High-quality training data for computer vision — data collection, data curation and data annotation & labeling that help you build accurate, production-ready computer vision models at scale.
Training a computer vision model to interpret the visual world takes large volumes of accurately labeled image and video data. Shaip delivers computer vision training data end to end — from sourcing and data curation to annotation & labeling and quality assurance — so your models learn from diverse, representative and bias-aware examples and perform reliably in the real world.
A complete, managed pipeline for computer vision training data — with software and workforce included.
Training ML models to interpret & comprehend the visual world requires large volumes of accurately labeled image and video data.
From bounding boxes, semantic segmentation, polygons, polylines to keypoint annotation we can help you with any image/video annotation technique.
Computer vision models drift once they hit real-world data. Shaip validates model performance against human-verified ground truth to keep it accurate in production.
From image data annotation to video data annotation, our experts apply the right technique for your model task — object detection, segmentation, classification, tracking and more.
License high-quality, off-the-shelf computer vision datasets to accelerate your project — or have Shaip build a custom dataset to your geography, demographics and annotation requirements.
450k images of driver faces with car setups in different poses and variations, covering 20,000 unique participants from 10+ ethnicities.
Use Case: In-car ADAS model
Format: Images
Volume: 455,000+
Annotation: No
80k+ images of landmarks from over 40 countries, collected based on custom requirements.
Use Case: Landmark Detection
Format: Images
Volume: 80,000+
Annotation: No
84.5k drone videos of college and school campuses, factory sites, playgrounds, streets and vegetable markets, with GPS details.
Use Case: Pedestrian Tracking
Format: Videos
Volume: 84,500+
Annotation: Yes
55k annotated images with 50+ variations across food types, lighting, indoor and outdoor environments, backgrounds and camera distances.
Use Case: Food Recognition
Format: Images
Volume: 55,000+
Annotation: Yes
Diverse, representative training data exposes your model to the real-world variation it will face, improving generalization and reliability.
Balanced, curated datasets minimize bias tied to specific groups or conditions, promoting fairer and more accurate predictions.
Edge cases and varied lighting, backgrounds and orientations make your model resilient in production, not just in the lab.
A 500k+-strong workforce and a patented platform deliver large volumes fast — without sacrificing quality.
Annotation of X-rays, CT, MRI, ultrasound, pathology slides, and dental imagery — with HIPAA-controlled workflows and clinician-led review.
Face recognition data, weapon and threat detection, crowd analytics, and licence-plate datasets with documented consent and ethics review.
Satellite, aerial, and drone imagery annotation — land use, infrastructure, agriculture monitoring, and disaster response.
Egocentric video, hand-object interaction, manipulation, and warehouse / factory perception data for VLA and robotics foundation models.
Multiple cameras capture videos from a different angle to identify the boundaries of traffic signals, roads, cars, objects, and pedestrians nearby to train the self-driving cars to auto steer the vehicle and avoid hitting obstacles while driving the passenger safely.
Product attribute tagging, shelf detection, visual search, and try-on imagery for personalisation and inventory automation.
Enterprises choose Shaip as their computer vision data partner for proven scale, rigorous quality and cross-industry expertise — the qualities that separate the best computer vision data providers and companies from the rest.
Trained experts annotate and review every image and video.
Training data for standalone CV models and VLMs — image, video, audio and text.
Healthcare, automotive, retail, robotics, your domain, your data.
Patented, web-based platform for faster turnaround and seamless delivery.
500K+ crowd contributors and 10,000+ in-house experts across 60+ geographies.
Multi-tier QA with GDPR & HIPAA-compliant, fully transparent delivery.
Detects Biometric Fraud with a 25,000-Video Anti-Spoofing Dataset
Problem: Acquire 25,000 balanced real & replay-attack videos across 5 ethnicities, at 720p+ / 26+ FPS
Solution: 12,500 participants, two videos each, 4 phased batches, 12 metadata attributes + QA
Result: Models that reliably separate real from spoofed biometric videos, cutting fraud risk
Powers Visual Search & Virtual Try-On with Multi-Attribute Fashion Tagging
Problem: Attribute-tag apparel across the full taxonomy, distinguishing look-alike items at 99% accuracy
Solution: 6+ attribute layers per garment, tight per-garment bounding boxes, two-tier QA gate
Result: Production-ready dataset powering visual search, recommendation, try-on, and inventory AI
Computer vision is all about making sense of the visual world to train computer vision applications. Its success completely boils down to what we call image annotation – the fundamental process behind the technology that makes machines make intelligent decisions and this is exactly what we are about to discuss and explore.
Today, we are at the dawn of the next-generation mechanism, where our faces are our pass codes. Through the recognition of unique facial features, machines can detect if the person trying to access a device is authorized, match CCTV footage with actual images to track felons & defaulters, reduce crime in retail stores, and more.
Human beings have the innate ability to distinguish and precisely identify objects, people, animals, and places from photographs. However, computers don’t come with the capability to classify images. Yet, they can be trained to interpret visual information using computer vision applications and image recognition technology.
Empowering teams to build world-leading AI products.
From data collection to annotation, licensing, and validation — we'll help you get to market faster, with data you can trust.
Contact UsComputer vision services help AI teams train models to interpret images, videos, and sensor-based visual data. These services typically include image annotation, video annotation, object detection, semantic segmentation, 3D point cloud labeling, dataset collection, and quality-managed annotation workflows. Shaip provides computer vision services to help enterprises build high-quality training datasets for production AI models.
Computer vision is a branch of artificial intelligence that enables machines to understand and analyze visual data such as images, videos, medical scans, satellite imagery, retail photos, or autonomous driving footage. It allows AI models to detect objects, classify scenes, recognize patterns, track movement, and make decisions based on visual inputs.
Computer vision works by training machine learning and deep learning models on labeled visual datasets. Human annotators label objects, regions, attributes, keypoints, or pixels in images and videos so the model can learn visual patterns. Once trained, the model can identify, classify, segment, or track objects in new visual data.
Shaip offers image annotation services including bounding boxes, polygons, polylines, keypoints, semantic segmentation, instance segmentation, panoptic segmentation, 3D cuboids, image classification, and 3D point cloud annotation. These annotation types support use cases such as object detection, facial landmark annotation, autonomous driving, medical imaging, retail visual search, and robotics AI.
Common computer vision annotation techniques include bounding boxes for object detection, polygons for irregular object boundaries, semantic segmentation for pixel-level labeling, instance segmentation for separating individual objects, keypoints for pose or landmark detection, 3D cuboids for spatial object labeling, and polylines for lanes, roads, tracks, or boundaries.
Yes. Shaip can customize computer vision datasets based on project requirements such as geography, environment, camera angle, lighting condition, object class, demographic mix, annotation taxonomy, image format, video frame rate, metadata fields, and delivery schema. Custom dataset design helps improve model relevance, accuracy, and real-world performance.
The amount of labeled data needed depends on the model type, use case, object complexity, number of classes, and performance target. A baseline model may start with thousands of labeled images per class, while production-grade computer vision models often require tens of thousands or more examples across varied lighting, angles, backgrounds, and edge cases.
Shaip supports computer vision projects across healthcare and medical imaging, autonomous vehicles and ADAS, robotics and physical AI, retail and e-commerce, geospatial and UAV imaging, agriculture, security and surveillance, insurance, smart cities, and industrial AI. Each industry requires domain-specific annotation guidelines, QA workflows, and expert review.
Computer vision is used in autonomous vehicles for obstacle detection, healthcare for medical image analysis, retail for visual search and product tagging, manufacturing for defect detection, agriculture for crop monitoring, security for surveillance analytics, insurance for damage assessment, and robotics for object recognition, navigation, and task execution.
Shaip uses structured quality workflows, reviewer calibration, project-specific guidelines, quality checks, and human-in-the-loop review to maintain annotation accuracy. Projects typically begin with a pilot batch to validate taxonomy, edge-case rules, acceptance criteria, and reviewer alignment before scaling to full production annotation.
Shaip supports secure handling of sensitive data through privacy, compliance, and access-control workflows. For regulated projects, Shaip can support de-identification, NDA-bound teams, controlled access, auditability, secure cloud delivery, and compliance-aligned processes for standards such as HIPAA, GDPR, ISO 27001, ISO 9001, and SOC 2.
Computer vision project timelines depend on data volume, annotation complexity, number of object classes, QA depth, tool setup, and review cycles. Pilot batches often help define throughput and quality benchmarks before full production. Large enterprise projects are commonly delivered in phased batches with continuous feedback and quality reporting.
The cost of computer vision services depends on the data type, annotation method, project volume, object complexity, number of classes, QA requirements, domain expertise, security needs, and turnaround time. Shaip scopes pricing based on the required workflow, pilot results, delivery format, and production scale.
Shaip helps enterprises build production-ready computer vision datasets through scalable data collection, image and video annotation, 3D annotation, human-in-the-loop quality review, and compliance-focused delivery. With experience across healthcare, autonomous systems, retail, robotics, and other AI use cases, Shaip supports complex visual AI projects from pilot to production.
