Image Annotation Services

Labeling assigns a single tag to an entire image, for example, marking a photo as “cat.” Annotation is more detailed and involves marking specific regions within the image, like drawing a box around the cat or tracing its outline. Labeling tells the model what is in the image; annotation tells it where.

AI models can only learn from the data they are trained on. Annotations serve as the ground truth; without them, the model has no reference point to learn from. The more accurate and consistent the annotations, the better the model performs in real-world conditions.

A general starting point is 300–500 annotated images per object class for simpler tasks. More complex or varied objects typically require several thousand. Using transfer learning can reduce this number, but having diverse, well-annotated data consistently leads to stronger model performance.

YOLO uses one .txt  file per image. Each line in that file represents a single object and contains its class ID along with its center position, width, and height, all expressed as values between 0 and 1 relative to the image size. A separate file maps each class ID to its corresponding label name.

Start with clear, detailed annotation guidelines before the project begins. Use a two-step review process where a second person checks every annotation. Regularly compare work across annotators to ensure consistency, and track agreement scores to catch errors before they affect model training.

Both have their place. Manual annotation is more precise and handles complex or ambiguous cases well, but it is time-consuming and costly at scale. Automated annotation is fast and efficient, but requires human review to maintain accuracy. 

AnnotationBox delivers 98-99% annotation accuracy across typical project types, validated through our four-tier QA process: annotator self-review, peer review with inter-annotator agreement (IAA) scoring, senior validator review, and automated schema validation. For complex medical or geospatial projects, accuracy rates are project-scoped during discovery. We share QA metrics and IAA scores with clients on request.

Pricing depends on annotation type, dataset complexity, object density, and project volume. As a reference, bounding box annotation typically ranges from $0.02 to $0.10 per annotation, while semantic segmentation can range from $0.50 to $3.00 per image. We provide a detailed written quote after a free consultation. Volume discounts apply above 100,000 annotations.

Our annotators work with CVAT, Label Studio, Labelbox, and can also operate within client-specified annotation platforms. For high-volume projects, we use AI-assisted pre-labeling to generate draft annotations that trained annotators then review and correct, accelerating delivery by 30-50%.

Client data is transferred and stored with TLS 1.2+ encryption. Annotator access is restricted to assigned batches only, with no data download capability. For medical projects, we operate HIPAA-compliant workflows including BAA execution. For EU data, we operate under GDPR-compliant DPAs. All staff sign comprehensive NDAs. Project data is purged on completion.

Yes. AnnotationBox’s 1,000+ trained annotators can scale to projects requiring millions of annotated images. Our largest ongoing clients run continuous annotation pipelines with weekly delivery milestones. For large-scale projects, we can dedicate specific annotation teams to your account for consistency across the full dataset.

Yes. AnnotationBox provides a HIPAA-compliant medical image annotation company covering radiology (CT, MRI, X-ray), pathology slides, ophthalmology images, and surgical video. Annotators on medical projects are trained in clinical terminology and domain-appropriate labeling practices. We work under BAAs for all healthcare clients.

Turnaround time depends on dataset size and complexity. Small pilot batches (under 1,000 images) can typically be delivered within 3-5 business days. Mid-scale projects (10,000-50,000 images) typically complete in 2-4 weeks with daily delivery milestones. Enterprise-scale pipelines are scoped with custom delivery schedules during project discovery.

Absolutely. We recommend every new client begin with a paid pilot batch of 50-200 representative images. The pilot serves two purposes: it validates that our annotators correctly interpret your guidelines, and it gives you a concrete quality benchmark before scaling. Pilot results are typically delivered within 48-72 hours of guideline approval.