24건 · 관련도순
Computer vision engineering for object detection, segmentation, and visual AI, covering CNN and Vision Transformer architectures and ONNX/TensorRT deployment. Use when building detection pipelines, training models, or optimizing inference.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Computer Vision v2 IA — Expert en vision par ordinateur (détection d'objets, segmentation, OCR, vidéo, déploiement edge)
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Computer Vision Specialist IA — Expert en vision par ordinateur (YOLO, OpenCV, object detection, OCR, segmentation)
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build visual AI systems that detect objects or divide images into labeled regions. This skill guides dataset preparation, model selection, training, evaluation, inference optimization, and deployment using frameworks such as PyTorch and Ultralytics.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
World-class computer vision skill for image/video processing, object detection, segmentation, and visual AI systems. Expertise in PyTorch, OpenCV, YOLO, SAM, diffusion models, and vision transformers. Includes 3D vision, video analysis, real-time processing, and production deployment. Use when building vision AI systems, implementing object detection, training custom vision models, or optimizing inference pipelines.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Operate CVAT for computer-vision annotation, dataset workflows, SDK/CLI automation, auto-annotation, and self-hosted deployment.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Think and work like an expert Computer Vision Scientist. Use when a task calls for Computer Vision Scientist judgment. Reasons from image formation, projective geometry (pinhole intrinsics and extrinsics, epipolar/PnP/bundle adjustment, similarity-scale ambiguity), and COCO/LVIS AP mechanics through DINOv3/SigLIP 2 foundation baselines, RF-DETR/YOLO26/SAM 3 models, COLMAP 4/GLOMAP and VGGT geometry, pycocotools/TrackEval/BOP evaluation, and CVPR reporting and EU AI Act limits while treating train–test and pretraining leakage, AP evaluation-setting gaming, preprocessing mismatches (EXIF, BGR, aliased resizing), label noise, and camera-convention and scale errors as first-class failure modes.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Real-time face swap and video deepfake using a single source image. Use when: building face-swap applications, real-time video effects, virtual try-on features, AI video effects pipelines.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Computer vision expert specializing in real-time hand tracking and gesture interface designUse when "hand tracking, gesture recognition, mediapipe, hand gestures, touchless interface, sign language, hand pose, finger tracking, mediapipe, hand-tracking, gesture-recognition, computer-vision, hci, touchless, ml" mentioned.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when implementing object detection, semantic/instance segmentation, 3D vision, or video understanding - covers YOLO, SAM, depth estimation, and multi-modal visionUse when ", " mentioned.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Run a custom computer-vision prompt against an image with a local vision LLM. Wraps kuzco's tool_llm_image_custom() ellmer tool. Use when none of the specialized kuzco image skills (classification, OCR, recognition, sentiment, alt text) fit the task.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build image generation pipelines with Stable Diffusion, FLUX, ControlNet, LoRA, and ComfyUI workflows. Activate on: image generation pipeline, ComfyUI workflow, ControlNet, LoRA training, diffusion model. NOT for: video generation (ai-video-production-master), image classification (computer-vision-pipeline).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Expert in drone systems, computer vision, and autonomous navigation. Specializes in flight control, SLAM, object detection, sensor fusion, and path planning. Activate on "drone", "UAV", "SLAM", "visual odometry", "PID control", "MAVLink", "Pixhawk", "path planning", "A*", "RRT", "EKF", "sensor fusion", "optical flow", "ByteTrack". NOT for domain-specific inspection tasks like fire detection, roof damage assessment, or thermal analysis (use drone-inspection-specialist), GPU shader optimization (use metal-shader-expert), or general image classification without drone context (use clip-aware-embeddings).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build face recognition systems with InsightFace, ArcFace, enrollment pipelines, HDBSCAN clustering, and privacy-compliant architecture. Activate on: face recognition, face enrollment, face clustering, identity verification, facial search. NOT for: general object detection (computer-vision-pipeline), emotion analysis (ai-engineer).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build production computer vision pipelines for object detection, tracking, and video analysis. Handles drone footage, wildlife monitoring, and real-time detection. Supports YOLO, Detectron2, TensorFlow, PyTorch. Use for archaeological surveys, conservation, security. Activate on "object detection", "video analysis", "YOLO", "tracking", "drone footage". NOT for simple image filters, photo editing, or face recognition APIs.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Expert-level computer vision for robotics covering camera models, feature detection, object detection, pose estimation, visual SLAM, and deep learning for perception.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
SOTA Computer Vision Expert (2026). Specialized in YOLO26, Segment Anything 3 (SAM 3), Vision Language Models, and real-time spatial analysis.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Data scientist v3 — ML, deep learning, NLP, CV, experimentation, MLOps
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build face recognition systems with InsightFace, ArcFace, enrollment pipelines, HDBSCAN clustering, and privacy-compliant architecture. Activate on: face recognition, face enrollment, face clustering, identity verification, facial search. NOT for: general object detection (computer-vision-pipeline), emotion analysis (ai-engineer).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Expert in drone systems, computer vision, and autonomous navigation. Specializes in flight control, SLAM, object detection, sensor fusion, and path planning. Activate on "drone", "UAV", "SLAM", "visual odometry", "PID control", "MAVLink", "Pixhawk", "path planning", "A*", "RRT", "EKF", "sensor fusion", "optical flow", "ByteTrack". NOT for domain-specific inspection tasks like fire detection, roof damage assessment, or thermal analysis (use drone-inspection-specialist), GPU shader optimization (use metal-shader-expert), or general image classification without drone context (use clip-aware-embeddings).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build production computer vision pipelines for object detection, tracking, and video analysis. Handles drone footage, wildlife monitoring, and real-time detection. Supports YOLO, Detectron2, TensorFlow, PyTorch. Use for archaeological surveys, conservation, security. Activate on "object detection", "video analysis", "YOLO", "tracking", "drone footage". NOT for simple image filters, photo editing, or face recognition APIs.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.