# Detection Segmentation Pipeline

> Object detection and segmentation pipelines. COCO/VOC formats, DETR/YOLO/Mask2Former, mAP/mIoU evaluation.

- Skill: `aselimc/detection-segmentation-pipeline` (Agent Skill)
- Install (CLI): `npx skillmds@latest add aselimc/detection-segmentation-pipeline`
- Raw SKILL.md: https://api.skillmd.com/api/skills/aselimc/detection-segmentation-pipeline/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: aselimc (https://skillmd.com/u/aselimc)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/aselimc/detection-segmentation-pipeline

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# Detection & Segmentation Pipeline

## Data Formats
- **COCO**: JSON annotations, `[x, y, w, h]` bbox format
- **VOC**: XML per image, `[xmin, ymin, xmax, ymax]`
- **YOLO**: txt per image, `[class cx cy w h]` normalized

## Model Selection
| Task | Recommended | Alternative |
|------|------------|-------------|
| 2D Detection | RT-DETR, YOLOv8 | Faster R-CNN, DINO |
| Instance Seg | Mask2Former | Mask R-CNN |
| Semantic Seg | SegFormer, Mask2Former | DeepLabv3+ |
| Panoptic | Mask2Former | - |
| 3D Detection | CenterPoint, PointPillars | VoxelNet |

## Evaluation
- Detection: COCO mAP (AP@0.5, AP@0.5:0.95, AP_S/M/L)
- Segmentation: mIoU, per-class IoU, boundary F1
- Always report FLOPs and inference latency alongside accuracy

## Key Libraries
mmdetection, detectron2, ultralytics, torchvision