cv-detection
Best practices for object detection tasks. Use when working on COCO, VOC, or detection architectures like YOLO and DETR.
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v1.0.0
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name cv-detection description Best practices for object detection tasks. Use when working on COCO, VOC, or detection architectures like YOLO and DETR. metadata {"category":"domain","trigger-keywords":"detection,object,bbox,yolo,coco,anchor,faster rcnn","applicable-stages":"9,10","priority":"5","version":"1.0","author":"researchclaw","references":"Ren et al., Faster R-CNN, NeurIPS 2015; Carion et al., End-to-End Object Detection with Transformers, ECCV 2020"} 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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| Field | Description |
|---|---|
| format | Format tag (skill/v1) |
| skill_id | Unique skill ID |
| name | Skill name |
| version | Version |
| description | Description |
| category | Categories (array) |
| trigger_words | Trigger words |
| tags | Tags |
| source | Source |
| source_url | Source URL (this page) |
| exported_at | Exported at (set per download) |
| system_prompt | System prompt body |
| model_config | Model config: provider / model / temperature / max_tokens / top_p |
| examples | Examples |
| install_guide | Import guide for Coze / Dify / Claude / custom frameworks |
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