Foundation model for image segmentation with zero-shot transfer. Use when you need to segment any object in images using points, boxes, or masks as prompts, or automatically generate all object masks in an image.
apm install @orchestra-research/segment-anything-model---
name: segment-anything-model
description: Foundation model for image segmentation with zero-shot transfer. Use when you need to segment any object in images using points, boxes, or masks as prompts, or automatically generate all object masks in an image.
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]
---
# 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.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
# Load ONNX model
ort_session = onnxruntime.InferenceSession("sam_onnx.onnx")
# Run inference (image embeddings computed separately)
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
# Load model
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
# Process medical images (grayscale to RGB)
medical_image = cv2.imread("scan.png", cv2.IMREAD_GRAYSCALE)
rgb_image = cv2.cvtColor(medical_image, cv2.COLOR_GRAY2RGB)
predictor.set_image(rgb_image)
# Segment region of interest
masks, scores, _ = predictor.predict(
box=np.array([x1, y1, x2, y2]), # ROI bounding box
multimask_output=True
)
```
## Output format
### Mask data structure
```python
# SamAutomaticMaskGenerator output
{
"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
# Encode mask to RLE
rle = mask_utils.encode(np.asfortranarray(mask.astype(np.uint8)))
rle["counts"] = rle["counts"].decode("utf-8")
# Decode RLE to mask
decoded_mask = mask_utils.decode(rle)
```
## Performance optimization
### GPU memory
```python
# Use smaller model for limited VRAM
sam = sam_model_registry["vit_b"](checkpoint="sam_vit_b_01ec64.pth")
# Process images in batches
# Clear CUDA cache between large batches
torch.cuda.empty_cache()
```
### Speed optimization
```python
# Use half precision
sam = sam.half()
# Reduce points for automatic generation
mask_generator = SamAutomaticMaskGenerator(
model=sam,
points_per_side=16, # Default is 32
)
# Use ONNX for deployment
# Export with --return-single-mask for faster inference
```
## 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