Platform

AI video labeling for industrial robotics scenes.

Turn camera footage into export-ready training data. Vision Lab labels video, assigns confidence scores, and checks coverage so your team can focus on the model.

~85 ms
per frame labeled, internal benchmark

Three capabilities, one process

Built for industrial video, not generic images.

AI-Assisted Annotation

Each frame receives an initial annotation from models tuned for factory, warehouse, and outdoor robotics environments. The system creates bounding boxes, segmentation masks, and event tags before human review begins.

Confidence-Based Review

Every annotation receives a confidence score. Frames below the threshold enter the review queue automatically, while high-confidence frames are auto-approved for a focused review process.

Multi-Format Export

Export as COCO JSON, YOLO, Pascal VOC, or Custom Schema. Each bundle includes an annotation file, coverage audit CSV, and a manifest of every labeled frame with its confidence score and review status.

From upload to training data in days

Skip the labeling backlog. In a typical pilot, the path from first upload to labeled export takes under a week.

How the dataset takes shape

Day 1
Upload video clips and define the annotation taxonomy, including object classes, event types, and confidence thresholds. Most projects are configured in under an hour.
Day 1-2
The AI pass labels and scores all frames by confidence, then sorts them into the review queue. A preliminary coverage report shows class representation and where more footage may be needed.
Day 2-4
Human reviewers correct and approve the low-confidence frames. The model has already handled the rest, so reviewers focus only on items requiring attention.
Day 4-5
Export in your chosen format with a coverage audit, then feed the dataset into training.
Review Queue - Frame Confidence
frame_2847
0.51
pallet
review
frame_2848
0.74
arm
review
frame_2851
0.48
e-stop
review
frame_2855
0.82
conveyor
review
4 frames flagged below 0.85 confidence threshold. 1,204 frames auto-approved.

Dataset quality drives results

Faster labeling helps, but model performance depends on whether the dataset contains the right classes at the right density.

Annotation Precision

Every label includes a confidence score. Annotations below your confidence threshold go to human review instead of quietly weakening the dataset.

Class Coverage Reporting

The coverage report lists labeled instances by class. It shows whether emergency stop zones have 80 examples and pallets have 4,000 before training, not after model failure.

Consistency Scoring

For footage reviewed by multiple people, the platform calculates inter-annotator agreement and identifies inconsistencies. A high score indicates that the taxonomy is being applied consistently.

Label Agreement
94%
AI vs. human reviewer spot-check, internal testing
Coverage
<48 h
upload to export-ready dataset, across pilot teams
Consistency
0.91
inter-annotator agreement (Cohen's kappa), early-access pilots
Review Reduction
~8%
of frames enter the review queue on typical industrial footage

Works with your pipeline

Vision Lab outputs the format your training stack already uses, without conversion scripts or custom parsers.

Formats for every training stack

Use COCO JSON with PyTorch pipelines, YOLO with Ultralytics, or Pascal VOC with legacy systems. Custom Schema export is available on Team and Studio tiers.

Each export bundle contains the annotation file, a coverage audit CSV, and a manifest of every labeled frame with its confidence score and review status.

COCO JSON YOLO Pascal VOC Custom Schema CSV Manifest
Request Access 
# Export dataset via Vision Lab CLI
from visionlab import Client

# Initialize with your project ID
vl = Client(api_key="vl_live_...")
project = vl.projects.get("proj_warehouse_q2")

# Export reviewed labels in COCO format
export = project.export(
    format="coco",
    filter="reviewed",
    min_confidence=0.85
)

# Returns path to .zip with annotations + manifest
print(export.path)
# /exports/warehouse_q2_coco_20260601.zip

Early Access

Build your first labeled dataset

Vision Lab is in early access. Request access and our team will contact you within two business days to discuss your first project.

Request Access See Pricing