'labeling' 검색 결과

Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).

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Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).

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Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).

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Estimate intracellular reaction rates from steady-state carbon-13 tracing measurements. Validate carbon atom mappings and measurement inputs, simulate labeling, fit constrained fluxes, and diagnose which pathway rates the data can meaningfully constrain.

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• Estimate metabolic fluxes from carbon-13 labeling
• Validate atom mappings and measurement inputs
• Simulate labeling for proposed reaction rates

Use when running video data augmentation and auto-labeling workflows on OSMO: flow selection, preflight, submit-time interpolation, monitoring, and output retrieval. Trigger keywords: video data augmentation, data enrichment, auto labeling, VDA demo, OSMO workflow, pseudo labeling.

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Operate AnyLabeling desktop annotation, auto-labeling model, dataset export, install, packaging, and release workflows for the PyQt6/ONNX image-labeling package.

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Implements data classification labels and tagging systems including metadata tagging, DLP integration, automated label propagation, user-applied labels, and label inheritance rules. Covers Microsoft Purview sensitivity labels and enterprise labeling architecture. Keywords: data labeling, sensitivity labels, metadata tagging, DLP integration, label propagation, Purview, classification.

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Develops data classification policies with tiered handling (public, internal, confidential, restricted), labeling requirements, enforcement mechanisms, and procedures per tier. Covers policy governance, exception handling, and compliance monitoring. Keywords: classification policy, data tiers, handling procedures, labeling, enforcement, data governance, information security.

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Set up and manage data labeling workflows using manual annotation tools, semi-automated pipelines, active learning, and programmatic weak supervision. Use when the user requests data labeling or provides relevant inputs for this workflow.

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Set up systematic data labeling workflows using Label Studio or similar tools. Implement quality controls, measure inter-annotator agreement, manage labeler teams, and integrate labeled data into ML training pipelines. Use when starting a supervised ML project that requires labeled training data, when model performance is limited by insufficient labeled examples, when labeling text, images, audio, or video, or when implementing active learning to prioritize the most valuable examples.

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Data Labeling Specialist IA — Expert en annotation de données (outils, contrôle qualité, agreement inter-annotateurs, active learning)

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Review open Mastra GitHub issues and trace reported defects to source code. Apply the @mastra/core label to confirmed core bugs, report uncertain ownership, and summarize whether fixes are active, stale, or unclaimed.

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• Checking whether reported bugs belong to @mastra/core
• Labeling confirmed core bugs with supporting evidence
• Previewing issue classifications without changing GitHub

Manage local projects for labeling text, images, audio, and video through the host application's MCP tools. Create projects, update labels and annotations, review saved work, and export approved results as JSON.

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• Create a text labeling project
• Update annotations and label names
• Review saved annotation results

Use when a user describes a custom PAIDF Orchestration pipeline — a specific ordered combination of stages such as augmentation only, auto-labeling only, detection+captioning only, or image attribute augmentation without full auto-labeling — that no existing DAG in airflow/dags/workflows/ covers, and asks for a new Kubernetes DAG. Also use to check that a generated or existing DAG's model/container/prompt choices match an external spec document (e.g. a PAIDF `launchable.md`).

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Use when a user needs to get started with PAIDF Auto-Labeling, plan a scenario, run or debug a shipped cookbook, author prompts or cookbooks, migrate a pipeline, or configure a stage. Confirm critical inputs (data path, output path, endpoints) and ask when any are missing. This is a router: read the matching reference instead of inventing a workflow.

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Protein quantification from mass spectrometry data including label-free (LFQ, intensity-based), isobaric labeling (TMT, iTRAQ), and metabolic labeling (SILAC) approaches. Use when extracting protein abundances from MS data for differential analysis.

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Work with hyperspectral and multispectral images in MATLAB. Covers reading/writing (ENVI, NITF, TIFF, Sentinel-2, Landsat, ASTER), ECOSTRESS spectral libraries, processing (calibration, atmospheric correction, denoising, sharpening, dimensionality reduction, endmember extraction, unmixing, target/anomaly detection, spectral indices, segmentation), labeling (Spectral Image Labeler app, ground truth objects, automation algorithms), and deep learning (pixel classification CNNs, unmixing autoencoders, transfer learning). Use when reading, writing, processing, analyzing, classifying, labeling, or applying deep learning to hyperspectral or multispectral images.

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Design the structure of a website or product including sitemap, navigation, URL structure, content types, taxonomy, and labeling. Use this skill whenever the user asks to plan a sitemap, design navigation, structure URLs, define content types, build taxonomies, design site search, or organize content at the system level. Triggers on sitemap, site structure, navigation, IA, information architecture, URL structure, content types, taxonomy, categorization, breadcrumbs, hub pages, faceted navigation, site search, labeling. Also triggers when content is being created without a structural plan, or when an existing site's structure is being audited or restructured.

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Build a product-specific Amazon FBA prep, labeling, packing, and shipment-readiness plan. Use when a seller asks how to prepare units for FBA, choose barcode and labeling steps, plan poly-bagging, bubble wrap, sets, case packs, cartons, dangerous-goods review, supplier or prep-center instructions, or prevent receiving delays. Do not use as a substitute for current Seller Central requirements.

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Collect and preserve insect specimens following museum-grade standards including legal compliance, collection methods, humane dispatch, dry pinning, wet preservation, labeling, storage, and curation. Covers permit requirements, protected species regulations, sweep nets, beating trays, pitfall traps, light traps, Malaise traps, aspirators, ethyl acetate killing jars, freezing, pin placement by order, wing spreading, ethanol preservation for soft-bodied specimens, specimen labeling with locality and date, storage with pest management, and database entry. Use when building a reference collection for taxonomic study, preserving voucher specimens for ecological research, preparing specimens for identification by specialists, or curating an existing collection.

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Expert en annotation de données avancé (annotation, quality, active learning, consensus, tools)

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음성 파일을 OpenAI 전사 API로 보내 결과를 텍스트나 JSON으로 저장합니다. 모델과 언어를 지정하고 화자별로 발언을 구분할 수 있습니다.

• 회의 녹음을 텍스트로 변환합니다
• 인터뷰에서 화자를 구분합니다
• 전사 결과를 JSON으로 저장합니다

React와 Next.js의 폼과 모달을 키보드와 화면 낭독기로 사용할 수 있도록 돕습니다. 레이블, 오류 표시, 포커스 이동을 구현하고 점검합니다.

• 폼 레이블과 오류 표시를 개선합니다
• 드롭다운의 키보드 조작을 점검합니다
• 모달을 열고 닫을 때 포커스를 관리합니다

뇌파 등을 측정하는 기기를 NeuroSkill과 연결해 집중·기분·졸음 지표를 확인합니다. 과거 기록 비교, 수면 단계 검토, 휴식 제안에도 활용합니다.

• 현재 집중·이완 지표 확인
• 기록된 세션 간 집중 변화 비교
• 기록된 수면 단계 돌아보기