Cv detection

Skill thada2402/AutoResearchClaw/researchclaw/skills/builtin/domain/cv-detection

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

Install

npx -y skills add thada2402/AutoResearchClaw --skill cv-detection

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SKILL.md

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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)

What ships with it

Read from the repository

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Gives 0 of the 12 instructions most hr recruiting skills give in 177 tokens

Counted across 356 of the 357 authors here whose files we hold, read 2026-08-07

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Said here and by no other author read

use a pre-trained ImageNet backbone
apply multi-scale training and testing
use an IoU threshold of 0.5 for mAP50
use FPN for multi-scale feature extraction
apply focal loss for class imbalance in one-stage detectors

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.