segment-anything-model

What this skill does

# Segment Anything Model (SAM)

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
# From GitHub
pip install git+https://github.com/facebookresearch/segment-anything.git

# Optional dependencies
pip install opencv-python pycocotools matplotlib

# Or use HuggingFace transformers
pip install transformers
```

### Download checkpoints

```bash
# ViT-H (largest, most accurate) - 2.4GB
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth

# ViT-L (medium) - 1.2GB
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth

# ViT-B (smallest, fastest) - 375MB
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

# Load model
sam = sam_model_registry["vit_h"](checkpoint="sam_vit_h_4b8939.pth")
sam.to(device="cuda")

# Create predictor
predictor = SamPredictor(sam)

# Set image (computes embeddings once)
image = cv2.imread("image.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
predictor.set_image(image)

# Predict with point prompts
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
)

# Select best mask
best_mask = masks[np.argmax(scores)]
```

### HuggingFace Transformers

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

# Load model and processor
model = SamModel.from_pretrained("facebook/sam-vit-huge")
processor = SamProcessor.from_pretrained("facebook/sam-vit-huge")
model.to("cuda")

# Process image with point prompt
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()}

# Generate masks
with torch.no_grad():
    outputs = model(**inputs)

# Post-process masks to original size
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
# Single foreground point
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
)

# Multiple points (foreground + background)
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
# Bounding box [x1, y1, x2, y2]
input_box = np.array([425, 600, 700, 875])

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

### Combined prompts

```python
# Box + points for precise control
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
# Initial prediction
masks, scores, logits = predictor.predict(
    point_coords=np.array([[500, 375]]),
    point_labels=np.array([1]),
    multimask_output=True
)

# Refine with additional point using previous mask
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

# Create generator
mask_generator = SamAutomaticMaskGenerator(sam)

# Generate all masks
masks = mask_generator.generate(image)

# Each mask contains:
# - segmentation: binary mask
# - bbox: [x, y, w, h]
# - area: pixel count
# - predicted_iou: quality score
# - stability_score: robustness score
# - point_coords: generating point
```

### 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
# Sort by area (largest first)
masks = sorted(masks, key=lambda x: x['area'], reverse=True)

# Filter by predicted IoU
high_quality = [m for m in masks if m['predicted_iou'] > 0.9]

# Filter by stability score
stable_masks = [m for m in masks if m['stability_score'] > 0.95]
```

## Batched inference

### Multiple images

```python
# Process multiple images efficiently
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
# Process multiple prompts efficiently (one image encoding)
predictor.set_image(image)

# Batch of point prompts
points = [
    np.a