Data Annotation Services · Image Annotation
Image Annotation Services for Computer Vision
Bounding boxes, polygons, pixel masks, keypoints and 3D cuboids drawn by trained annotators to your taxonomy, checked at every stage and delivered in COCO, YOLO or your own schema.
- Boxes, polygons & masks
- Pilot batch before scale
- Managed or dedicated team
Image Annotation Services
Labels Your Model Can Actually Learn From
A detection model trained on loose boxes learns loose boxes. When two annotators disagree on whether a half-hidden pedestrian counts, or where a shelf ends and a product begins, that disagreement ends up in your mAP. Zilo AI's image annotation services put trained annotators on your data with a written rulebook for those edge cases, agreed with your team on a pilot batch before any volume work starts.
We label product photos, retail shelves, scanned documents, aerial and drone imagery, medical scans, street scenes and factory lines. Hand us a managed project and receive QA-checked datasets, or add dedicated annotators who work inside your own tool, whether that is CVAT, Label Studio, Labelbox, SuperAnnotate, Encord or V7.
Every Label Type
2D boxes, rotated boxes, polygons, semantic, instance and panoptic masks, keypoints, classification and attribute tags.
Pilot Before Volume
A calibration batch settles the edge cases and the guideline wording before your budget goes into scale.
Multi-Level QA
Reviewer passes, gold-standard images and consensus checks catch drift between annotators and between batches.
Your Format
Delivery in COCO, YOLO, Pascal VOC or a custom JSON schema, ready to drop into your training pipeline.
What We Annotate
Image Annotation Types We Deliver
Pick one label type or combine several on the same image. Each task runs to your class list and your edge-case rules, with the tools and formats your team already uses.
Image Data Annotator
Labels still images to your class taxonomy, from product photos and scanned documents to retail shelves, aerial views and street scenes, applying boxes, image-level tags and attributes under consistent edge-case rules.
Bounding Box, Polygon & Segmentation
Draws tight 2D boxes, detailed polygons and pixel-level semantic, instance or panoptic masks wherever shape matters, from vehicles and pedestrians to crop rows, product packaging and surface defects.
Keypoint & Pose Annotation
Places skeletal keypoints and landmarks on people, hands, faces and objects, with visibility flags for occluded points, to train pose estimation, gesture recognition, sports analytics and ergonomics models.
3D Point Cloud / LiDAR Annotation
Fits 3D cuboids and point-level labels to LiDAR and radar point clouds, tracks objects across sweeps and links them to camera frames for autonomous driving, robotics, drone and mapping datasets.
For ML & Data Teams
How to Brief an Image Annotation Vendor
Most image annotation problems start in the brief, not in the labelling. A short guideline that leaves occlusion, truncation and tiny objects undefined will come back labelled three different ways. Use this checklist before you send data to any vendor, Zilo AI included.
Questions to ask any annotation vendor
- How do you write and version the labelling guideline, and who signs off on changes?
- What happens when two annotators label the same image differently?
- How do you measure quality, and can we see the review results for each batch?
- Can your team work inside our annotation tool rather than yours?
- How do you handle images that do not fit any class in our taxonomy?
- Who on your side do we talk to when a model result points to a labelling problem?
Fix the Class List First
Write every class with a one-line definition and an example image. Decide now whether a car seen in a reflection or on a billboard counts.
Define Occlusion and Truncation
Say whether a box covers only the visible part or the estimated full object, and at what percentage hidden an object is skipped.
Set a Minimum Object Size
Tiny objects cost the most time and add the most noise. Agree the pixel size below which an object is ignored or tagged differently.
Choose the Cheapest Label That Works
Boxes are faster than polygons and polygons are faster than pixel masks. Use masks only where your model really needs the shape.
Run a Pilot and Measure It
Label a few hundred images, review them together, and fix the guideline before scaling. Most disagreements show up in the first batch.
Plan for Guideline Changes
Models reveal new edge cases. Agree how a rule change is rolled out and whether earlier batches get relabelled.
Engagement Models
Flexible Ways to Work With Us
Outsource a complete annotation project or extend your in-house data team, whichever suits your pipeline.
Managed Projects
Share your data and guidelines, and we handle annotation, quality checks and delivery of AI-ready datasets on schedule.
Best forEnd-to-end outsourcing
Pilot Batch
Start with a small, calibrated sample to validate guidelines, accuracy and turnaround before committing to scale.
Best forNew projects & vendor evaluation
Dedicated Teams
Trained annotators work exclusively on your project, in your tools and workflows, with a single point of contact.
Best forOngoing, high-volume pipelines
Annotator Staffing
Hire trained annotators, raters and linguists on contract to extend your in-house data or AI team.
Best forScaling your own team
How It Works
From Raw Data to AI-Ready Data
A structured workflow built around your guidelines, with quality checks at every stage.
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1
Share
Send us sample data, your label taxonomy or guidelines, target volumes and preferred output format.
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2
Pilot
We annotate a calibration batch, align on edge cases and refine guidelines together with your team.
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3
Scale
Trained annotators work through your data at volume, with multi-level review and QA sampling.
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4
Deliver
Receive accurate, consistently labeled data in your format, and iterate as your models evolve.
Why Zilo AI
Why AI Teams Choose Zilo AI for Data Annotation
Good training data comes from a repeatable process, not a one-off delivery. We pair trained people with agreed guidelines and layered quality checks, and adapt to your tools, formats and engagement model instead of forcing ours.
- One partner for image, video, text, audio and LLM data
- Pilot batches and guideline calibration before scaling
- Multi-level review with gold-standard and consensus checks
- Delivery in COCO, YOLO, JSONL, SRT or your custom schema
- Secure, access-controlled workflows aligned to your data policies
- Managed projects or dedicated annotators on contract
Industries We Serve
- Autonomous Vehicles & Mobility
- AI Labs & LLM Developers
- E-commerce & Retail
- Healthcare & Life Sciences
- BFSI & Fintech
- Search & Discovery
- Manufacturing
- Media & Entertainment
- Agriculture
FAQs
Image Annotation Services: Common Questions
Have a question we have not answered? Talk to our team and we will get back to you quickly.
Talk to Our Team
- +91 77604 02792
- connect@ziloservices.com
- Mon to Fri: 8am to 10pm
- Hebbal Kempapura, Bangalore
What image annotation services does Zilo AI offer?
We provide 2D bounding boxes, rotated boxes, polygons, semantic, instance and panoptic segmentation, keypoint and pose annotation, image classification and attribute tagging, OCR region labelling and 3D cuboids on LiDAR point clouds.
Which annotation tools do your annotators use?
Our annotators work in CVAT, Label Studio, Labelbox, SuperAnnotate, Encord, V7 or your in-house tool. If you already have a tool and a workspace, we can work inside it.
How do you check annotation quality?
Every project starts with a pilot batch to agree on guidelines and edge cases. After that, work goes through multi-level review with gold-standard images and consensus checks, and the results are shared with you.
What output formats do you deliver?
We deliver in COCO, YOLO, Pascal VOC or a custom JSON schema that matches your training pipeline.
Can we start small before committing to a large project?
Yes. Most clients start with a pilot batch, check the labels against their own review, and then scale through a managed project or a dedicated team.
Is our image data kept secure?
We work in secure, access-controlled workflows aligned to your data policies, and can work entirely inside your own environment if you prefer.
Ready to Start Your Image Annotation Project?
Send us a sample of your images and your class list, and we will run a pilot batch so you can judge the labels before you commit.