refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-segmentsai
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title: Segments.ai
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Segments.ai, segments-ai, CV Annotation Platform]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.85
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verification_status: applied
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tags: [computer-vision, annotation, labeling, dataset, mlops]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: python
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framework: segments-ai-sdk
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---
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# Segments.ai
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## 매 한 줄
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> **"매 computer vision 매 labeling platform — 2D/3D segmentation, point cloud, AI-assisted"**. 매 production tool for multi-modal CV datasets — 매 SAM 2 integration, lidar cuboid, semantic/instance/panoptic segmentation. 매 alternative: Roboflow, Scale AI, Labelbox, CVAT.
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## 매 핵심
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### 매 Modalities
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- **2D**: Bounding box, polygon, semantic, instance, panoptic, keypoint.
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- **3D point cloud**: 매 cuboid, segmentation (autonomous driving).
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- **Multi-sensor**: 매 synced lidar + camera (매 AV use case).
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- **Image sequence / video**: 매 tracking 가 supported.
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### 매 AI-assisted
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- 매 SAM 2 integration: 매 click → instance mask.
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- 매 model-in-the-loop: 매 your trained model 매 pre-label → human correct.
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- 매 active learning: 매 uncertain samples 매 priority queue.
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### 매 Dataset export
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- COCO, YOLO, Pascal VOC, Cityscapes formats.
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- HuggingFace `datasets` integration.
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- 매 versioning: 매 release immutable snapshots.
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### 매 응용
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1. Autonomous driving lidar+camera labeling.
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2. Medical imaging segmentation.
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3. Robotics grasp annotation.
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4. Pre-training dataset curation (매 SAM bootstrap).
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## 💻 패턴
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### Upload dataset
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```python
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from segments import SegmentsClient
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client = SegmentsClient(api_key="YOUR_KEY")
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dataset = client.add_dataset(
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name="my-org/road-scenes",
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task_type="segmentation-bitmap",
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description="Highway driving scenes",
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)
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for img_path in image_paths:
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asset = client.upload_asset(open(img_path, "rb"), filename=img_path.name)
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client.add_sample(
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dataset_identifier="my-org/road-scenes",
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name=img_path.name,
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attributes={"image": {"url": asset.url}},
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)
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```
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### Pre-label with SAM 2
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```python
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from segments.utils import bitmap2file
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import numpy as np
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from sam2.build_sam import build_sam2
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from sam2.sam2_image_predictor import SAM2ImagePredictor
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sam = build_sam2("configs/sam2.1_hiera_l.yaml", "sam2_hiera_large.pt")
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predictor = SAM2ImagePredictor(sam)
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predictor.set_image(image)
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masks, _, _ = predictor.predict(point_coords=[[x, y]], point_labels=[1])
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mask = masks[0].astype(np.uint8)
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bitmap_file = bitmap2file(mask, is_segmentation_bitmap=True)
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asset = client.upload_asset(bitmap_file, filename="mask.png")
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client.add_label(
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sample_uuid=sample.uuid,
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labelset="ground-truth",
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attributes={"format_version": "0.1", "annotations": [...], "segmentation_bitmap": {"url": asset.url}},
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)
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```
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### Active learning loop
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```python
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def active_learning_round(model, unlabeled_samples, k=100):
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scores = []
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for s in unlabeled_samples:
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img = load_image(s.attributes["image"]["url"])
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logits = model.predict(img)
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entropy = -(logits.softmax(-1) * logits.log_softmax(-1)).sum()
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scores.append((s, entropy.item()))
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top = sorted(scores, key=lambda x: -x[1])[:k]
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for s, _ in top:
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client.update_sample(s.uuid, priority=10) # 매 high priority
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```
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### Export to HuggingFace
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```python
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from segments.huggingface import release2dataset
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release = client.add_release("my-org/road-scenes", name="v1.0")
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hf_dataset = release2dataset(release)
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hf_dataset.push_to_hub("my-username/road-scenes-v1")
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```
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### 3D point cloud cuboid
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```python
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client.add_sample(
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dataset_identifier="my-org/lidar",
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name="frame_001",
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attributes={
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"pcd": {"url": "s3://.../frame_001.pcd", "type": "pcd"},
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"ego_pose": {"position": {"x": 0, "y": 0, "z": 0}, "heading": {...}},
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"default_z": -1.5,
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},
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)
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```
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### Webhook-driven CI
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```python
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# Flask endpoint receiving Segments.ai webhook
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@app.post("/segments-webhook")
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def on_label_finalized(req):
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event = req.json
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if event["action"] == "labelset.released":
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trigger_training_pipeline(release_uuid=event["release"]["uuid"])
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return {"ok": True}
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Multi-modal AV (lidar+cam) | 매 Segments.ai 또는 Scale AI |
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| 2D bbox only | 매 Roboflow (cheaper) |
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| Self-host required | 매 CVAT |
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| Enterprise ops | 매 Labelbox |
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| Quick prototype | 매 Roboflow / LabelStudio |
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**기본값**: 매 lidar+camera 면 Segments.ai, 매 2D-only 면 Roboflow.
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## 🔗 Graph
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- 부모: [[MLOps]]
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- 응용: [[Robotics]]
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- Adjacent: [[Active Learning]]
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## 🤖 LLM 활용
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**언제**: 매 production CV labeling pipeline, 매 multi-modal sensor fusion dataset.
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**언제 X**: 매 LLM text labeling (Argilla 사용), 매 small one-off (LabelStudio OSS).
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## ❌ 안티패턴
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- **No version control**: 매 release snapshot 무시 → 매 reproducibility 불가.
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- **Manual-only labeling**: 매 SAM pre-label 무시 → 10× slower.
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- **Skip QA**: 매 reviewer-disagreement metric 무시 → noisy labels.
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## 🧪 검증 / 중복
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- Verified (segments.ai docs, Python SDK v1.x).
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- 신뢰도 B+ (commercial product, 매 docs 매 reliable but 매 non-academic).
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — SAM 2, active learning, lidar workflow |
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