---
id: wiki-2026-0508-image-segmentation
title: Image Segmentation
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [segmentation, semantic segmentation, instance segmentation, panoptic, SAM, Mask R-CNN]
duplicate_of: none
source_trust_level: A
confidence_score: 0.96
verification_status: applied
tags: [computer-vision, segmentation, semantic, instance, panoptic, sam, deeplab]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: Python
  framework: PyTorch / Detectron2 / SAM
---

# Image Segmentation

## 매 한 줄
> **"매 image 의 의 의 pixel-level 의 의 의 region 의 의 의 classify"**. 매 semantic (class only) / instance (per-object) / panoptic (both). 매 modern: SAM (Meta 2023), SAM 2 (video), Mask R-CNN, DeepLab v3+.

## 매 핵심

### 매 type
- **Semantic**: 매 매 pixel 의 class.
- **Instance**: 매 매 object 의 separate.
- **Panoptic**: 매 semantic + instance.

### 매 famous
- **U-Net** (Ronneberger 2015): 매 medical.
- **Mask R-CNN** (He 2017): 매 instance.
- **DeepLab v3+** (Chen 2018): 매 atrous conv.
- **SAM** (Meta 2023): 매 promptable, foundation.
- **SAM 2** (Meta 2024): 매 video.
- **Segment Anything in Medical**.

### 매 응용
1. **Medical** (tumor, organ).
2. **Autonomous driving**.
3. **Photo editing** (background remove).
4. **Industrial inspection**.
5. **Satellite / agriculture**.

## 💻 패턴

### SAM (segment anything)
```python
from segment_anything import SamPredictor, sam_model_registry
sam = sam_model_registry['vit_h'](checkpoint='sam_vit_h.pth').cuda()
predictor = SamPredictor(sam)
predictor.set_image(image)

# 매 prompt: point
masks, scores, logits = predictor.predict(
    point_coords=np.array([[500, 375]]),
    point_labels=np.array([1]),  # 매 1 = foreground
    multimask_output=True,
)
```

### SAM 2 (video)
```python
from sam2.sam2_video_predictor import SAM2VideoPredictor
predictor = SAM2VideoPredictor.from_pretrained('facebook/sam2-hiera-large').cuda()

with torch.inference_mode():
    state = predictor.init_state(video_path='video.mp4')
    predictor.add_new_points(state, frame_idx=0, obj_id=1, points=[[500, 375]], labels=[1])
    for frame_idx, masks in predictor.propagate_in_video(state):
        save(frame_idx, masks)
```

### U-Net
```python
import torch.nn as nn

class UNet(nn.Module):
    def __init__(self, in_ch=3, out_ch=1):
        super().__init__()
        self.enc1 = self._block(in_ch, 64)
        self.enc2 = self._block(64, 128)
        self.enc3 = self._block(128, 256)
        self.enc4 = self._block(256, 512)
        self.bottle = self._block(512, 1024)
        self.dec4 = self._block(1024 + 512, 512)
        self.dec3 = self._block(512 + 256, 256)
        self.dec2 = self._block(256 + 128, 128)
        self.dec1 = self._block(128 + 64, 64)
        self.head = nn.Conv2d(64, out_ch, 1)
    
    def _block(self, ic, oc):
        return nn.Sequential(nn.Conv2d(ic, oc, 3, padding=1), nn.ReLU(),
                              nn.Conv2d(oc, oc, 3, padding=1), nn.ReLU())
    
    def forward(self, x):
        e1 = self.enc1(x)
        e2 = self.enc2(F.max_pool2d(e1, 2))
        e3 = self.enc3(F.max_pool2d(e2, 2))
        e4 = self.enc4(F.max_pool2d(e3, 2))
        b = self.bottle(F.max_pool2d(e4, 2))
        d4 = self.dec4(torch.cat([F.interpolate(b, scale_factor=2), e4], 1))
        d3 = self.dec3(torch.cat([F.interpolate(d4, scale_factor=2), e3], 1))
        d2 = self.dec2(torch.cat([F.interpolate(d3, scale_factor=2), e2], 1))
        d1 = self.dec1(torch.cat([F.interpolate(d2, scale_factor=2), e1], 1))
        return self.head(d1)
```

### Mask R-CNN (Detectron2)
```python
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
from detectron2 import model_zoo

cfg = get_cfg()
cfg.merge_from_file(model_zoo.get_config_file('COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml'))
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url('COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml')
predictor = DefaultPredictor(cfg)
outputs = predictor(image)  # 매 boxes, masks, classes
```

### Loss (Dice + BCE)
```python
def dice_bce_loss(pred, target):
    bce = F.binary_cross_entropy_with_logits(pred, target)
    p = torch.sigmoid(pred)
    intersection = (p * target).sum()
    dice = 1 - (2 * intersection + 1) / (p.sum() + target.sum() + 1)
    return bce + dice
```

### IoU eval
```python
def iou(pred_mask, gt_mask):
    inter = (pred_mask & gt_mask).sum()
    union = (pred_mask | gt_mask).sum()
    return inter / union if union > 0 else 0
```

### Panoptic segmentation (Detectron2)
```python
cfg = get_cfg()
cfg.merge_from_file(model_zoo.get_config_file('COCO-PanopticSegmentation/panoptic_fpn_R_50_3x.yaml'))
predictor = DefaultPredictor(cfg)
panoptic, segments_info = predictor(image)['panoptic_seg']
```

### Boundary refinement (CRF)
```python
import pydensecrf.densecrf as dcrf
def crf_refine(prob_map, image):
    d = dcrf.DenseCRF2D(image.shape[1], image.shape[0], n_classes)
    U = -np.log(prob_map.transpose(2, 0, 1).reshape(n_classes, -1) + 1e-9)
    d.setUnaryEnergy(U.astype(np.float32))
    d.addPairwiseGaussian(sxy=3, compat=3)
    d.addPairwiseBilateral(sxy=80, srgb=13, rgbim=image, compat=10)
    return np.argmax(d.inference(5), axis=0).reshape(image.shape[:2])
```

### Augmentation (albumentations)
```python
import albumentations as A
augment = A.Compose([
    A.RandomCrop(512, 512),
    A.HorizontalFlip(),
    A.RandomBrightnessContrast(),
    A.ElasticTransform(),
])
```

### Active learning (uncertainty)
```python
def select_to_label(model, unlabeled_imgs, k=10):
    """매 매 image 의 의 entropy 의 highest."""
    entropies = []
    for img in unlabeled_imgs:
        prob = model(img).softmax(1)
        ent = -(prob * prob.log()).sum(1).mean()
        entropies.append(ent)
    return [unlabeled_imgs[i] for i in np.argsort(entropies)[-k:]]
```

### Foundation model fine-tune (SAM-Med)
```python
from segment_anything import sam_model_registry
sam = sam_model_registry['vit_b']()
# 매 freeze image encoder, train mask decoder on medical data
for p in sam.image_encoder.parameters(): p.requires_grad = False
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| Promptable | SAM / SAM 2 |
| Medical | U-Net + transfer |
| Instance + class | Mask R-CNN |
| Real-time | YOLOv8-seg |
| Panoptic | Mask2Former |
| Video | SAM 2 |
| Few-shot | SAM zero-shot |

**기본값**: 매 modern = SAM 2 (zero-shot) + 매 fine-tune for domain + 매 Dice + BCE loss + 매 IoU eval + 매 active learning.

## 🔗 Graph
- 부모: [[Computer Vision|Computer-Vision]]
- 변형: [[Semantic-Segmentation]] · [[Instance-Segmentation]]
- 응용: [[SAM]] · [[Mask-R-CNN]]
- Adjacent: [[Object-Detection]] · [[Foundation-Models]]

## 🤖 LLM 활용
**언제**: Vision task. Medical. AV. Photo editing.
**언제 X**: Non-vision.

## ❌ 안티패턴
- **Train from scratch**: 매 SAM transfer 의 better.
- **Pixel accuracy alone**: 매 IoU/F1 의 use.
- **Single class without ignore**: 매 imbalance.
- **No CRF for boundary**: 매 jagged.

## 🧪 검증 / 중복
- Verified (Ronneberger U-Net, He Mask R-CNN, Kirillov SAM 2023, SAM 2 2024).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — segmentation + 매 SAM / U-Net / Mask R-CNN / loss code |
