Image Annotation Services
Pixel-precise labels that train vision AI
Expert human image annotation across bounding boxes, polygon segmentation, keypoints, semantic segmentation, 3D cuboids, and OCR — with domain-specialist annotators, 98.5% QA accuracy, and 48h pilot delivery.
Trusted by AI teams worldwide
50M+
Images annotated
98.5%
QA accuracy
40+
Languages
2K+
Domain expert annotators
48h
Pilot batch turnaround
Annotation types
Every image labeling method, done with pixel precision
Each annotation task handled by specialists trained on task-specific QA rubrics — not generalists applying uniform workflows to every image type.
01
Bounding Box Annotation
2D axis-aligned and rotated bounding boxes for object detection — with corner handle precision, occlusion flags, crowd region handling, and multi-class support at scale.
02
Polygon & Instance Segmentation
Precise polygon outlines for irregular objects — instance segmentation where each individual object gets its own unique mask, plus panoptic segmentation combining semantic and instance approaches.
03
Semantic Segmentation
Pixel-class labeling across the entire image — every pixel assigned a category for scene understanding, autonomous driving, satellite imagery analysis, and medical image parsing.
04
Keypoint & Pose Annotation
Human and animal skeleton keypoint labeling — joint coordinates with visibility flags across age, body type, activity, and clothing variation. For fitness AI, sports analytics, and healthcare pose estimation.
05
3D Cuboid Annotation
3D bounding boxes for camera images and LiDAR point clouds — 8-vertex cuboid labeling with depth estimation, size dimensions, and orientation angles for autonomous vehicle and robotics AI.
06
Image Classification
Single-label, multi-label, and hierarchical image-level classification — whole-image category assignment and region-level attribute labeling at scale for search, recommendation, and moderation AI.
07
OCR & Text Region Detection
Text region bounding boxes, word-level and character-level segmentation, and transcription for document AI — supporting invoice processing, license plate recognition, and scene text understanding.
08
Medical Image Annotation
ROI labeling, organ segmentation, lesion detection, and pathology classification for radiology, histopathology, ophthalmology, and dermatology AI — annotated by clinically trained professionals under HIPAA-ready protocols.
09
Depth & Quality Assessment
Depth map annotation, surface normal labeling, stereo disparity estimation, and image quality scoring — blur, clipping, noise, and usability ratings for filtering training sets and 3D reconstruction AI.
Use cases
Image annotation for every computer vision application
From training object detectors to fine-tuning foundational vision models — every CV system needs accurately labeled image data. Synnth delivers it.
Autonomous Vehicles & ADAS
Multi-class urban scene annotation — vehicle and pedestrian tracking, lane markings, drivable area segmentation, traffic signs, and adverse condition datasets across weather, time-of-day, and geography.
Medical & Healthcare AI
Radiology, pathology, dermatology, and ophthalmology annotation by clinically trained professionals — HIPAA-ready, DICOM-compatible, with strict audit trails and consent documentation.
Retail & E-Commerce
Product image classification, attribute tagging, shelf-scan annotation, and visual search training data — for inventory AI, recommendation engines, and e-commerce search relevance.
Robotics & Warehouse Automation
Object pose estimation, bin-picking datasets, conveyor belt defect detection, and 3D point cloud annotation for industrial automation and human-robot collaboration systems.
Agriculture & Remote Sensing
Crop health classification, weed vs. crop segmentation, aerial field mapping, and livestock counting from drone and satellite imagery for precision agriculture AI.
Security & Surveillance AI
Person re-identification, crowd density estimation, anomaly detection, perimeter breach labeling, and abandoned object annotation for intelligent video surveillance systems.
Quality assurance
QA that matches the demands of pixel-level precision
Image annotation quality isn’t just about label correctness — it’s about boundary accuracy, temporal consistency, and demographic balance. Our QA pipeline is built for all three.
Boundary accuracy is measured geometrically — not just label correctness. Bounding box IoU (Intersection over Union) against gold-standard references and polygon vertex precision are both tracked per annotator per category, because off-by-a-pixel boundaries compound into real training data degradation at scale.
For demographic diversity, every collection campaign is designed with explicit quotas covering age, gender, ethnicity, skin tone, and geographic region. We run diversity audits before delivering datasets to prevent training bias from being introduced at the labeling stage.
Geometric validation
Automated checks validate polygon closure, box containment, keypoint range, and boundary precision — filtering geometric errors before human QA review begins.
Domain-matched annotators
Medical images by clinicians. Automotive scenes by CV engineers. Industrial images by domain specialists. Annotation expertise matched to subject matter — not generic labeling workflows.
Per-class QA reporting
Every delivery includes per-class accuracy metrics, annotator calibration stats, IoU scores, rejection rates, and a revision log — so your ML team has full visibility into dataset quality.
How it works
From image to production-ready annotated dataset
A transparent four-stage pipeline with geometric validation and quality gates at every step — built for CV teams who need consistent, scalable image annotation.
Define scope
Share your use case, object categories, annotation type, demographic requirements, and edge-case scenarios. We co-design ontologies, labeling guidelines, and QA rubrics with your CV team.
Screen & prepare
Images are pre-screened for quality (blur, exposure, resolution), assigned to domain-matched annotators, and set up in task-specific tooling with your ontology pre-loaded.
Annotate & QA
Expert annotators label your images. Every batch passes geometric validation, inter-annotator agreement checks on calibration samples, and senior reviewer sign-off before delivery.
Deliver & iterate
Receive clean datasets in COCO, Pascal VOC, YOLO, or your custom format — with a full QA report including per-class accuracy, IoU scores, and revision log. Same annotator pool every batch.
Why Synnth
Built for teams where annotation quality is model quality
What separates Synnth from generic image labeling platforms — especially for boundary precision, domain expertise, and demographic diversity.
Geometric precision QA
Boundary accuracy is validated geometrically — IoU scoring, polygon closure checks, and vertex precision measurement — not just label correctness. Off-by-pixel boundaries degrade model accuracy at training scale.
0.94 avg. IoU
Domain-matched annotators
Medical images by clinicians. Automotive scenes by engineers. Agricultural imagery by domain professionals. We staff annotation based on subject-matter expertise, not just annotator availability.
200+ specialists
Diversity by design
Collection and annotation campaigns are built with explicit demographic quotas. We prevent model bias through deliberate dataset composition — not post-hoc auditing after the training run.
Balanced by default
Custom annotation schemas
We build task-specific ontologies, edge-case handling guides, and QA rubrics designed around your model’s exact deployment domain — not off-the-shelf templates that generate systematic errors on your corner cases.
Enterprise-grade security
All images encrypted at rest and in transit. GDPR compliant, HIPAA-ready for medical imagery. NDAs on every engagement. Proprietary product and patient images never leave our controlled, access-audited environments.
Fast pilot SLAs
Validate annotation quality — IoU scores, edge-case handling, boundary accuracy — before committing to full production volume. Pilot batches of up to 5,000 images in 48 hours at full QA standards.
48h pilot delivery
Input & output formats
Delivered in the format your pipeline already expects
No conversion scripts needed. Annotations arrive clean and structured, ready to plug into your training infrastructure.
Industries
Image annotation expertise across every sector
Annotation teams matched to your industry’s terminology, regulatory requirements, and quality standards — not generic workflows applied uniformly.
Autonomous Vehicles
Dashcam, roadside, and LiDAR annotation for self-driving systems — across weather, lighting, and global geographies.
Healthcare AI
Radiology, pathology, dermatology image annotation by clinical professionals under HIPAA-ready protocols.
Retail & E-Commerce
Product images, shelf scanning, visual search, and attribute tagging for inventory and recommendation AI.
Robotics & Industrial
3D point clouds, bin-picking, conveyor inspection, and pose estimation datasets for warehouse and factory automation.
Agriculture & Environment
Drone and satellite imagery annotation — crop health, weed detection, field mapping, and wildlife monitoring datasets.
Security & Public Safety
Person re-ID, crowd analysis, anomaly detection, and multi-camera tracking annotation for surveillance AI.
FAQ
Common questions about image annotation
Everything you need to know before starting an image annotation project with Synnth.
💡 Can’t find your answer here? Talk to our team — we typically respond within one business day.
What is AI image annotation and why does it matter?
AI image annotation is the process of labeling photographs or rendered images with structured metadata — bounding boxes, segmentation masks, keypoints, classification labels, or depth values — to create training data for computer vision models. The quality, diversity, and geometric precision of annotations directly determines object detection accuracy, segmentation mask quality, and pose estimation performance in production.
What is the difference between semantic and instance segmentation annotation?
Semantic segmentation assigns a category label to every pixel in an image — all cars share a single “vehicle” label regardless of how many appear. Instance segmentation goes further, giving each individual object its own unique mask — car A and car B are labeled separately with distinct instance IDs. Panoptic segmentation combines both, providing semantic labels for background regions and instance masks for foreground objects. Synnth supports all three, individually or in a combined workflow.
How does Synnth measure image annotation quality?
Image annotation quality is measured through geometric metrics — Intersection over Union (IoU) for bounding boxes and segmentation masks, Average Precision for keypoint localization — measured against gold-standard references on calibration samples. Automated geometry checks validate polygon closure, box containment, and vertex range. Senior reviewers sign off on every batch before delivery. We report per-class IoU scores, rejection rates, and annotator calibration stats in every QA report.
Can Synnth annotate medical images such as X-rays, CT scans, and MRI?
Yes. Medical image annotation projects are staffed with annotators who have clinical training relevant to the imaging modality — radiologists for chest X-rays and CT, pathologists for histopathology slides, dermatologists for skin lesion images. All healthcare data is handled under HIPAA-ready protocols with access-controlled environments, full audit trails, and signed Business Associate Agreements (BAAs) where required. Synnth accepts DICOM format and delivers annotations compatible with clinical AI pipelines.
What image formats does Synnth accept for annotation?
Synnth accepts JPEG, PNG, TIFF, BMP, WebP, and RAW formats for standard image annotation. For medical imaging we support DICOM. For satellite and aerial imagery we accept GeoTIFF. Annotations are delivered in your preferred format — COCO JSON, Pascal VOC XML, YOLO TXT, LabelMe JSON, TFRecord, Cityscapes, KITTI, or custom schemas. Format is agreed during scoping at no additional cost.
How does Synnth handle edge cases like occlusion, small objects, and truncation?
Edge cases are handled through explicit annotation guidelines built collaboratively with your CV team — not left to individual annotator judgment. For occlusion, we use visibility flags and partial bounding box conventions. For small objects below a defined pixel threshold, we apply separate labeling rules. For truncated objects at image borders, we annotate the visible portion with a truncation flag. All edge-case handling conventions are documented in the project specification and calibrated before production begins.
What is the minimum project size and turnaround time?
We accept pilot batches from 1,000 images, typically delivered within 48–72 hours at full QA standards. Annotation throughput depends on task complexity — bounding box annotation is faster per image than polygon segmentation or keypoint labeling. For large-scale or ongoing production projects, we scope velocity targets and delivery schedules during the initial consultation and provide honest estimates based on your specific task complexity.
How is proprietary image data kept secure during annotation?
All images are uploaded through TLS-encrypted channels and stored with AES-256 encryption at rest. Annotation work is performed only within access-controlled, audited environments — annotators can view assigned images through our secure platform but cannot download or export raw files. NDAs are signed on every engagement. Proprietary product images, medical imagery, and sensitive visual data never leave our controlled environments under any circumstances.
Get started
Start your image annotation project today
Tell us your use case, annotation types, object categories, and volume. Our team responds within one business day with a scoping plan and no-obligation quote.
- info@synnth.com
- Mon–Fri, 9am–6pm IST
- Response within 1 business day
- No setup fees
- No setup fees
- NDA available on request
- Free pilot for qualifying projects