reduces the human annotation hours needed per dataset
- Dataset versioning from the data layer ensures full reproducibility of training runs - a critical capability for regulated AI applications
Cons
- Team plan at $100 per user per month is expensive for early-stage ML teams building their first production datasets
- Initial auto-labeling model setup requires significant labeled training data and ML engineering effort before automation benefits materialize
- Overkill for straightforward object detection tasks where simpler tools like Roboflow provide faster onboarding and lower cost
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