AI Image Data Collection

The visual training data your computer vision needs

Diverse, accurately annotated image datasets — from controlled capture campaigns to pixel-precise labeling — built for object detection, segmentation, classification, and beyond.

Trusted by AI teams worldwide

50M+

Annotations delivered

98.5%

Average QA accuracy

40+

Image Categories

2K+

Domain expert annotators

48h

Pilot batch turnaround

Use cases

Image datasets for every
computer vision application

Whether you’re training a detector, fine-tuning a foundation model,
or building a medical imaging tool — Synnth sources and labels
the exact images your model needs.

Object Detection & Localization

Precisely annotated bounding boxes across diverse environments, lighting conditions, and object scales — for training YOLO, Faster R-CNN, DETR, and similar detection architectures.

Bounding boxes, Occlusion handling, Multi-class, COCO format

Semantic & Instance Segmentation

Pixel-level polygon and mask annotations for scene understanding, autonomous driving, medical imaging, and industrial quality control — with strict boundary accuracy requirements.

Polygon masks, Semantic labels, Instance masks, Panoptic

Pose Estimation & Keypoint Detection

Human and animal pose datasets with skeleton keypoint labeling across age, gender, body type, activity, and clothing variation — for fitness, healthcare, and action recognition AI.

Skeleton keypoints, COCO pose Action labels, Occlusion flags

Autonomous Vehicles & ADAS

Multi-category urban and highway scene datasets — vehicles, pedestrians, cyclists, signs, lane markings — annotated across weather conditions, times of day, and geographic regions.

Lane detection, 3D cuboids, Weather variation, Depth maps

Medical & Healthcare Imaging

HIPAA-ready medical image annotation by qualified clinical professionals — radiology, pathology, dermatology, ophthalmology — with strict provenance and consent documentation.

HIPAA-ready, DICOM, Clinical experts, ROI labeling

Retail, Manufacturing & Industrial

Product imagery, defect detection datasets, shelf-scan annotation, and industrial inspection images — built for inventory AI, visual quality control, and e-commerce search.

Defect labels, Product attributes, OCR Shelf mapping

Annotation types

Every labeling method,
done precisely

Our annotators are trained on each task type with strict quality rubrics.
No generic labeling workflows — each annotation method
has its own QA criteria.

Bounding
Boxes

2D axis-aligned and rotated boxes for object detection. Fast, scalable, and accurate for rectangular objects.

Polygon
Segmentation

Precise polygon outlines for irregular objects. Used for semantic, instance, and panoptic segmentation tasks.

Keypoint &
Pose

Skeleton joint labeling for human and animal pose estimation, action recognition, and biomechanics AI.

Semantic Segmentation

Pixel-class labeling across the entire image — essential for scene understanding, autonomous driving, and aerial imagery.

3D Cuboid
Annotation

3D bounding boxes for LiDAR point clouds and RGB images — critical for autonomous vehicles and warehouse robotics.

Image
Classification

Whole-image and region-level class labels, multi-label classification, and hierarchical taxonomy annotation at scale.

Depth & 3D
Estimation

Depth map generation, surface normal annotation, and stereo disparity labeling for robotics and scene reconstruction.

OCR & Text
Detection

Text region bounding boxes, word and character-level segmentation, and transcription for document AI and scene text recognition.

Annotation types

Every labeling method, done precisely

Our annotators are trained on each task type with strict quality rubrics. No generic labeling workflows — each annotation method has its own QA criteria.

Image collection

Controlled photo capture campaigns - studio and field shoots to your specifications.
Demographically balanced sourcing - age, gender, ethnicity, skin tone coverage.
Environmental & edge-case sets - night, rain, fog, glare, occlusion scenarios.
Medical & industrial imagery - specialist capture under HIPAA or ISO protocols.
Synthetic data augmentation - generated images to fill rare-class and long-tail gaps.
Aerial & satellite imagery - drone capture and licensed satellite sourcing.
Multi-camera & stereo rigs - synchronized multi-view datasets for 3D tasks.

Annotation & labeling

Bounding boxes & polygons - 2D, 3D, rotated, with occlusion flags.
Semantic segmentation - pixel-level class labeling with boundary precision.
Keypoint detection - body, face, hand, and custom skeletal schemas.
Image classification - single-label, multi-label, and hierarchical taxonomy.
Depth & 3D annotation - point cloud, cuboid, surface normal, disparity.
OCR & text detection - word-level boxes, character segmentation, transcription.
Attribute & quality scoring - age, expression, lighting, blur, occlusion ratings.

How it works

From brief to production-ready image dataset

A transparent pipeline with quality gates at every stage — designed for ML teams that need reliable, repeatable data delivery.

Define scope

Share your use case, object categories, demographic requirements, environment conditions, and annotation schema. We co-design ontologies and quality rubrics with your ML team.

Source & capture

We run controlled collection campaigns or source existing imagery from our licensed content pool. Diversity quotas, quality bars, and consent documentation are enforced before annotation begins.

Annotate & QA

Expert annotators label your images using task-specific tooling. Every image passes inter-annotator agreement scoring, automated geometry validation, and senior reviewer sign-off.

Deliver & iterate

Receive clean, structured datasets in COCO, Pascal VOC, YOLO, or custom formats with a full QA report. Free revisions within scope; ongoing batches on your schedule.

Why Synnth

Built for teams who can't afford bad labels

Six things that separate Synnth from generic image labeling tools and crowdsourcing platforms.

Human-in-the-loop
QA

Every annotation reviewed by expert humans — not just automated checks. We don’t trust boundary accuracy to algorithms alone. Semantic correctness requires judgment.

99.2% QA pass rate

Diversity
by design

Collection campaigns are built around explicit demographic and environmental quotas. We prevent training bias through deliberate, calibrated dataset composition from the start.

Balanced by default

Domain-matched
annotators

Medical images annotated by clinicians. Autonomous driving data by automotive engineers. Retail by e-commerce specialists. Annotation expertise matched to subject matter.

200+ specialists

Enterprise-grade
security

Images encrypted at rest and in transit. GDPR compliant, HIPAA-ready, SOC 2-aligned processes. NDAs on every engagement. Proprietary imagery never leaves controlled environments.

Fast pilot to
production

Validate data quality before committing to scale. Pilot batches of up to 5,000 images in 48–72 hours, with the same QA standards as full production runs.

48h pilot delivery

Custom annotation
schemas

We build task-specific ontologies, edge-case handling guides, and labeling rubrics for your exact model requirements — not off-the-shelf templates that generate edge-case errors.

Industries

Image annotation expertise across every sector

Our annotators are matched to your industry’s terminology, regulatory requirements, and quality standards — not generic labeling workflows.

Autonomous Vehicles

Lane detection, pedestrian tracking, LiDAR point cloud annotation, and traffic sign classification across diverse geographies and weather conditions.

Healthcare AI

Radiology, pathology, dermatology, and ophthalmology image annotation by medical professionals under HIPAA-compliant data handling protocols.

Retail & E-Commerce

Product image classification, attribute tagging, shelf-scan annotation, and visual search training data for inventory and merchandising AI.

Robotics & Warehouse

3D point cloud annotation, object pose estimation, bin-picking datasets, and conveyor belt defect detection data for industrial automation.

Security & Surveillance

Person re-identification, crowd counting, anomaly detection, and license plate recognition datasets for safety and access control systems.

Agriculture & Environment

Crop health classification, weed detection, aerial field mapping, and wildlife monitoring datasets from drone and satellite imagery.

Output formats

Delivered in the format your pipeline already expects

No conversion needed. Datasets arrive ready to plug into your training infrastructure.

FAQ

Common questions about AI image data collection

Everything you need to know before starting an image data project with Synnth.

💡 Can’t find your answer here? Talk to our team — we typically respond within one business day.

What is AI image data collection?

AI image data collection is the process of sourcing, capturing, and curating photographs or rendered images specifically to train computer vision models such as object detectors, image classifiers, semantic segmentation networks, pose estimators, and face recognition systems. The quality, diversity, and accuracy of labels directly determines model performance in production.

How does Synnth ensure demographic diversity in image datasets?

We design every collection campaign with explicit demographic quotas covering age, gender, ethnicity, skin tone, geographic region, and disability representation. We recruit contributors across our global network to fill each demographic cell, and run diversity audits before delivering the dataset. This prevents training bias from being introduced at the data stage.

What annotation formats does Synnth support?

We support COCO JSON, Pascal VOC XML, YOLO TXT, LabelMe JSON, TFRecord, KITTI, Cityscapes, ADE20K, Open Images CSV, DICOM for medical data, and custom schemas designed around your pipeline. If you have an in-house labeling format, we can deliver to it.

Can Synnth annotate medical images such as X-rays and MRI scans?

Yes. Medical image annotation projects are staffed with clinically trained annotators who understand anatomy and pathology. All medical data is handled under HIPAA-ready protocols with access-controlled environments, strict NDAs, and full audit trails.

What is the difference between semantic and instance segmentation?

Semantic segmentation assigns a class label to every pixel in the image — all pedestrians share one “person” label. Instance segmentation goes further, giving each individual object its own unique mask — pedestrian A and pedestrian B are labeled separately. Both are supported by Synnth, as well as panoptic segmentation which combines both approaches.

Can Synnth produce synthetic data to supplement my image dataset?

Yes. For rare object classes, dangerous scenarios, or long-tail edge cases that are difficult or expensive to capture in the real world, we can generate and annotate synthetic images using 3D rendering pipelines. Synthetic data is often combined with real captured images and can significantly improve model robustness on underrepresented cases.

How is annotation quality measured and reported?

Quality is measured through inter-annotator agreement (IAA) on benchmark samples, automated geometry checks (e.g., box containment, polygon validity), and senior reviewer sign-off. Every delivery includes a QA report showing per-class accuracy, IAA scores, rejection rates, and revision log. Our standard QA pass rate exceeds 98.5%.

What is the minimum order and turnaround time?

We accept pilot batches starting from 1,000 images, typically delivered within 48–72 hours. Enterprise projects with high volume or complex annotation tasks are scoped with custom SLAs, dedicated project managers, and priority pipeline access.

How is my proprietary image data kept secure?

All images are uploaded through TLS-encrypted channels, stored with AES-256 encryption at rest, and annotated only within access-controlled, audited environments. NDAs are signed on every engagement. We do not share, train on, or re-use your data under any circumstances.

Get started

Start your image data project today

Tell us your use case, object categories, and volume targets. Our team will respond within one business day with a scoping plan and a no-obligation quote.