---
name: segment-anything-model
description: "สกิลสำหรับ Multimodal Segment Anything ใช้เมื่อต้องการแนวทาง ขั้นตอน Template หรือคำแนะนำเชิงปฏิบัติสำหรับงาน AI, Agent, Machine Learning, LLM, RAG, Prompt, Automation และงานวิจัย AI ในหัวข้อนี้"
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Multimodal, Image Segmentation, Computer Vision, SAM, Zero-Shot]
dependencies: [segment-anything, transformers>=4.30.0, torch>=1.7.0]
---

# สกิล: Multimodal Segment Anything

Comprehensive guide to using Meta AI's Segment Anything Model for zero-shot image segmentation.

## When to use SAM

**Use SAM when:**
- Need to segment any object in images without task-specific training
- Building interactive annotation tools with point/box prompts
- Generating training data for other vision models
- Need zero-shot transfer to new image domains
- Building object detection/segmentation pipelines
- Processing medical, satellite, or domain-specific images

**Key features:**
- **Zero-shot segmentation**: Works on any image domain without fine-tuning
- **Flexible prompts**: Points, bounding boxes, or previous masks
- **Automatic segmentation**: Generate all object masks automatically
- **High quality**: Trained on 1.1 billion masks from 11 million images
- **Multiple model sizes**: ViT-B (fastest), ViT-L, ViT-H (most accurate)
- **ONNX export**: Deploy in browsers and edge devices

**Use alternatives instead:**
- **YOLO/Detectron2**: For real-time object detection with classes
- **Mask2Former**: For semantic/panoptic segmentation with categories
- **GroundingDINO + SAM**: For text-prompted segmentation
- **SAM 2**: For video segmentation tasks

## Quick start

### Installation

```bash
# สกิล: Multimodal Segment Anything
pip install git+https://github.com/facebookresearch/segment-anything.git

# สกิล: Multimodal Segment Anything
pip install opencv-python pycocotools matplotlib

# สกิล: Multimodal Segment Anything
pip install transformers
```

### Download checkpoints

```bash
# สกิล: Multimodal Segment Anything
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth

# สกิล: Multimodal Segment Anything
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth

# สกิล: Multimodal Segment Anything
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth
```

### Basic usage with SamPredictor

```python
import numpy as np
from segment_anything import sam_model_registry, SamPredictor

# สกิล: Multimodal Segment Anything
sam = sam_model_registry["vit_h"](checkpoint="sam_vit_h_4b8939.pth")
sam.to(device="cuda")

# สกิล: Multimodal Segment Anything
predictor = SamPredictor(sam)

# สกิล: Multimodal Segment Anything
image = cv2.imread("image.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
predictor.set_image(image)

# สกิล: Multimodal Segment Anything
input_point = np.array([[500, 375]])  # (x, y) coordinates
input_label = np.array([1])  # 1 = foreground, 0 = background

masks, scores, logits = predictor.predict(
    point_coords=input_point,
    point_labels=input_label,
    multimask_output=True  # Returns 3 mask options
)

# สกิล: Multimodal Segment Anything
best_mask = masks[np.argmax(scores)]
```

### HuggingFace Transformers

```python
import torch
from PIL import Image
from transformers import SamModel, SamProcessor

# สกิล: Multimodal Segment Anything
model = SamModel.from_pretrained("facebook/sam-vit-huge")
processor = SamProcessor.from_pretrained("facebook/sam-vit-huge")
model.to("cuda")

# สกิล: Multimodal Segment Anything
image = Image.open("image.jpg")
input_points = [[[450, 600]]]  # Batch of points

inputs = processor(image, input_points=input_points, return_tensors="pt")
inputs = {k: v.to("cuda") for k, v in inputs.items()}

# สกิล: Multimodal Segment Anything
with torch.no_grad():
    outputs = model(**inputs)

# สกิล: Multimodal Segment Anything
masks = processor.image_processor.post_process_masks(
    outputs.pred_masks.cpu(),
    inputs["original_sizes"].cpu(),
    inputs["reshaped_input_sizes"].cpu()
)
```

## Core concepts

### Model architecture

```
SAM Architecture:
┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│  Image Encoder  │────▶│ Prompt Encoder  │────▶│  Mask Decoder   │
│     (ViT)       │     │ (Points/Boxes)  │     │ (Transformer)   │
└─────────────────┘     └─────────────────┘     └─────────────────┘
        │                       │                       │
   Image Embeddings      Prompt Embeddings         Masks + IoU
   (computed once)       (per prompt)             predictions
```

### Model variants

| Model | Checkpoint | Size | Speed | Accuracy |
|-------|------------|------|-------|----------|
| ViT-H | `vit_h` | 2.4 GB | Slowest | Best |
| ViT-L | `vit_l` | 1.2 GB | Medium | Good |
| ViT-B | `vit_b` | 375 MB | Fastest | Good |

### Prompt types

| Prompt | Description | Use Case |
|--------|-------------|----------|
| Point (foreground) | Click on object | Single object selection |
| Point (background) | Click outside object | Exclude regions |
| Bounding box | Rectangle around object | Larger objects |
| Previous mask | Low-res mask input | Iterative refinement |

## Interactive segmentation

### Point prompts

```python
# สกิล: Multimodal Segment Anything
input_point = np.array([[500, 375]])
input_label = np.array([1])

masks, scores, logits = predictor.predict(
    point_coords=input_point,
    point_labels=input_label,
    multimask_output=True
)

# สกิล: Multimodal Segment Anything
input_points = np.array([[500, 375], [600, 400], [450, 300]])
input_labels = np.array([1, 1, 0])  # 2 foreground, 1 background

masks, scores, logits = predictor.predict(
    point_coords=input_points,
    point_labels=input_labels,
    multimask_output=False  # Single mask when prompts are clear
)
```

### Box prompts

```python
# สกิล: Multimodal Segment Anything
input_box = np.array([425, 600, 700, 875])

masks, scores, logits = predictor.predict(
    box=input_box,
    multimask_output=False
)
```

### Combined prompts

```python
# สกิล: Multimodal Segment Anything
masks, scores, logits = predictor.predict(
    point_coords=np.array([[500, 375]]),
    point_labels=np.array([1]),
    box=np.array([400, 300, 700, 600]),
    multimask_output=False
)
```

### Iterative refinement

```python
# สกิล: Multimodal Segment Anything
masks, scores, logits = predictor.predict(
    point_coords=np.array([[500, 375]]),
    point_labels=np.array([1]),
    multimask_output=True
)

# สกิล: Multimodal Segment Anything
masks, scores, logits = predictor.predict(
    point_coords=np.array([[500, 375], [550, 400]]),
    point_labels=np.array([1, 0]),  # Add background point
    mask_input=logits[np.argmax(scores)][None, :, :],  # Use best mask
    multimask_output=False
)
```

## Automatic mask generation

### Basic automatic segmentation

```python
from segment_anything import SamAutomaticMaskGenerator

# สกิล: Multimodal Segment Anything
mask_generator = SamAutomaticMaskGenerator(sam)

# สกิล: Multimodal Segment Anything
masks = mask_generator.generate(image)

# สกิล: Multimodal Segment Anything
# สกิล: Multimodal Segment Anything
# สกิล: Multimodal Segment Anything
# สกิล: Multimodal Segment Anything
# สกิล: Multimodal Segment Anything
# สกิล: Multimodal Segment Anything
# สกิล: Multimodal Segment Anything
```

### Customized generation

```python
mask_generator = SamAutomaticMaskGenerator(
    model=sam,
    points_per_side=32,          # Grid density (more = more masks)
    pred_iou_thresh=0.88,        # Quality threshold
    stability_score_thresh=0.95,  # Stability threshold
    crop_n_layers=1,             # Multi-scale crops
    crop_n_points_downscale_factor=2,
    min_mask_region_area=100,    # Remove tiny masks
)

masks = mask_generator.generate(image)
```

### Filtering masks

```python
# สกิล: Multimodal Segment Anything
masks = sorted(masks, key=lambda x: x['area'], reverse=True)

# สกิล: Multimodal Segment Anything
high_quality = [m for m in masks if m['predicted_iou'] > 0.9]

# สกิล: Multimodal Segment Anything
stable_masks = [m for m in masks if m['stability_score'] > 0.95]
```

## Batched inference

### Multiple images

```python
# สกิล: Multimodal Segment Anything
images = [cv2.imread(f"image_{i}.jpg") for i in range(10)]

all_masks = []
for image in images:
    predictor.set_image(image)
    masks, _, _ = predictor.predict(
        point_coords=np.array([[500, 375]]),
        point_labels=np.array([1]),
        multimask_output=True
    )
    all_masks.append(masks)
```

### Multiple prompts per image

```python
# สกิล: Multimodal Segment Anything
predictor.set_image(image)

# สกิล: Multimodal Segment Anything
points = [
    np.array([[100, 100]]),
    np.array([[200, 200]]),
    np.array([[300, 300]])
]

all_masks = []
for point in points:
    masks, scores, _ = predictor.predict(
        point_coords=point,
        point_labels=np.array([1]),
        multimask_output=True
    )
    all_masks.append(masks[np.argmax(scores)])
```

## ONNX deployment

### Export model

```bash
python scripts/export_onnx_model.py \
    --checkpoint sam_vit_h_4b8939.pth \
    --model-type vit_h \
    --output sam_onnx.onnx \
    --return-single-mask
```

### Use ONNX model

```python
import onnxruntime

# สกิล: Multimodal Segment Anything
ort_session = onnxruntime.InferenceSession("sam_onnx.onnx")

# สกิล: Multimodal Segment Anything
masks = ort_session.run(
    None,
    {
        "image_embeddings": image_embeddings,
        "point_coords": point_coords,
        "point_labels": point_labels,
        "mask_input": np.zeros((1, 1, 256, 256), dtype=np.float32),
        "has_mask_input": np.array([0], dtype=np.float32),
        "orig_im_size": np.array([h, w], dtype=np.float32)
    }
)
```

## Common workflows

### Workflow 1: Annotation tool

```python
import cv2

# สกิล: Multimodal Segment Anything
predictor = SamPredictor(sam)
predictor.set_image(image)

def on_click(event, x, y, flags, param):
    if event == cv2.EVENT_LBUTTONDOWN:
        # Foreground point
        masks, scores, _ = predictor.predict(
            point_coords=np.array([[x, y]]),
            point_labels=np.array([1]),
            multimask_output=True
        )
        # Display best mask
        display_mask(masks[np.argmax(scores)])
```

### Workflow 2: Object extraction

```python
def extract_object(image, point):
    """Extract object at point with transparent background."""
    predictor.set_image(image)

    masks, scores, _ = predictor.predict(
        point_coords=np.array([point]),
        point_labels=np.array([1]),
        multimask_output=True
    )

    best_mask = masks[np.argmax(scores)]

    # Create RGBA output
    rgba = np.zeros((image.shape[0], image.shape[1], 4), dtype=np.uint8)
    rgba[:, :, :3] = image
    rgba[:, :, 3] = best_mask * 255

    return rgba
```

### Workflow 3: Medical image segmentation

```python
# สกิล: Multimodal Segment Anything
medical_image = cv2.imread("scan.png", cv2.IMREAD_GRAYSCALE)
rgb_image = cv2.cvtColor(medical_image, cv2.COLOR_GRAY2RGB)

predictor.set_image(rgb_image)

# สกิล: Multimodal Segment Anything
masks, scores, _ = predictor.predict(
    box=np.array([x1, y1, x2, y2]),  # ROI bounding box
    multimask_output=True
)
```

## Output format

### Mask data structure

```python
# สกิล: Multimodal Segment Anything
{
    "segmentation": np.ndarray,  # H×W binary mask
    "bbox": [x, y, w, h],        # Bounding box
    "area": int,                 # Pixel count
    "predicted_iou": float,      # 0-1 quality score
    "stability_score": float,    # 0-1 robustness score
    "crop_box": [x, y, w, h],    # Generation crop region
    "point_coords": [[x, y]],    # Input point
}
```

### COCO RLE format

```python
from pycocotools import mask as mask_utils

# สกิล: Multimodal Segment Anything
rle = mask_utils.encode(np.asfortranarray(mask.astype(np.uint8)))
rle["counts"] = rle["counts"].decode("utf-8")

# สกิล: Multimodal Segment Anything
decoded_mask = mask_utils.decode(rle)
```

## Performance optimization

### GPU memory

```python
# สกิล: Multimodal Segment Anything
sam = sam_model_registry["vit_b"](checkpoint="sam_vit_b_01ec64.pth")

# สกิล: Multimodal Segment Anything
# สกิล: Multimodal Segment Anything
torch.cuda.empty_cache()
```

### Speed optimization

```python
# สกิล: Multimodal Segment Anything
sam = sam.half()

# สกิล: Multimodal Segment Anything
mask_generator = SamAutomaticMaskGenerator(
    model=sam,
    points_per_side=16,  # Default is 32
)

# สกิล: Multimodal Segment Anything
# สกิล: Multimodal Segment Anything
```

## Common issues

| Issue | Solution |
|-------|----------|
| Out of memory | Use ViT-B model, reduce image size |
| Slow inference | Use ViT-B, reduce points_per_side |
| Poor mask quality | Try different prompts, use box + points |
| Edge artifacts | Use stability_score filtering |
| Small objects missed | Increase points_per_side |

## References

- **[Advanced Usage](references/advanced-usage.md)** - Batching, fine-tuning, integration
- **[Troubleshooting](references/troubleshooting.md)** - Common issues and solutions

## Resources

- **GitHub**: https://github.com/facebookresearch/segment-anything
- **Paper**: https://arxiv.org/abs/2304.02643
- **Demo**: https://segment-anything.com
- **SAM 2 (Video)**: https://github.com/facebookresearch/segment-anything-2
- **HuggingFace**: https://huggingface.co/facebook/sam-vit-huge
