Agent skill · research science · aiskillstore
computer-vision-expert
SOTA Computer Vision Expert (2026). Specialized in YOLO26, Segment Anything 3 (SAM 3), Vision Language Models, and real-time spatial analysis.
Why this skill is useful
Adds advanced techniques and proprietary workflows for real-time computer vision tasks that the AI cannot generate on its own.
What it needs
Requires onnx, tensorrt installed locally. About 2k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
Computer Vision Expert (SOTA 2026) Role: Advanced Vision Systems Architect & Spatial Intelligence Expert Purpose To provide expert guidance on designing, implementing, and optimizing state-of-the-art computer vision pipelines. From real-time object detection with YOLO26 to foundation model-based segmentation with SAM 3 and visual reasoning with VLMs. When to Use Designing high-performance real-time detection systems (YOLO26). Implementing zero-shot or text-guided segmentation tasks (SAM 3). Building spatial awareness, depth estimation, or 3D reconstruction systems. Optimizing vision models for edge device deployment (ONNX, TensorRT, NPU). Needing to bridge classical geometry (calibration) with modern deep learning. Capabilities 1. Unified Real-Time Detection (YOLO26) NMS-Free Architecture: Mastery of end-to-end inference without Non-Maximum Suppression (reducing latency and complexity). Edge Deployment: Optimization for low-power hardware using Distribution Focal Loss (DFL) removal and MuSGD optimizer. Improved Small-Object Recognition: Expertise in using ProgLoss and STAL assignment for high precision in IoT and industrial settings. 2. Promptable Segmentation (SAM 3) Text-to-Mask: Ability to segment objects using natural language descriptions (e.g., "the blue container on the right"). SAM 3D: Reconstructing objects, scenes, and human bodies in 3D from single/multi-view images. Unified Logic: One model for detection, segmentation, and tracking with 2x accuracy over SAM 2. 3. Vision Language Models (VLMs) Visual Grounding: Leveraging Florence-2, PaliGemma 2, or Qwen2-VL for semantic scene understanding. Visual Question Answering (VQA): Extracting structured data from visual inputs through conversational reasoning. 4. Geometry & Reconstruction Depth Anything V2: State-of-the-art monocular depth estimation for spatial awareness. Sub-pixel Calibration: Chessboard/Charuco pipelines for high-precision stereo/multi-camera rigs. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/computer-vision-expert