「labeling」の検索結果

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).

日本語の概要は準備中です。原文の説明を表示しています。

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).

日本語の概要は準備中です。原文の説明を表示しています。

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).

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

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

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

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

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

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`).

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

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

日本語の概要は準備中です。原文の説明を表示しています。

音声ファイルをOpenAIの文字起こしAPIに送り、テキストやJSONで保存します。モデルや言語の指定、話者を区別した文字起こしにも対応するスキル。

• 会議の録音を文字起こししたいとき
• インタビューの話者を区別したいとき
• 文字起こしをJSONで保存したいとき

ReactやNext.jsのフォームやモーダルを、キーボード操作と画面読み上げに対応させるスキル。ラベル、エラー表示、フォーカス移動を実装・見直しします。

• フォームのラベルとエラー表示の改善
• ドロップダウンのキーボード操作確認
• モーダル開閉時のフォーカス管理

脳波などを測る機器とNeuroSkillをつなぎ、集中・気分・眠気の指標を確認するスキル。過去の記録との比較や睡眠の振り返り、休憩の提案にも使えます。

• 現在の集中・リラックス指標の確認
• 記録したセッション間の集中比較
• 記録した睡眠段階の振り返り

Estimates intracellular metabolic fluxes from steady-state carbon-13 isotope-tracing measurements using validated atom maps, mfapy isotope simulation, constrained multistart fitting, and flux-profile diagnostics. Use for 13C-MFA, carbon tracing, mass isotopomer distributions (MDVs/MIDs), positional isotopomers, parallel tracer experiments, and determining whether labeling data constrain a pathway flux. Distinguishes measured-label inference from COBRA flux balance analysis and flags experiments requiring nonstationary MFA.

日本語の概要は準備中です。原文の説明を表示しています。

Creates and manages annotation configs (categorical, continuous, freeform label schemas) and annotation queues (human review workflows) on Arize. Applies human annotations to project spans via the Python SDK. Use when the user mentions annotation config, annotation queue, label schema, human feedback, bulk annotate spans, update_annotations, labeling queue, annotate record, or human review.

日本語の概要は準備中です。原文の説明を表示しています。

Generates deep links to the Arize UI for traces, spans, sessions, datasets, labeling queues, evaluators, and annotation configs. Produces clickable URLs for sharing Arize resources with team members. Use when the user wants to link to or open a trace, span, session, dataset, evaluator, or annotation config in the Arize UI.

日本語の概要は準備中です。原文の説明を表示しています。