「detect」の検索結果

Training, inference, and deployment workflows for DAMO-YOLO object detection.

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 DeepFace for face recognition, verification, embeddings, face detection, demographic analysis, datastore search, API serving, model selection, and troubleshooting.

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。

Route D-FINE object-detection setup, training, architecture inspection, inference, export, and troubleshooting workflows.

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。

Routes Darkflow object-detection, training, and export workflows for the legacy YOLO 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。

Guide Det3D PyTorch 3D object-detection workflows across configuration, KITTI/nuScenes/Lyft data preparation, GPU training and evaluation, custom CUDA operations, and visualization.

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。

Routes CenterNet object-detection training, evaluation, configuration, and setup workflows.

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。

Routes BEVFormer camera-only 3D detection workflows, from install/import and config inspection through nuScenes data prep, distributed training and evaluation, and log 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。

Use anomalib for anomaly-detection install, data/model selection, training and evaluation, deployment and inference, and benchmark pipelines.

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 IBM AI Fairness 360 for tabular fairness datasets, metrics, bias mitigation algorithms, sklearn-compatible workflows, subgroup detectors, and explainers.

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。

Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, passive voice, negative parallelisms, and filler phrases.

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。

Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.

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。

Turn long videos & livestream VODs into viral vertical shorts, 100% locally — on-device transcription, LLM highlight detection, 9:16 reframe with karaoke captions, and a per-clip render-QA report. Use when the user asks to clip / cut / 切片 / 剪 a long video, podcast or stream replay into short clips, find highlights / 爆点 in a video, or transcribe a media file. Footage never leaves the machine.

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。

GraphQL IDOR/auth-bypass, XXE file-read/SSRF (SVG/DOCX/SAML), SSTI detection and RCE. OOB-mandatory for blind XXE. Wiki-first, FIND schema output.

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。

Adaptive, targeted web fuzzing - deterministic wordlist selection (wl-pick.sh) plus judgment. Picks the right SecLists list per surface (content/vhost/api/params/artifacts) smallest-first, calibrates filters against soft-404s, recurses, escalates T0 harness -> T1 seclists -> T2 cewl -> T3 app-specific on signal, pivots to hidden-param fuzzing, and detects/handles WAF/Cloudflare/throttle (backoff, origin-bypass, or hard STOP on the DoS tell). Engagement-type aware (ctf loud, pt calibrated, bb stealth). Use for "fuzz", "content discovery", "directory brute", "vhost fuzz", "find hidden params", "which wordlist", "gobuster/ffuf/feroxbuster/cewl/arjun".

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。

HTTP request smuggling / desync hunting - CL.TE, TE.CL, TE.TE, CL.0, and HTTP/2 downgrade. Timing-based detection, differential confirmation, no-blind-claims. Wiki-first, FIND schema output.

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。

Insecure deserialization hunting across Java / .NET / PHP / Python / Ruby / Node. Gadget-chain RCE, OOB-gated blind detection, magic-byte fingerprinting. Wiki-first, FIND schema output.

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。

Validate, lint, audit, or fix PromQL queries and alerting rules; detects anti-patterns.

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。

Character archetype art direction for Blender covering fantasy races, horror creatures, roles (knight, assassin, detective), and body types (mecha, android, chibi, etc.).

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。

AI-powered E2E web testing — eyes and hands for AI coding tools. Declarative YAML scenarios, Playwright execution, visual matching (OpenCV + OCR), platform auto-detection (Flutter/React/Vue), learning DB. Install: npx skills add ksgisang/awt-skill --skill awt -g

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。

Audits GitHub Actions workflows for security vulnerabilities in AI agent integrations including Claude Code Action, Gemini CLI, OpenAI Codex, and GitHub AI Inference. Detects attack vectors where attacker-controlled input reaches. AI agents running in CI/CD pipelines.

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。

Generate project-specific AGENTS.md and companion rules by analyzing a codebase. Supports full, minimal, update, and dry-run modes with package-manager detection, monorepos, backups, managed blocks, confidence scoring, and command validation.

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 generating or refactoring Spring Boot 4 JPA entities, repositories, queries, projections, persistence tests, entity relationships, embeddables, IDs, or Hibernate mappings. Covers Jakarta Persistence 3.2 imports, Hibernate 7 entity modeling, new-state detection, N+1 prevention, projections, keyset pagination, batch writes, and common agent mistakes.

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。

Universal opt-out mechanism implementation across US state privacy laws. Covers Global Privacy Control (GPC) signal technical implementation, state-by-state recognition requirements, browser detection methods, authenticated vs unauthenticated handling, and compliance testing.

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。

Conduct LINDDUN privacy threat modeling across all seven categories: Linking, Identifying, Non-repudiation, Detecting, Data Disclosure, Unawareness, and Non-compliance. Includes DFD-based analysis, threat trees, privacy-specific mitigation strategies, and integration with STRIDE security threat modeling.

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。