Bounding Boxes
2D boxes for object detection, tightly fitted and consistent across classes, occlusions, and truncated objects.
Train computer vision models on precisely labeled images. Smart Annotahub delivers bounding boxes, polygons, keypoints, and pixel-level segmentation at scale, with multi-stage QA on every batch.
2D boxes for object detection, tightly fitted and consistent across classes, occlusions, and truncated objects.
Vertex-accurate outlines for irregular shapes such as vehicles, products, rooftops, and anatomy.
Pixel-level class masks for scene understanding, with clean boundaries and no unlabeled gaps.
Separate masks for every object instance, so models can count and track individual items.
Landmark and pose annotation for faces, bodies, hands, and articulated objects.
Image-level labels and attributes for content moderation, retrieval, and dataset curation.
From your first sample to production volume, every project follows the same six-step path, with no commitment until the pilot proves the quality.
We sign an NDA first, then clarify your goals, taxonomy, edge cases, and target accuracy for the image dataset.
We annotate a sample of your image dataset at no cost, so you can judge quality, speed, and edge-case handling first-hand.
Based on your pilot feedback, we finalize scope, timeline, pricing, and the Service Level Agreement.
We assemble a dedicated team, train it on your guidelines, and agree on communication channels and progress tracking.
Our team runs image annotation to the agreed plan, with throughput and accuracy KPIs tracked for every annotator.
Your annotated images pass multi-stage quality review before delivery. Your feedback feeds straight back into the guidelines.
We adapt taxonomies and edge-case rules to the domain, so the labels match how your model will be used in production.
Vehicles, pedestrians, lanes, and traffic signs across weather and lighting conditions.
Product detection, shelf monitoring, and attribute tagging for catalog search.
Defect detection and surface inspection on production-line imagery.
Crop, weed, and fruit detection from field cameras and drones.
Region-of-interest outlining on medical images under expert review.
Your platform or ours. We adapt to existing pipelines, review stages, and export schemas.
Tell us about your data and requirements. We'll return an annotated sample with a precise quote, usually within a few working days.
We’ve received your request and will be in touch soon.
Reviews
We chose Smart Annotahub for its strong value, recommendation, and shared company values. Their 10-person team delivered accurate data annotation with a flexible, collaborative approach. They responded quickly, went the extra mile to meet deadlines, and kept the project on track. We’ve been very pleased with the experience and have no improvements to suggest at this time.
The team is highly responsive and flexible, quickly adapting to our needs to keep the project on track. Clear, detailed annotation guidelines help them deliver accurate results faster.
We chose Smart Annotahub for its expertise, openness to new ideas, and strong interest in autonomous vehicles. Their team provides consistent cuboid and polygon annotation for our growing image dataset, with responsive communication, attentive project management, and reliable quality assurance. We’re very pleased with the collaboration and look forward to continuing our work together.
Accuracy targets are agreed per project during the pilot. Multi-stage review and spot-checks keep production batches at or above the agreed threshold, and every delivery comes with a QA report.
Edge cases are logged, escalated to the project lead, and turned into written guideline updates so every annotator applies the same rule from then on.
Yes. We can validate and correct pre-annotations or model predictions, which is often faster and cheaper than labeling from scratch.
Most pilots start within a few days of receiving sample data. Production teams are typically ready shortly after guidelines are approved.