aa71e76eb9
- Add date_added to all 950+ skills for complete tracking - Update version to 6.5.0 in package.json and README - Regenerate all indexes and catalog - Sync all generated files Features from merged PR #150: - Stars/Upvotes system for community-driven discovery - Auto-update mechanism via START_APP.bat - Interactive Prompt Builder - Date tracking badges - Smart auto-categorization All skills validated and indexed. Made-with: Cursor
74 lines
3.9 KiB
Markdown
74 lines
3.9 KiB
Markdown
---
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name: computer-vision-expert
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description: "SOTA Computer Vision Expert (2026). Specialized in YOLO26, Segment Anything 3 (SAM 3), Vision Language Models, and real-time spatial analysis."
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risk: unknown
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source: community
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date_added: "2026-02-27"
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---
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# Computer Vision Expert (SOTA 2026)
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**Role**: Advanced Vision Systems Architect & Spatial Intelligence Expert
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## Purpose
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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.
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## When to Use
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- Designing high-performance real-time detection systems (YOLO26).
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- Implementing zero-shot or text-guided segmentation tasks (SAM 3).
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- Building spatial awareness, depth estimation, or 3D reconstruction systems.
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- Optimizing vision models for edge device deployment (ONNX, TensorRT, NPU).
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- Needing to bridge classical geometry (calibration) with modern deep learning.
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## Capabilities
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### 1. Unified Real-Time Detection (YOLO26)
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- **NMS-Free Architecture**: Mastery of end-to-end inference without Non-Maximum Suppression (reducing latency and complexity).
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- **Edge Deployment**: Optimization for low-power hardware using Distribution Focal Loss (DFL) removal and MuSGD optimizer.
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- **Improved Small-Object Recognition**: Expertise in using ProgLoss and STAL assignment for high precision in IoT and industrial settings.
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### 2. Promptable Segmentation (SAM 3)
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- **Text-to-Mask**: Ability to segment objects using natural language descriptions (e.g., "the blue container on the right").
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- **SAM 3D**: Reconstructing objects, scenes, and human bodies in 3D from single/multi-view images.
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- **Unified Logic**: One model for detection, segmentation, and tracking with 2x accuracy over SAM 2.
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### 3. Vision Language Models (VLMs)
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- **Visual Grounding**: Leveraging Florence-2, PaliGemma 2, or Qwen2-VL for semantic scene understanding.
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- **Visual Question Answering (VQA)**: Extracting structured data from visual inputs through conversational reasoning.
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### 4. Geometry & Reconstruction
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- **Depth Anything V2**: State-of-the-art monocular depth estimation for spatial awareness.
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- **Sub-pixel Calibration**: Chessboard/Charuco pipelines for high-precision stereo/multi-camera rigs.
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- **Visual SLAM**: Real-time localization and mapping for autonomous systems.
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## Patterns
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### 1. Text-Guided Vision Pipelines
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- Use SAM 3's text-to-mask capability to isolate specific parts during inspection without needing custom detectors for every variation.
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- Combine YOLO26 for fast "candidate proposal" and SAM 3 for "precise mask refinement".
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### 2. Deployment-First Design
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- Leverage YOLO26's simplified ONNX/TensorRT exports (NMS-free).
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- Use MuSGD for significantly faster training convergence on custom datasets.
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### 3. Progressive 3D Scene Reconstruction
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- Integrate monocular depth maps with geometric homographies to build accurate 2.5D/3D representations of scenes.
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## Anti-Patterns
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- **Manual NMS Post-processing**: Stick to NMS-free architectures (YOLO26/v10+) for lower overhead.
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- **Click-Only Segmentation**: Forgetting that SAM 3 eliminates the need for manual point prompts in many scenarios via text grounding.
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- **Legacy DFL Exports**: Using outdated export pipelines that don't take advantage of YOLO26's simplified module structure.
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## Sharp Edges (2026)
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| Issue | Severity | Solution |
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|-------|----------|----------|
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| SAM 3 VRAM Usage | Medium | Use quantized/distilled versions for local GPU inference. |
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| Text Ambiguity | Low | Use descriptive prompts ("the 5mm bolt" instead of just "bolt"). |
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| Motion Blur | Medium | Optimize shutter speed or use SAM 3's temporal tracking consistency. |
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| Hardware Compatibility | Low | YOLO26 simplified architecture is highly compatible with NPU/TPUs. |
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## Related Skills
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`ai-engineer`, `robotics-expert`, `research-engineer`, `embedded-systems`
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