receiving-code-review
OBobra/superpowers298k近 7 天新增 2,631 个 star
cv-detection
Best practices for object detection tasks. Use when working on COCO, VOC, or detection architectures like YOLO and DETR.
- Star
- 14,635
- 分类
- 编码与调试
- 许可证
- MIT
- 最近提交
- 2026年8月19日
researchclaw/skills/builtin/domain/cv-detection/SKILL.md
在 GitHub 上查看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)
相似的 Skill
requesting-code-review
OBobra/superpowers298k近 7 天新增 2,631 个 star
systematic-debugging
OBobra/superpowers298k近 7 天新增 2,631 个 star
code-review
MAmattpocock/skills288k近 7 天新增 10,178 个 star
codebase-design
MAmattpocock/skills288k近 7 天新增 10,178 个 star
scaffold-exercises
MAmattpocock/skills288k近 7 天新增 10,178 个 star