Cv Detection
Best practices for object detection tasks.
Install
npx skills add aiming-lab/AutoResearchClaw --skill cv-detection -g -a claude-code -y- Repository
- aiming-lab/AutoResearchClaw14.8k
- Category
- Engineering
- Updated
- 3mo ago
- License
- MIT
- Context cost
- Lightabout 137 tokens
- Runs scripts
- No, instructions only
What it does
Best practices for object detection tasks. Use when working on COCO, VOC, or detection architectures like YOLO and DETR.
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)
