# Cv Detection

Best practices for object detection tasks.

- Repository: https://github.com/aiming-lab/AutoResearchClaw (14798 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-08-19
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add aiming-lab/AutoResearchClaw --skill cv-detection -g -a claude-code -y`
Codex: `npx skills add aiming-lab/AutoResearchClaw --skill cv-detection -g -a codex -y`
Cursor: `npx skills add aiming-lab/AutoResearchClaw --skill cv-detection -g -a cursor -y`
Gemini CLI: `npx skills add aiming-lab/AutoResearchClaw --skill cv-detection -g -a gemini-cli -y`

## Description

Best practices for object detection tasks. Use when working on COCO, VOC, or detection architectures like YOLO and DETR.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

## Object Detection Best Practice
Architecture families:
- One-stage: YOLO (v5/v8), SSD, RetinaNet, FCOS
- Two-stage: Faster R-CNN, Cascade R-CNN
- Transformer: DETR, DINO, RT-DETR

Training recipe:
- Use pre-trained backbone (ImageNet)
- Multi-scale training and testing
- IoU threshold: 0.5 for mAP50, 0.5:0.95 for mAP
- Use FPN for multi-scale feature extraction
- Focal loss for class imbalance in one-stage detectors

Standard benchmarks:
- COCO val2017: ~37 mAP (Faster R-CNN R50), ~51 mAP (DINO Swin-L)
- Pascal VOC: ~80 mAP50 (Faster R-CNN)