cv-detection
Best practices for object detection tasks. Use when working on COCO, VOC, or detection architectures like YOLO and DETR.
DeepseekModel
官方收录技能
质量 优秀 · 90
v1.0.0
获取
https://deepseekmodel.com/api/download.php?id=aiming-lab-autoresearchclaw-researchclaw-skills-builtin-domain-cv-detection-skill-md&format=skill
下载 .skill
标准格式，含 system_prompt 与 model_config，导入任意 Agent 框架即可使用
.skill 文件中 system_prompt 字段的实际内容。
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)
Agent 识别该技能的关键词，点击任意一个即可复制。
该技能未提供触发词。
下载的 .skill 包内含以下字段。
| 字段 | 说明 |
|---|---|
| format | 格式标识（skill/v1） |
| skill_id | 技能唯一 ID |
| name | 技能名称 |
| version | 版本号 |
| description | 技能描述 |
| category | 所属分类（数组） |
| trigger_words | 触发词列表 |
| tags | 标签列表 |
| source | 来源标识 |
| source_url | 来源链接（本页地址） |
| exported_at | 导出时间（每次下载生成） |
| system_prompt | 系统提示词正文 |
| model_config | 模型参数：provider / model / temperature / max_tokens / top_p |
| examples | 示例 |
| install_guide | 各平台导入说明（Coze / Dify / Claude / 自定义框架） |
