hikaru122700/cim-cac-maxcut-g22/cv-detection
Claude Code and agent skill by hikaru122700/cim-cac-maxcut-g22
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Checking that the SKILL.md file is still in the repository.
Best practices for object detection tasks. Use when working on COCO, VOC, or detection architectures like YOLO and DETR.
Install
npx vskill@latest install hikaru122700/cim-cac-maxcut-g22/cv-detectionSKILL.md
The same SKILL.md was published earlier as aiming-lab/autoresearchclaw/cv-detection.
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)
Security
- Security scanPassed, 90 of 100Pattern checks on the skill's files.
- Model reviewPassed
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingAutoResearchClaw/researchclaw/skills/builtin/domain/cv-detection/SKILL.md
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