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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.","origin-labs-ai\u002Fcode","origin-labs-ai","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F347654492?v=4","01M7B0MAD1JMVZ6ME9891AYYYG","Origin","01M7B0MADP3AZS42R4NEF1JTS8",0,"MIT","https:\u002F\u002Fgithub.com\u002Forigin-labs-ai\u002Fcode\u002Fblob\u002Fmain\u002Fpayloads\u002Fskills\u002Fmultimodal-segment-anything\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: multimodal-segment-anything\ndescription: 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.\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  author: Orchestra Research\n  tags: [Multimodal, Image Segmentation, Computer Vision, SAM, Zero-Shot]\n  dependencies: [segment-anything, transformers>=4.30.0, torch>=1.7.0]\n---\n\n# Segment Anything Model (SAM)\n\nComprehensive guide to using Meta AI's Segment Anything Model for zero-shot image segmentation.\n\n## When to use SAM\n\n**Use SAM when:**\n- Need to segment any object in images without task-specific training\n- Building interactive annotation tools with point\u002Fbox prompts\n- Generating training data for other vision models\n- Need zero-shot transfer to new image domains\n- Building object detection\u002Fsegmentation pipelines\n- Processing medical, satellite, or domain-specific images\n\n**Key features:**\n- **Zero-shot segmentation**: Works on any image domain without fine-tuning\n- **Flexible prompts**: Points, bounding boxes, or previous masks\n- **Automatic segmentation**: Generate all object masks automatically\n- **High quality**: Trained on 1.1 billion masks from 11 million images\n- **Multiple model sizes**: ViT-B (fastest), ViT-L, ViT-H (most accurate)\n- **ONNX export**: Deploy in browsers and edge devices\n\n**Use alternatives instead:**\n- **YOLO\u002FDetectron2**: For real-time object detection with classes\n- **Mask2Former**: For semantic\u002Fpanoptic segmentation with categories\n- **GroundingDINO + SAM**: For text-prompted segmentation\n- **SAM 2**: For video segmentation tasks\n\n## Quick start\n\n### Installation\n\n```bash\n# From GitHub\npip install git+https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fsegment-anything.git\n\n# Optional dependencies\npip install opencv-python pycocotools matplotlib\n\n# Or use HuggingFace transformers\npip install transformers\n```\n\n### Download checkpoints\n\n```bash\n# ViT-H (largest, most accurate) - 2.4GB\nwget https:\u002F\u002Fdl.fbaipublicfiles.com\u002Fsegment_anything\u002Fsam_vit_h_4b8939.pth\n\n# ViT-L (medium) - 1.2GB\nwget https:\u002F\u002Fdl.fbaipublicfiles.com\u002Fsegment_anything\u002Fsam_vit_l_0b3195.pth\n\n# ViT-B (smallest, fastest) - 375MB\nwget https:\u002F\u002Fdl.fbaipublicfiles.com\u002Fsegment_anything\u002Fsam_vit_b_01ec64.pth\n```\n\n### Basic usage with SamPredictor\n\n```python\nimport numpy as np\nfrom segment_anything import sam_model_registry, SamPredictor\n\n# Load model\nsam = sam_model_registry[\"vit_h\"](checkpoint=\"sam_vit_h_4b8939.pth\")\nsam.to(device=\"cuda\")\n\n# Create predictor\npredictor = SamPredictor(sam)\n\n# Set image (computes embeddings once)\nimage = cv2.imread(\"image.jpg\")\nimage = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\npredictor.set_image(image)\n\n# Predict with point prompts\ninput_point = np.array([[500, 375]])  # (x, y) coordinates\ninput_label = np.array([1])  # 1 = foreground, 0 = background\n\nmasks, scores, logits = predictor.predict(\n    point_coords=input_point,\n    point_labels=input_label,\n    multimask_output=True  # Returns 3 mask options\n)\n\n# Select best mask\nbest_mask = masks[np.argmax(scores)]\n```\n\n### HuggingFace Transformers\n\n```python\nimport torch\nfrom PIL import Image\nfrom transformers import SamModel, SamProcessor\n\n# Load model and processor\nmodel = SamModel.from_pretrained(\"facebook\u002Fsam-vit-huge\")\nprocessor = SamProcessor.from_pretrained(\"facebook\u002Fsam-vit-huge\")\nmodel.to(\"cuda\")\n\n# Process image with point prompt\nimage = Image.open(\"image.jpg\")\ninput_points = [[[450, 600]]]  # Batch of points\n\ninputs = processor(image, input_points=input_points, return_tensors=\"pt\")\ninputs = {k: v.to(\"cuda\") for k, v in inputs.items()}\n\n# Generate masks\nwith torch.no_grad():\n    outputs = model(**inputs)\n\n# Post-process masks to original size\nmasks = processor.image_processor.post_process_masks(\n    outputs.pred_masks.cpu(),\n    inputs[\"original_sizes\"].cpu(),\n    inputs[\"reshaped_input_sizes\"].cpu()\n)\n```\n\n## Core concepts\n\n### Model architecture\n\n```\nSAM Architecture:\n┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐\n│  Image Encoder  │────▶│ Prompt Encoder  │────▶│  Mask Decoder   │\n│     (ViT)       │     │ (Points\u002FBoxes)  │     │ (Transformer)   │\n└─────────────────┘     └─────────────────┘     └─────────────────┘\n        │                       │                       │\n   Image Embeddings      Prompt Embeddings         Masks + IoU\n   (computed once)       (per prompt)             predictions\n```\n\n### Model variants\n\n| Model | Checkpoint | Size | Speed | Accuracy |\n|-------|------------|------|-------|----------|\n| ViT-H | `vit_h` | 2.4 GB | Slowest | Best |\n| ViT-L | `vit_l` | 1.2 GB | Medium | Good |\n| ViT-B | `vit_b` | 375 MB | Fastest | Good |\n\n### Prompt types\n\n| Prompt | Description | Use Case |\n|--------|-------------|----------|\n| Point (foreground) | Click on object | Single object selection |\n| Point (background) | Click outside object | Exclude regions |\n| Bounding box | Rectangle around object | Larger objects |\n| Previous mask | Low-res mask input | Iterative refinement |\n\n## Interactive segmentation\n\n### Point prompts\n\n```python\n# Single foreground point\ninput_point = np.array([[500, 375]])\ninput_label = np.array([1])\n\nmasks, scores, logits = predictor.predict(\n    point_coords=input_point,\n    point_labels=input_label,\n    multimask_output=True\n)\n\n# Multiple points (foreground + background)\ninput_points = np.array([[500, 375], [600, 400], [450, 300]])\ninput_labels = np.array([1, 1, 0])  # 2 foreground, 1 background\n\nmasks, scores, logits = predictor.predict(\n    point_coords=input_points,\n    point_labels=input_labels,\n    multimask_output=False  # Single mask when prompts are clear\n)\n```\n\n### Box prompts\n\n```python\n# Bounding box [x1, y1, x2, y2]\ninput_box = np.array([425, 600, 700, 875])\n\nmasks, scores, logits = predictor.predict(\n    box=input_box,\n    multimask_output=False\n)\n```\n\n### Combined prompts\n\n```python\n# Box + points for precise control\nmasks, scores, logits = predictor.predict(\n    point_coords=np.array([[500, 375]]),\n    point_labels=np.array([1]),\n    box=np.array([400, 300, 700, 600]),\n    multimask_output=False\n)\n```\n\n### Iterative refinement\n\n```python\n# Initial prediction\nmasks, scores, logits = predictor.predict(\n    point_coords=np.array([[500, 375]]),\n    point_labels=np.array([1]),\n    multimask_output=True\n)\n\n# Refine with additional point using previous mask\nmasks, scores, logits = predictor.predict(\n    point_coords=np.array([[500, 375], [550, 400]]),\n    point_labels=np.array([1, 0]),  # Add background point\n    mask_input=logits[np.argmax(scores)][None, :, :],  # Use best mask\n    multimask_output=False\n)\n```\n\n## Automatic mask generation\n\n### Basic automatic segmentation\n\n```python\nfrom segment_anything import SamAutomaticMaskGenerator\n\n# Create generator\nmask_generator = SamAutomaticMaskGenerator(sam)\n\n# Generate all masks\nmasks = mask_generator.generate(image)\n\n# Each mask contains:\n# - segmentation: binary mask\n# - bbox: [x, y, w, h]\n# - area: pixel count\n# - predicted_iou: quality score\n# - stability_score: robustness score\n# - point_coords: generating point\n```\n\n### Customized generation\n\n```python\nmask_generator = SamAutomaticMaskGenerator(\n    model=sam,\n    points_per_side=32,          # Grid density (more = more masks)\n    pred_iou_thresh=0.88,        # Quality threshold\n    stability_score_thresh=0.95,  # Stability threshold\n    crop_n_layers=1,             # Multi-scale crops\n    crop_n_points_downscale_factor=2,\n    min_mask_region_area=100,    # Remove tiny masks\n)\n\nmasks = mask_generator.generate(image)\n```\n\n### Filtering masks\n\n```python\n# Sort by area (largest first)\nmasks = sorted(masks, key=lambda x: x['area'], reverse=True)\n\n# Filter by predicted IoU\nhigh_quality = [m for m in masks if m['predicted_iou'] > 0.9]\n\n# Filter by stability score\nstable_masks = [m for m in masks if m['stability_score'] > 0.95]\n```\n\n## Batched inference\n\n### Multiple images\n\n```python\n# Process multiple images efficiently\nimages = [cv2.imread(f\"image_{i}.jpg\") for i in range(10)]\n\nall_masks = []\nfor image in images:\n    predictor.set_image(image)\n    masks, _, _ = predictor.predict(\n        point_coords=np.array([[500, 375]]),\n        point_labels=np.array([1]),\n        multimask_output=True\n    )\n    all_masks.append(masks)\n```\n\n### Multiple prompts per image\n\n```python\n# Process multiple prompts efficiently (one image encoding)\npredictor.set_image(image)\n\n# Batch of point prompts\npoints = [\n    np.array([[100, 100]]),\n    np.array([[200, 200]]),\n    np.array([[300, 300]])\n]\n\nall_masks = []\nfor point in points:\n    masks, scores, _ = predictor.predict(\n        point_coords=point,\n        point_labels=np.array([1]),\n        multimask_output=True\n    )\n    all_masks.append(masks[np.argmax(scores)])\n```\n\n## ONNX deployment\n\n### Export model\n\n```bash\npython scripts\u002Fexport_onnx_model.py \\\n    --checkpoint sam_vit_h_4b8939.pth \\\n    --model-type vit_h \\\n    --output sam_onnx.onnx \\\n    --return-single-mask\n```\n\n### Use ONNX model\n\n```python\nimport onnxruntime\n\n# Load ONNX model\nort_session = onnxruntime.InferenceSession(\"sam_onnx.onnx\")\n\n# Run inference (image embeddings computed separately)\nmasks = ort_session.run(\n    None,\n    {\n        \"image_embeddings\": image_embeddings,\n        \"point_coords\": point_coords,\n        \"point_labels\": point_labels,\n        \"mask_input\": np.zeros((1, 1, 256, 256), dtype=np.float32),\n        \"has_mask_input\": np.array([0], dtype=np.float32),\n        \"orig_im_size\": np.array([h, w], dtype=np.float32)\n    }\n)\n```\n\n## Common workflows\n\n### Workflow 1: Annotation tool\n\n```python\nimport cv2\n\n# Load model\npredictor = SamPredictor(sam)\npredictor.set_image(image)\n\ndef on_click(event, x, y, flags, param):\n    if event == cv2.EVENT_LBUTTONDOWN:\n        # Foreground point\n        masks, scores, _ = predictor.predict(\n            point_coords=np.array([[x, y]]),\n            point_labels=np.array([1]),\n            multimask_output=True\n        )\n        # Display best mask\n        display_mask(masks[np.argmax(scores)])\n```\n\n### Workflow 2: Object extraction\n\n```python\ndef extract_object(image, point):\n    \"\"\"Extract object at point with transparent background.\"\"\"\n    predictor.set_image(image)\n\n    masks, scores, _ = predictor.predict(\n        point_coords=np.array([point]),\n        point_labels=np.array([1]),\n        multimask_output=True\n    )\n\n    best_mask = masks[np.argmax(scores)]\n\n    # Create RGBA output\n    rgba = np.zeros((image.shape[0], image.shape[1], 4), dtype=np.uint8)\n    rgba[:, :, :3] = image\n    rgba[:, :, 3] = best_mask * 255\n\n    return rgba\n```\n\n### Workflow 3: Medical image segmentation\n\n```python\n# Process medical images (grayscale to RGB)\nmedical_image = cv2.imread(\"scan.png\", cv2.IMREAD_GRAYSCALE)\nrgb_image = cv2.cvtColor(medical_image, cv2.COLOR_GRAY2RGB)\n\npredictor.set_image(rgb_image)\n\n# Segment region of interest\nmasks, scores, _ = predictor.predict(\n    box=np.array([x1, y1, x2, y2]),  # ROI bounding box\n    multimask_output=True\n)\n```\n\n## Output format\n\n### Mask data structure\n\n```python\n# SamAutomaticMaskGenerator output\n{\n    \"segmentation\": np.ndarray,  # H×W binary mask\n    \"bbox\": [x, y, w, h],        # Bounding box\n    \"area\": int,                 # Pixel count\n    \"predicted_iou\": float,      # 0-1 quality score\n    \"stability_score\": float,    # 0-1 robustness score\n    \"crop_box\": [x, y, w, h],    # Generation crop region\n    \"point_coords\": [[x, y]],    # Input point\n}\n```\n\n### COCO RLE format\n\n```python\nfrom pycocotools import mask as mask_utils\n\n# Encode mask to RLE\nrle = mask_utils.encode(np.asfortranarray(mask.astype(np.uint8)))\nrle[\"counts\"] = rle[\"counts\"].decode(\"utf-8\")\n\n# Decode RLE to mask\ndecoded_mask = mask_utils.decode(rle)\n```\n\n## Performance optimization\n\n### GPU memory\n\n```python\n# Use smaller model for limited VRAM\nsam = sam_model_registry[\"vit_b\"](checkpoint=\"sam_vit_b_01ec64.pth\")\n\n# Process images in batches\n# Clear CUDA cache between large batches\ntorch.cuda.empty_cache()\n```\n\n### Speed optimization\n\n```python\n# Use half precision\nsam = sam.half()\n\n# Reduce points for automatic generation\nmask_generator = SamAutomaticMaskGenerator(\n    model=sam,\n    points_per_side=16,  # Default is 32\n)\n\n# Use ONNX for deployment\n# Export with --return-single-mask for faster inference\n```\n\n## Common issues\n\n| Issue | Solution |\n|-------|----------|\n| Out of memory | Use ViT-B model, reduce image size |\n| Slow inference | Use ViT-B, reduce points_per_side |\n| Poor mask quality | Try different prompts, use box + points |\n| Edge artifacts | Use stability_score filtering |\n| Small objects missed | Increase points_per_side |\n\n## References\n\n- **[Advanced Usage](references\u002Fadvanced-usage.md)** - Batching, fine-tuning, integration\n- **[Troubleshooting](references\u002Ftroubleshooting.md)** - Common issues and solutions\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fsegment-anything\n- **Paper**: https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.02643\n- **Demo**: https:\u002F\u002Fsegment-anything.com\n- **SAM 2 (Video)**: https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fsegment-anything-2\n- **HuggingFace**: https:\u002F\u002Fhuggingface.co\u002Ffacebook\u002Fsam-vit-huge\n","# Segment Anything Model (SAM)\n\nComprehensive guide to using Meta AI's Segment Anything Model for zero-shot image segmentation.\n\n## When to use SAM\n\n**Use SAM when:**\n- Need to segment any object in images without task-specific training\n- Building interactive annotation tools with point\u002Fbox prompts\n- Generating training data for other vision models\n- Need zero-shot transfer to new image domains\n- Building object detection\u002Fsegmentation pipelines\n- Processing medical, satellite, or domain-specific images\n\n**Key features:**\n- **Zero-shot segmentation**: Works on any image domain without fine-tuning\n- **Flexible prompts**: Points, bounding boxes, or previous masks\n- **Automatic segmentation**: Generate all object masks automatically\n- **High quality**: Trained on 1.1 billion masks from 11 million images\n- **Multiple model sizes**: ViT-B (fastest), ViT-L, ViT-H (most accurate)\n- **ONNX export**: Deploy in browsers and edge devices\n\n**Use alternatives instead:**\n- **YOLO\u002FDetectron2**: For real-time object detection with classes\n- **Mask2Former**: For semantic\u002Fpanoptic segmentation with categories\n- **GroundingDINO + SAM**: For text-prompted segmentation\n- **SAM 2**: For video segmentation tasks\n\n## Quick start\n\n### Installation\n\n```bash\n# From GitHub\npip install git+https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fsegment-anything.git\n\n# Optional dependencies\npip install opencv-python pycocotools matplotlib\n\n# Or use HuggingFace transformers\npip install transformers\n```\n\n### Download checkpoints\n\n```bash\n# ViT-H (largest, most accurate) - 2.4GB\nwget https:\u002F\u002Fdl.fbaipublicfiles.com\u002Fsegment_anything\u002Fsam_vit_h_4b8939.pth\n\n# ViT-L (medium) - 1.2GB\nwget https:\u002F\u002Fdl.fbaipublicfiles.com\u002Fsegment_anything\u002Fsam_vit_l_0b3195.pth\n\n# ViT-B (smallest, fastest) - 375MB\nwget https:\u002F\u002Fdl.fbaipublicfiles.com\u002Fsegment_anything\u002Fsam_vit_b_01ec64.pth\n```\n\n### Basic usage with SamPredictor\n\n```python\nimport numpy as np\nfrom segment_anything import sam_model_registry, SamPredictor\n\n# Load model\nsam = sam_model_registry[\"vit_h\"](checkpoint=\"sam_vit_h_4b8939.pth\")\nsam.to(device=\"cuda\")\n\n# Create predictor\npredictor = SamPredictor(sam)\n\n# Set image (computes embeddings once)\nimage = cv2.imread(\"image.jpg\")\nimage = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\npredictor.set_image(image)\n\n# Predict with point prompts\ninput_point = np.array([[500, 375]])  # (x, y) coordinates\ninput_label = np.array([1])  # 1 = foreground, 0 = background\n\nmasks, scores, logits = predictor.predict(\n    point_coords=input_point,\n    point_labels=input_label,\n    multimask_output=True  # Returns 3 mask options\n)\n\n# Select best mask\nbest_mask = masks[np.argmax(scores)]\n```\n\n### HuggingFace Transformers\n\n```python\nimport torch\nfrom PIL import Image\nfrom transformers import SamModel, SamProcessor\n\n# Load model and processor\nmodel = SamModel.from_pretrained(\"facebook\u002Fsam-vit-huge\")\nprocessor = SamProcessor.from_pretrained(\"facebook\u002Fsam-vit-huge\")\nmodel.to(\"cuda\")\n\n# Process image with point prompt\nimage = Image.open(\"image.jpg\")\ninput_points = [[[450, 600]]]  # Batch of points\n\ninputs = processor(image, input_points=input_points, return_tensors=\"pt\")\ninputs = {k: v.to(\"cuda\") for k, v in inputs.items()}\n\n# Generate masks\nwith torch.no_grad():\n    outputs = model(**inputs)\n\n# Post-process masks to original size\nmasks = processor.image_processor.post_process_masks(\n    outputs.pred_masks.cpu(),\n    inputs[\"original_sizes\"].cpu(),\n    inputs[\"reshaped_input_sizes\"].cpu()\n)\n```\n\n## Core concepts\n\n### Model architecture\n\n```\nSAM Architecture:\n┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐\n│  Image Encoder  │────▶│ Prompt Encoder  │────▶│  Mask Decoder   │\n│     (ViT)       │     │ (Points\u002FBoxes)  │     │ (Transformer)   │\n└─────────────────┘     └─────────────────┘     └─────────────────┘\n        │                       │                       │\n   Image Embeddings      Prompt Embeddings         Masks + IoU\n   (computed once)       (per prompt)             predictions\n```\n\n### Model variants\n\n| Model | Checkpoint | Size | Speed | Accuracy |\n|-------|------------|------|-------|----------|\n| ViT-H | `vit_h` | 2.4 GB | Slowest | Best |\n| ViT-L | `vit_l` | 1.2 GB | Medium | Good |\n| ViT-B | `vit_b` | 375 MB | Fastest | Good |\n\n### Prompt types\n\n| Prompt | Description | Use Case |\n|--------|-------------|----------|\n| Point (foreground) | Click on object | Single object selection |\n| Point (background) | Click outside object | Exclude regions |\n| Bounding box | Rectangle around object | Larger objects |\n| Previous mask | Low-res mask input | Iterative refinement |\n\n## Interactive segmentation\n\n### Point prompts\n\n```python\n# Single foreground point\ninput_point = np.array([[500, 375]])\ninput_label = np.array([1])\n\nmasks, scores, logits = predictor.predict(\n    point_coords=input_point,\n    point_labels=input_label,\n    multimask_output=True\n)\n\n# Multiple points (foreground + background)\ninput_points = np.array([[500, 375], [600, 400], [450, 300]])\ninput_labels = np.array([1, 1, 0])  # 2 foreground, 1 background\n\nmasks, scores, logits = predictor.predict(\n    point_coords=input_points,\n    point_labels=input_labels,\n    multimask_output=False  # Single mask when prompts are clear\n)\n```\n\n### Box prompts\n\n```python\n# Bounding box [x1, y1, x2, y2]\ninput_box = np.array([425, 600, 700, 875])\n\nmasks, scores, logits = predictor.predict(\n    box=input_box,\n    multimask_output=False\n)\n```\n\n### Combined prompts\n\n```python\n# Box + points for precise control\nmasks, scores, logits = predictor.predict(\n    point_coords=np.array([[500, 375]]),\n    point_labels=np.array([1]),\n    box=np.array([400, 300, 700, 600]),\n    multimask_output=False\n)\n```\n\n### Iterative refinement\n\n```python\n# Initial prediction\nmasks, scores, logits = predictor.predict(\n    point_coords=np.array([[500, 375]]),\n    point_labels=np.array([1]),\n    multimask_output=True\n)\n\n# Refine with additional point using previous mask\nmasks, scores, logits = predictor.predict(\n    point_coords=np.array([[500, 375], [550, 400]]),\n    point_labels=np.array([1, 0]),  # Add background point\n    mask_input=logits[np.argmax(scores)][None, :, :],  # Use best mask\n    multimask_output=False\n)\n```\n\n## Automatic mask generation\n\n### Basic automatic segmentation\n\n```python\nfrom segment_anything import SamAutomaticMaskGenerator\n\n# Create generator\nmask_generator = SamAutomaticMaskGenerator(sam)\n\n# Generate all masks\nmasks = mask_generator.generate(image)\n\n# Each mask contains:\n# - segmentation: binary mask\n# - bbox: [x, y, w, h]\n# - area: pixel count\n# - predicted_iou: quality score\n# - stability_score: robustness score\n# - point_coords: generating point\n```\n\n### Customized generation\n\n```python\nmask_generator = SamAutomaticMaskGenerator(\n    model=sam,\n    points_per_side=32,          # Grid density (more = more masks)\n    pred_iou_thresh=0.88,        # Quality threshold\n    stability_score_thresh=0.95,  # Stability threshold\n    crop_n_layers=1,             # Multi-scale crops\n    crop_n_points_downscale_factor=2,\n    min_mask_region_area=100,    # Remove tiny masks\n)\n\nmasks = mask_generator.generate(image)\n```\n\n### Filtering masks\n\n```python\n# Sort by area (largest first)\nmasks = sorted(masks, key=lambda x: x['area'], reverse=True)\n\n# Filter by predicted IoU\nhigh_quality = [m for m in masks if m['predicted_iou'] > 0.9]\n\n# Filter by stability score\nstable_masks = [m for m in masks if m['stability_score'] > 0.95]\n```\n\n## Batched inference\n\n### Multiple images\n\n```python\n# Process multiple images efficiently\nimages = [cv2.imread(f\"image_{i}.jpg\") for i in range(10)]\n\nall_masks = []\nfor image in images:\n    predictor.set_image(image)\n    masks, _, _ = predictor.predict(\n        point_coords=np.array([[500, 375]]),\n        point_labels=np.array([1]),\n        multimask_output=True\n    )\n    all_masks.append(masks)\n```\n\n### Multiple prompts per image\n\n```python\n# Process multiple prompts efficiently (one image encoding)\npredictor.set_image(image)\n\n# Batch of point prompts\npoints = [\n    np.array([[100, 100]]),\n    np.array([[200, 200]]),\n    np.array([[300, 300]])\n]\n\nall_masks = []\nfor point in points:\n    masks, scores, _ = predictor.predict(\n        point_coords=point,\n        point_labels=np.array([1]),\n        multimask_output=True\n    )\n    all_masks.append(masks[np.argmax(scores)])\n```\n\n## ONNX deployment\n\n### Export model\n\n```bash\npython scripts\u002Fexport_onnx_model.py \\\n    --checkpoint sam_vit_h_4b8939.pth \\\n    --model-type vit_h \\\n    --output sam_onnx.onnx \\\n    --return-single-mask\n```\n\n### Use ONNX model\n\n```python\nimport onnxruntime\n\n# Load ONNX model\nort_session = onnxruntime.InferenceSession(\"sam_onnx.onnx\")\n\n# Run inference (image embeddings computed separately)\nmasks = ort_session.run(\n    None,\n    {\n        \"image_embeddings\": image_embeddings,\n        \"point_coords\": point_coords,\n        \"point_labels\": point_labels,\n        \"mask_input\": np.zeros((1, 1, 256, 256), dtype=np.float32),\n        \"has_mask_input\": np.array([0], dtype=np.float32),\n        \"orig_im_size\": np.array([h, w], dtype=np.float32)\n    }\n)\n```\n\n## Common workflows\n\n### Workflow 1: Annotation tool\n\n```python\nimport cv2\n\n# Load model\npredictor = SamPredictor(sam)\npredictor.set_image(image)\n\ndef on_click(event, x, y, flags, param):\n    if event == cv2.EVENT_LBUTTONDOWN:\n        # Foreground point\n        masks, scores, _ = predictor.predict(\n            point_coords=np.array([[x, y]]),\n            point_labels=np.array([1]),\n            multimask_output=True\n        )\n        # Display best mask\n        display_mask(masks[np.argmax(scores)])\n```\n\n### Workflow 2: Object extraction\n\n```python\ndef extract_object(image, point):\n    \"\"\"Extract object at point with transparent background.\"\"\"\n    predictor.set_image(image)\n\n    masks, scores, _ = predictor.predict(\n        point_coords=np.array([point]),\n        point_labels=np.array([1]),\n        multimask_output=True\n    )\n\n    best_mask = masks[np.argmax(scores)]\n\n    # Create RGBA output\n    rgba = np.zeros((image.shape[0], image.shape[1], 4), dtype=np.uint8)\n    rgba[:, :, :3] = image\n    rgba[:, :, 3] = best_mask * 255\n\n    return rgba\n```\n\n### Workflow 3: Medical image segmentation\n\n```python\n# Process medical images (grayscale to RGB)\nmedical_image = cv2.imread(\"scan.png\", cv2.IMREAD_GRAYSCALE)\nrgb_image = cv2.cvtColor(medical_image, cv2.COLOR_GRAY2RGB)\n\npredictor.set_image(rgb_image)\n\n# Segment region of interest\nmasks, scores, _ = predictor.predict(\n    box=np.array([x1, y1, x2, y2]),  # ROI bounding box\n    multimask_output=True\n)\n```\n\n## Output format\n\n### Mask data structure\n\n```python\n# SamAutomaticMaskGenerator output\n{\n    \"segmentation\": np.ndarray,  # H×W binary mask\n    \"bbox\": [x, y, w, h],        # Bounding box\n    \"area\": int,                 # Pixel count\n    \"predicted_iou\": float,      # 0-1 quality score\n    \"stability_score\": float,    # 0-1 robustness score\n    \"crop_box\": [x, y, w, h],    # Generation crop region\n    \"point_coords\": [[x, y]],    # Input point\n}\n```\n\n### COCO RLE format\n\n```python\nfrom pycocotools import mask as mask_utils\n\n# Encode mask to RLE\nrle = mask_utils.encode(np.asfortranarray(mask.astype(np.uint8)))\nrle[\"counts\"] = rle[\"counts\"].decode(\"utf-8\")\n\n# Decode RLE to mask\ndecoded_mask = mask_utils.decode(rle)\n```\n\n## Performance optimization\n\n### GPU memory\n\n```python\n# Use smaller model for limited VRAM\nsam = sam_model_registry[\"vit_b\"](checkpoint=\"sam_vit_b_01ec64.pth\")\n\n# Process images in batches\n# Clear CUDA cache between large batches\ntorch.cuda.empty_cache()\n```\n\n### Speed optimization\n\n```python\n# Use half precision\nsam = sam.half()\n\n# Reduce points for automatic generation\nmask_generator = SamAutomaticMaskGenerator(\n    model=sam,\n    points_per_side=16,  # Default is 32\n)\n\n# Use ONNX for deployment\n# Export with --return-single-mask for faster inference\n```\n\n## Common issues\n\n| Issue | Solution |\n|-------|----------|\n| Out of memory | Use ViT-B model, reduce image size |\n| Slow inference | Use ViT-B, reduce points_per_side |\n| Poor mask quality | Try different prompts, use box + points |\n| Edge artifacts | Use stability_score filtering |\n| Small objects missed | Increase points_per_side |\n\n## References\n\n- **[Advanced Usage](references\u002Fadvanced-usage.md)** - Batching, fine-tuning, integration\n- **[Troubleshooting](references\u002Ftroubleshooting.md)** - Common issues and solutions\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fsegment-anything\n- **Paper**: https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.02643\n- **Demo**: https:\u002F\u002Fsegment-anything.com\n- **SAM 2 (Video)**: https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fsegment-anything-2\n- **HuggingFace**: https:\u002F\u002Fhuggingface.co\u002Ffacebook\u002Fsam-vit-huge\n",{"name":5,"license":15,"metadata":24,"description":7},{"tags":25,"author":31,"version":32,"dependencies":33},[26,27,28,29,30],"Multimodal","Image Segmentation","Computer Vision","SAM","Zero-Shot","Orchestra Research","1.0.0",[34,35,36],"segment-anything","transformers>=4.30.0","torch>=1.7.0","main","1e72c52e0f3ffd72fbc6ba994fda0f4a6d63911a","payloads\u002Fskills\u002Fmultimodal-segment-anything","https:\u002F\u002Fgithub.com\u002Forigin-labs-ai\u002Fcode","https:\u002F\u002Fgithub.com\u002Forigin-labs-ai","2026-11-14 20:35:25",{"source":8,"skillName":5,"commands":44,"prompt":54,"zipAvailable":55},[45,48,51],{"tool":46,"command":47},"npx","npx skills add origin-labs-ai\u002Fcode --skill multimodal-segment-anything",{"tool":49,"command":50},"bunx","bunx skills add origin-labs-ai\u002Fcode --skill multimodal-segment-anything",{"tool":52,"command":53},"pnpm","pnpm dlx skills add origin-labs-ai\u002Fcode --skill multimodal-segment-anything","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Forigin-labs-ai\u002Fcode\u002Fblob\u002Fmain\u002Fpayloads\u002Fskills\u002Fmultimodal-segment-anything\u002FSKILL.md",true,[57,63],{"path":58,"resourceType":59,"resourceTypeDesc":60,"sizeBytes":61,"sourceHtmlUrl":62},"references\u002Fadvanced-usage.md","REFERENCE","参考文档",15803,"https:\u002F\u002Fgithub.com\u002Forigin-labs-ai\u002Fcode\u002Fblob\u002Fmain\u002Fpayloads\u002Fskills\u002Fmultimodal-segment-anything\u002Freferences\u002Fadvanced-usage.md",{"path":64,"resourceType":59,"resourceTypeDesc":60,"sizeBytes":65,"sourceHtmlUrl":66},"references\u002Ftroubleshooting.md",11914,"https:\u002F\u002Fgithub.com\u002Forigin-labs-ai\u002Fcode\u002Fblob\u002Fmain\u002Fpayloads\u002Fskills\u002Fmultimodal-segment-anything\u002Freferences\u002Ftroubleshooting.md",[68,76,84,92,100,109],{"skillNo":69,"name":70,"nameZh":6,"description":71,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":72,"sourceHtmlUrl":73,"categories":74,"keywords":75,"publishedAt":19},"01M7BP6Z7NBQXMB8Y5ZRR7PGCF","web-search","Implement web search capabilities using the z-ai-web-dev-sdk. Use this skill when the user needs to search for real-time information from the web, retrieve up-to-date content beyond the knowledge cutoff, or find the latest news and data. Returns structured search results with URLs, snippets, and metadata.","https:\u002F\u002Fgithub.com\u002Forigin-labs-ai\u002Fcode\u002Fblob\u002Fmain\u002Fwindows\u002Fsonar\u002Fmerger\u002Fcode-fusion-dead-20261101\u002Fskills\u002Fweb-search\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Forigin-labs-ai\u002Fcode\u002Fblob\u002Fmain\u002Fwindows\u002Fsonar\u002Fmerger\u002Fcode-fusion-dead-20261101\u002Fskills\u002Fweb-search\u002FSKILL.md",[],[],{"skillNo":77,"name":78,"nameZh":6,"description":79,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":80,"sourceHtmlUrl":81,"categories":82,"keywords":83,"publishedAt":19},"01M7BP6T13CWYHZSHGYG0RT885","web-reader","Implement web page content extraction capabilities using the z-ai-web-dev-sdk. Use this skill when the user needs to scrape web pages, extract article content, retrieve page metadata, or build applications that process web content. Supports automatic content extraction with title, HTML, and publication time retrieval.","https:\u002F\u002Fgithub.com\u002Forigin-labs-ai\u002Fcode\u002Fblob\u002Fmain\u002Fwindows\u002Fsonar\u002Fmerger\u002Fcode-fusion-dead-20261101\u002Fskills\u002Fweb-reader\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Forigin-labs-ai\u002Fcode\u002Fblob\u002Fmain\u002Fwindows\u002Fsonar\u002Fmerger\u002Fcode-fusion-dead-20261101\u002Fskills\u002Fweb-reader\u002FSKILL.md",[],[],{"skillNo":85,"name":86,"nameZh":6,"description":87,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":88,"sourceHtmlUrl":89,"categories":90,"keywords":91,"publishedAt":19},"01M7BP6GM9Z301PPT4ZRV049AN","video-understand","Implement specialized video understanding capabilities using the z-ai-web-dev-sdk. Use this skill when the user needs to analyze video content, understand motion and temporal sequences, extract information from video frames, describe video scenes, or perform video-based AI analysis. Optimized for MP4, AVI, MOV, and other common video formats.","https:\u002F\u002Fgithub.com\u002Forigin-labs-ai\u002Fcode\u002Fblob\u002Fmain\u002Fwindows\u002Fsonar\u002Fmerger\u002Fcode-fusion-dead-20261101\u002Fskills\u002Fvideo-understand\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Forigin-labs-ai\u002Fcode\u002Fblob\u002Fmain\u002Fwindows\u002Fsonar\u002Fmerger\u002Fcode-fusion-dead-20261101\u002Fskills\u002Fvideo-understand\u002FSKILL.md",[],[],{"skillNo":93,"name":94,"nameZh":6,"description":95,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":96,"sourceHtmlUrl":97,"categories":98,"keywords":99,"publishedAt":19},"01M7BP6ANRHQ1K7PVS11RBZCC7","Video Generation","Implement AI-powered video generation capabilities using the z-ai-web-dev-sdk. Use this skill when the user needs to generate videos from text prompts or images, create video content programmatically, or build applications that produce video outputs. Supports asynchronous task management with status polling and result retrieval.","https:\u002F\u002Fgithub.com\u002Forigin-labs-ai\u002Fcode\u002Fblob\u002Fmain\u002Fwindows\u002Fsonar\u002Fmerger\u002Fcode-fusion-dead-20261101\u002Fskills\u002Fvideo-generation\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Forigin-labs-ai\u002Fcode\u002Fblob\u002Fmain\u002Fwindows\u002Fsonar\u002Fmerger\u002Fcode-fusion-dead-20261101\u002Fskills\u002Fvideo-generation\u002FSKILL.md",[],[],{"skillNo":101,"name":102,"nameZh":6,"description":103,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":104,"licenseFileUrl":105,"sourceHtmlUrl":106,"categories":107,"keywords":108,"publishedAt":19},"01M7BP56W5SX2MMJWKMJVC66F0","skill-creator","Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Forigin-labs-ai\u002Fcode\u002Fblob\u002Fmain\u002Fwindows\u002Fsonar\u002Fmerger\u002Fcode-fusion-dead-20261101\u002Fskills\u002Fskill-creator\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Forigin-labs-ai\u002Fcode\u002Fblob\u002Fmain\u002Fwindows\u002Fsonar\u002Fmerger\u002Fcode-fusion-dead-20261101\u002Fskills\u002Fskill-creator\u002FSKILL.md",[],[],{"skillNo":110,"name":111,"nameZh":6,"description":112,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":104,"licenseFileUrl":6,"sourceHtmlUrl":113,"categories":114,"keywords":115,"publishedAt":19},"01M7BP503HPG0DBCN4K0JKCA88","seo-content-writer","Use when the user asks to \"write SEO content\", \"create a blog post\", \"write an article\", \"content writing\", \"draft optimized content\", \"write me an article\", \"create a blog post about\", \"help me write SEO content\", or \"draft content for\". Creates high-quality, SEO-optimized content that ranks in search engines. Applies on-page SEO best practices, keyword optimization, and content structure for maximum visibility and engagement. For AI citation optimization, see geo-content-optimizer. For updating existing content, see content-refresher.","https:\u002F\u002Fgithub.com\u002Forigin-labs-ai\u002Fcode\u002Fblob\u002Fmain\u002Fwindows\u002Fsonar\u002Fmerger\u002Fcode-fusion-dead-20261101\u002Fskills\u002Fseo-content-writer\u002FSKILL.md",[],[]]