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데이터 공유
원시 이미지, 비디오, 텍스트, 오디오 또는 LiDAR를 안전하게 업로드하십시오. 클라우드 스토리지, SFTP 또는 기존 ML 파이프라인에서 수집합니다.
Annotation service
Bounding Box Annotation Services
Enterprise bounding box annotation for object detection — retail SKUs, vehicles, people, and industrial assets with multi-tier QA.
- Pixel-tight boxes on difficult captures
- Multi-class taxonomies at scale
- COCO and YOLO exports
- 99.5% QA targets
Service overview
Bounding boxes remain the workhorse of computer vision detection pipelines. Our bounding box annotation services deliver consistent, guideline-driven boxes for SKUs, vehicles, people, equipment, and custom classes — with QA depth that keeps recall and precision stable in production.
Precision boxes that models can learn from
Tight axis-aligned and rotated boxes with clear rules for truncation, overlap, and class ambiguity — reducing false positives when models deploy on messy field data.
Scale without sacrificing review
Dedicated detection pools, golden-set benchmarking, and weekly error mining as taxonomies grow from dozens to thousands of classes.
Tooling and export flexibility
Label in your toolchain or our workspace; export COCO JSON, YOLO, Pascal VOC, or custom schemas aligned to your training jobs.
QA built for detection metrics
Inter-annotator agreement on hard examples, auditor consensus, and mAP-oriented sampling on validation batches before release.
Enterprise security and SLAs
Encrypted ingest, role-based access, GDPR-ready handling, and 24/7 throughput for global ML release cadences.
Get started
Scope your bounding box program with our enterprise team — volume, class count, accuracy targets, and timeline — and receive a pilot plan within one business day.
Industries we serve
Our annotation process
엔터프라이즈 주석 프로그램을 위한 검증된 캘리브레이션-투-프로덕션 워크플로.
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프로젝트 분석
귀사의 ML 및 제품 이해관계자와 함께 레이블링 지침, 클래스 분류 체계, 엣지 케이스 및 정확도 목표를 정의합니다.
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주석
훈련된 주석가가 귀사의 툴체인 또는 당사 작업 공간에서 바운딩 박스, 마스크, 트랙, 전사 또는 3D 큐보이드를 레이블링합니다.
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품질 보증
모든 데이터 세트가 학습 작업에 도달하기 전에 다중 패스 검토, 합의 점수 매기기 및 자동화된 검사를 수행합니다.
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배송 및 지원
COCO, JSON, Pascal VOC 또는 사용자 지정 내보내기를 받고, 모델 및 분류 체계가 발전함에 따라 지속적인 지원을 받으세요.
Service FAQ
Answers about scope, quality, tooling, and delivery.
Bounding boxes are ideal for object detection when approximate rectangular regions suffice. Polygons suit irregular shapes like shelves, lanes, or medical regions.
Yes. Guidelines define minimum visible area, truncation rules, and consensus review for heavily occluded objects.
Retail product detection, security person/vehicle boxes, automotive ADAS, agriculture drone surveys, and warehouse logistics.
Ready to start your bounding box annotation services project?
Talk to our enterprise team about volume, timeline, QA targets, and pricing.