Agent skill · research science · erichowens
computer-vision-pipeline
Build production computer vision pipelines for object detection, tracking, and video analysis. Handles drone footage, wildlife monitoring, and real-time detection. Supports YOLO, Detectron2,
Why this skill is useful
Adds executable scripts for preprocessing video frames and integrates multiple object detection models not commonly found in public documentation.
What it needs
Requires ffmpeg, pip, python installed locally. About 9k tokens when loaded. Last updated 2026-07-14. 180 stars on the source repository.
What this skill does
Computer Vision Pipeline Expert in building production-ready computer vision systems for object detection, tracking, and video analysis. When to Use ✅ Use for: Drone footage analysis (archaeological surveys, conservation) Wildlife monitoring and tracking Real-time object detection systems Video preprocessing and analysis Custom model training and inference Multi-object tracking (MOT) ❌ NOT for: Simple image filters (use Pillow/PIL) Photo editing (use Photoshop/GIMP) Face recognition APIs (use AWS Rekognition) Basic OCR (use Tesseract) --- Technology Selection Object Detection Models Model Speed (FPS) Accuracy (mAP) Use Case ------- ------------- ---------------- ---------- YOLOv8 140 53.9% Real-time detection Detectron2 25 58.7% High accuracy, research EfficientDet 35 55.1% Mobile deployment Faster R-CNN 10 42.0% Legacy systems Timeline: 2015: Faster R-CNN (two-stage detection) 2016: YOLO v1 (one-stage, real-time) 2020: YOLOv5 (PyTorch, production-ready) 2023: YOLOv8 (state-of-the-art) 2024: YOLOv8 is industry standard for real-time Decision tree: --- Common Anti-Patterns Anti-Pattern 1: Not Preprocessing Frames Before Detection Novice thinking: "Just run detection on raw video frames" Problem: Poor detection accuracy, wasted GPU cycles. Wrong approach: Why wrong: Video resolution too high (4K = 8.3 megapixels per frame) No normalization (pixel values 0-255 instead of 0-1) Aspect ratio not maintained GPU memory overflow on high-res frames Correct approach: Performance comparison: Raw 4K frames: 5 FPS, 72% mAP Preprocessed 640x640: 45 FPS, 89% mAP Timeline context: 2015: Manual preprocessing required 2020: YOLOv5 added auto-resize 2023: YOLOv8 has smart preprocessing but explicit control is better --- Anti-Pattern 2: Processing Every Frame in Video Novice thinking: "Run detection on every single frame" Problem: 99% of frames are redundant, wasting compute. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills erichowens/computer-vision-pipeline