Image Annotation Services

Yes. Our image annotation teams can work within client-managed platforms such as CVAT, Labelbox, V7, Scale AI, Supervisely, Label Studio, or proprietary in-house tools. We follow your existing data schema, class hierarchy, ontology, annotation rules, and review workflow. Final datasets can be exported in formats such as COCO, YOLO, Pascal VOC, JSON, CSV, RLE, PNG masks, Parquet, XML, or custom specifications, depending on your ML pipeline.

Mid-project changes are handled through recalibration. We update the annotation guidelines, retrain the affected annotators, run a validation batch against the revised rules, and review previously labeled data to decide whether it needs relabeling, selective correction, or schema mapping. This helps maintain consistency between older and newer batches even when your taxonomy, class definitions, or model requirements evolve.

Annotators are trained to flag unclear images instead of forcing a label. This may include occluded objects, low-light images, overlapping instances, ambiguous class boundaries, unusual object angles, dense scenes, or cases not covered by the current guidelines.

Flagged items are reviewed by QA leads or subject matter reviewers. If the case requires your involvement, we escalate the same to your team, document the final ruling, and add it to the project guidelines so the full annotation team handles similar cases consistently going forward.

Overall, 95-99%. Annotation accuracy depends on the image quality, labeling method, class complexity, and QA depth required for the project. For quality-critical datasets, we use inter-annotator agreement checks, reviewer audits, consensus review, and revision tracking to measure consistency before delivery. For high-risk use cases such as autonomous vehicles, healthcare, industrial inspection, or security AI, we can add domain reviewer validation so complex visual judgments are not handled by general annotators alone.

Turnaround is scoped after reviewing your dataset volume, annotation type, number of classes, image complexity, and quality requirements. Bounding boxes and classification tasks usually move faster than polygon annotation, segmentation masks, 3D cuboids, LiDAR labels, or dense object scenes.

Before production begins, we share a milestone-based delivery plan, so your team knows when to expect sample batches, production batches, review cycles, and final delivery. For urgent requirements, we can increase team capacity and run batches in parallel without removing QA checks.

Image annotation cost is project-specific. It depends on image volume, annotation technique, number of label classes, object density, image quality, QA requirements, turnaround expectations, and whether domain expert review is needed.

For example, simple image classification or bounding box annotation is generally scoped differently from pixel-level segmentation, 3D image annotation, LiDAR labeling, OCR correction, or dense scene annotation. After reviewing your requirements, we provide a custom estimate based on the actual project scope.

Yes. A pilot can be used to validate annotation quality, guideline clarity, tool compatibility, delivery format, turnaround, and review workflow before the full dataset moves into production. For complex projects, the pilot also helps identify edge cases, refine class definitions, test output formats, and confirm whether the annotation workflow is ready to scale.

Data-Entry-India follows ISO 27001:2022-certified information security processes, HIPAA-compliant handling for healthcare image datasets, GDPR-aligned data protection practices, and SOC 2 Type II-audited controls.

All annotators work under NDAs within access-controlled environments. Data is protected through encrypted transmission, secure cloud storage, and role-based access controls, so only authorized project members can access assigned datasets. Client images, annotations, and project-specific guidelines are never retained, reused, or repurposed after project completion.