ReddRadar
Agent skill
cv-detection
Best practices for object detection tasks. Use when working on COCO, VOC, or detection architectures like YOLO and DETR.
Install this agent skill to your Project
npx add-skill https://github.com/aiming-lab/AutoResearchClaw/tree/main/researchclaw/skills/builtin/domain/cv-detection
Metadata
Additional technical details for this skill
- author
- researchclaw
- version
- 1.0
- category
- domain
- priority
- 5
- references
- Ren et al., Faster R-CNN, NeurIPS 2015; Carion et al., End-to-End Object Detection with Transformers, ECCV 2020
- trigger keywords
- detection,object,bbox,yolo,coco,anchor,faster rcnn
- applicable stages
- 9,10
SKILL.md
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)
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