Antigravity Agent
170 lines
6.1 KiB
Markdown
170 lines
6.1 KiB
Markdown
---
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id: wiki-2026-0508-computer-vision
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title: Computer Vision
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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: [CV, Vision AI, Visual Recognition]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.93
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verification_status: applied
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tags: [computer-vision, deep-learning, multimodal, perception]
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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: PyTorch / Transformers / OpenCV
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---
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# Computer Vision
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## 매 한 줄
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> **"매 CV 의 핵심: pixels → semantic understanding via learned hierarchical features"**. 매 1960s Roberts edge detector 로 시작, 매 2012 AlexNet 으로 deep-learning revolution, 매 2020 ViT, 매 2023 SAM. 매 2026 현재 multimodal foundation models (GPT-5V, Claude Opus 4.7, Qwen3-VL, Llama 4 Vision) 가 zero-shot 으로 detection / VQA / OCR 의 통합.
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## 매 핵심
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### 매 task taxonomy
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- **Classification**: 매 image → label.
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- **Detection**: 매 bounding boxes + classes (YOLO, DETR, RT-DETR).
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- **Segmentation**: 매 pixel-level (semantic, instance, panoptic). SAM2 의 promptable.
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- **Pose / Keypoint**: 매 human/object joints (RTMPose, ViTPose).
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- **Depth / 3D**: 매 monocular depth (Depth Anything V2), NeRF, 3DGS.
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- **Generation**: 매 diffusion (FLUX.1, SD3.5), 매 video (Sora 2, Veo 3).
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- **VLM (Vision-Language)**: 매 image+text → text (GPT-5V, Claude Opus 4.7, Qwen3-VL).
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### 매 modern stack (2026)
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- **Backbone**: ConvNeXt-V2, ViT-L, EVA-02, DINOv2.
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- **Detection**: YOLOv11, RT-DETR-v2, Grounding DINO 1.5.
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- **Segmentation**: SAM2, Mask2Former.
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- **Foundation**: SigLIP-2, CLIP, DINOv2 (self-supervised features).
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- **VLM**: Claude Opus 4.7 Vision, GPT-5V, InternVL3, Qwen3-VL.
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### 매 응용
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1. Autonomous driving (Waymo, Tesla FSD perception).
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2. Medical imaging (MONAI, nnU-Net for segmentation).
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3. Document AI / OCR (Donut, Florence-2, GPT-5V).
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4. Robotics (open-vocabulary manipulation, RT-2).
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5. Content moderation, retail, agriculture.
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## 💻 패턴
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### Image classification (timm + finetune)
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```python
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import timm, torch
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model = timm.create_model("convnextv2_tiny.fcmae_ft_in22k_in1k",
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pretrained=True, num_classes=10)
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opt = torch.optim.AdamW(model.parameters(), lr=1e-4)
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# train as usual
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```
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### Object detection (Ultralytics YOLOv11)
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```python
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from ultralytics import YOLO
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model = YOLO("yolo11n.pt")
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results = model("img.jpg")
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for r in results:
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print(r.boxes.xyxy, r.boxes.cls, r.boxes.conf)
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```
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### Promptable segmentation (SAM2)
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```python
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from sam2.sam2_image_predictor import SAM2ImagePredictor
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predictor = SAM2ImagePredictor.from_pretrained("facebook/sam2-hiera-large")
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predictor.set_image(image)
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masks, scores, _ = predictor.predict(
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point_coords=[[500, 375]], point_labels=[1])
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```
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### CLIP zero-shot classification
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```python
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import torch, open_clip
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model, _, preprocess = open_clip.create_model_and_transforms(
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"ViT-L-14-SigLIP2", pretrained="webli")
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tokenizer = open_clip.get_tokenizer("ViT-L-14-SigLIP2")
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text = tokenizer(["a cat", "a dog", "a car"])
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with torch.no_grad():
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img_feat = model.encode_image(preprocess(img).unsqueeze(0))
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txt_feat = model.encode_text(text)
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probs = (img_feat @ txt_feat.T).softmax(-1)
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```
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### VLM via Claude (vision)
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```python
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import anthropic, base64
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client = anthropic.Anthropic()
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img_b64 = base64.b64encode(open("img.jpg", "rb").read()).decode()
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resp = client.messages.create(
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model="claude-opus-4-7-20260101",
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max_tokens=1024,
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messages=[{
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"role": "user",
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"content": [
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{"type": "image", "source": {"type": "base64",
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"media_type": "image/jpeg", "data": img_b64}},
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{"type": "text", "text": "What objects? Bounding boxes (x1,y1,x2,y2)."}
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]
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}])
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```
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### Monocular depth (Depth Anything V2)
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```python
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from transformers import pipeline
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pipe = pipeline("depth-estimation",
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model="depth-anything/Depth-Anything-V2-Large-hf")
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depth = pipe(image)["depth"]
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```
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### OpenCV preprocessing pipeline
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```python
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import cv2, numpy as np
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img = cv2.imread("scan.jpg")
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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blur = cv2.GaussianBlur(gray, (5, 5), 0)
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edges = cv2.Canny(blur, 50, 150)
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contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL,
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cv2.CHAIN_APPROX_SIMPLE)
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```
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## 매 결정 기준
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| 상황 | Approach |
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| Quick prototype, no labels | VLM (GPT-5V/Claude) zero-shot |
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| Production classification | timm finetune (ConvNeXt-V2) |
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| Real-time detection | YOLOv11 / RT-DETR |
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| Open-vocab detection | Grounding DINO 1.5 |
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| Pixel-perfect masks | SAM2 (promptable) |
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| Video understanding | InternVideo2 / Sora-style |
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| Edge / mobile | MobileNetV4 + ONNX/CoreML |
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**기본값**: 매 prototype 은 VLM 로 baseline, 매 production scale 시 specialized model 의 finetune.
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## 🔗 Graph
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- 부모: [[Deep Learning]] · [[Multimodal AI]]
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- 변형: [[CNN]] · [[Vision Transformer]] · [[Diffusion Models]]
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- 응용: [[Object Detection]] · [[Image Segmentation]] · [[OCR]]
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- Adjacent: [[NLP]] · [[Robotics]] · [[Generative AI]]
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## 🤖 LLM 활용
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**언제**: 매 quick image-understanding tasks (VQA, OCR, caption), 매 dataset bootstrapping (label generation), 매 vision-pipeline scaffolding.
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**언제 X**: 매 high-throughput / low-latency production — 매 specialized model 의 use. 매 medical / safety-critical 은 validated model only.
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## ❌ 안티패턴
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- **Re-inventing vs. timm/ultralytics**: 매 well-tested baselines 의 무시 X.
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- **No domain-specific augmentation**: 매 medical/satellite 의 ImageNet aug 의 그대로 사용.
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- **Ignoring image preprocessing**: 매 wrong normalization 의 가장 흔한 bug.
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- **VLM 의 fine-grained 작업 의 무비판 신뢰**: 매 small-object detection / counting 의 hallucination.
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- **No test-time augmentation for production**: 매 robustness 손실.
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## 🧪 검증 / 중복
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- Verified (Szeliski Computer Vision 2nd ed., Ultralytics YOLOv11 2025, SAM2 paper 2024, Depth Anything V2 2024).
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- 신뢰도 A.
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- 관련: [[CNN]], [[Vision Transformer]].
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — 2026 modern stack (YOLOv11, SAM2, VLMs) |
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