AI-assisted labeling that learns from your corrections

V7's auto-annotation models start by suggesting labels on new images or video frames. As human annotators correct these suggestions, the model improves and requires fewer corrections over time. This creates a feedback loop where annotation speed increases with every batch processed. The platform supports object detection, segmentation, classification, and keypoint labeling out of the box.

Business outcome: Reduce per-image annotation time by up to 80% after the first few hundred corrections, cutting dataset preparation costs significantly.

Version-controlled datasets with lineage tracking

Every annotation, model prediction, and human correction is tracked as a versioned event. Teams can roll back to any previous dataset state, compare annotation quality across versions, and export exact snapshots for model training. This audit trail is critical for regulated industries where reproducibility is mandatory.

Business outcome: Eliminate data versioning chaos and ensure every model training run uses a reproducible, documented dataset.

Train models directly inside the annotation platform

V7 integrates training pipelines that let teams launch model training jobs on their annotated data without exporting to a separate ML platform. The platform supports popular frameworks including PyTorch and TensorFlow. Training results feed back into the auto-annotation engine, creating a continuous improvement cycle.

Business outcome: Shorten the iteration loop between labeling, training, and evaluation from days to hours.

Automated annotation quality scoring and review workflows

V7 automatically scores annotation quality by comparing human labels against model predictions and consensus across multiple annotators. Low-confidence annotations are flagged for review. Managers can assign review tasks, track annotator accuracy over time, and set quality thresholds that trigger automatic re-labeling.

Business outcome: Maintain consistent annotation quality above 95% across distributed teams without manual spot-checking of every label.

Frame-accurate video labeling with interpolation and tracking

For video datasets, V7 supports automatic interpolation between keyframes, object tracking across frames, and temporal segmentation. Annotators label only a subset of frames, and the platform propagates those labels through the video using AI-assisted tracking. This is essential for autonomous vehicle and sports analytics use cases.

Business outcome: Reduce video annotation effort by 90% compared to frame-by-frame manual labeling, enabling larger video datasets within budget.

Connect to cloud storage, ML pipelines, and labeling marketplaces

V7 connects natively to AWS S3, Google Cloud Storage, and Azure Blob Storage for dataset import and export. API access allows integration with custom ML pipelines, and the platform supports exporting annotations in COCO, Pascal VOC, and YOLO formats. For scaling labeling capacity, V7 integrates with managed labeling workforces.

Business outcome: Plug V7 into existing infrastructure without rewriting data pipelines, reducing integration time from weeks to days.

• End-to-end workflow in one platformTeams avoid the friction of moving data between separate labeling, storage, and training tools.
• AI-assisted annotation reduces manual effortThe auto-annotation engine learns from corrections, making each batch faster than the last.
• Enterprise-grade compliance and audit trailsHIPAA, SOC 2, and full version history make V7 suitable for regulated medical and automotive applications.
• Video annotation with interpolationFrame interpolation and object tracking dramatically reduce the effort required for video datasets compared to frame-by-frame tools.

• High cost for small teamsThe Starter plan at $500/month is expensive for solo practitioners or small research groups with limited budgets.
• Learning curve for new usersThe platform's breadth of features requires dedicated training time before teams become productive.
• Limited support for 3D point cloud dataWhile V7 handles 2D and video well, 3D point cloud annotation for LiDAR data is less mature than dedicated tools.
• Professional RealityV7 Lab is designed for teams that already have a defined computer vision pipeline — it is not a plug-and-play solution for non-technical users who just want to label a few hundred images quickly.

Medical imaging AI development

Radiology teams use V7 to annotate CT scans, MRIs, and X-rays with pixel-level segmentation masks. The HIPAA-compliant infrastructure and DICOM support make it viable for clinical AI projects that require audit trails and data residency.

Autonomous vehicle perception training

Self-driving car teams label video streams with bounding boxes, lane markings, and 3D cuboids. V7's frame interpolation and object tracking reduce the labeling burden for the millions of frames required for perception model training.

Manufacturing quality inspection

Factory teams annotate defect images to train visual inspection models. V7's auto-annotation learns from each defect type, accelerating dataset creation as new product variants are introduced to the production line.

Agricultural drone imagery analysis

Agritech companies label drone-captured field images for crop health monitoring, weed detection, and yield estimation. V7's integration with cloud storage allows processing of large geospatial image datasets without local infrastructure.

Sign up for a free V7 Lab account and create your first workspace for your project.

Upload your image or video dataset directly from your local machine or connect a cloud storage bucket (AWS S3, GCS, or Azure).

Define your annotation ontology — the classes and label types (bounding box, segmentation, keypoint) your model needs to predict.

Assign annotation tasks to your team or enable auto-annotation to let AI generate initial labels for review and correction.