{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"segment-anything-2","slug":"segment-anything-2","name":"Segment Anything 2","type":"model","url":"https://github.com/facebookresearch/sam2","page_url":"https://unfragile.ai/segment-anything-2","categories":["image-generation"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"segment-anything-2__cap_0","uri":"capability://image.visual.point.prompt.image.segmentation.with.transformer.based.mask.prediction","name":"point-prompt image segmentation with transformer-based mask prediction","description":"Accepts single or multiple point coordinates on an image and generates precise object segmentation masks using a vision transformer encoder paired with a lightweight mask decoder. The architecture encodes the image once, then efficiently processes point prompts through a prompt encoder that converts coordinates to embeddings, which are fused with image features via cross-attention mechanisms to produce per-pixel segmentation logits.","intents":["I need to segment an object in an image by clicking a point on it","I want to interactively select multiple objects in a single image using point clicks","I need to programmatically segment objects given their center coordinates"],"best_for":["interactive annotation tools and labeling applications","developers building computer vision pipelines requiring flexible object selection","researchers prototyping segmentation-based workflows"],"limitations":["Requires at least one point per object; ambiguous objects may need multiple points for disambiguation","Point precision matters — points must land on the target object, not background","Single-frame processing; no temporal context for video sequences"],"requires":["Python 3.8+","PyTorch 1.9+","Pre-trained SAM2 model checkpoint (38.9M–224.4M parameters depending on variant)","Input image in standard formats (PNG, JPEG, etc.)"],"input_types":["image (numpy array, PIL Image, or file path)","point coordinates (list of [x, y] tuples)","optional point labels (positive=1, negative=0 for refinement)"],"output_types":["binary segmentation mask (H×W boolean array)","confidence scores per mask","bounding box of segmented region"],"categories":["image-visual","interactive-segmentation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"segment-anything-2__cap_1","uri":"capability://image.visual.bounding.box.prompt.image.segmentation.with.adaptive.mask.refinement","name":"bounding-box-prompt image segmentation with adaptive mask refinement","description":"Accepts bounding box coordinates (top-left and bottom-right corners) and generates segmentation masks by encoding the box as corner point embeddings plus a special box token, then fusing these with image features through cross-attention. The decoder refines the mask iteratively to respect box boundaries while capturing fine object details within the box region.","intents":["I have bounding boxes from an object detector and need to refine them into precise masks","I want to segment objects defined by rectangular regions without clicking individual points","I need to batch-process multiple bounding boxes across an image"],"best_for":["post-processing pipelines following object detection (YOLO, Faster R-CNN, etc.)","annotation tools where users draw rectangles instead of clicking points","batch segmentation workflows with pre-computed bounding boxes"],"limitations":["Box must tightly contain the target object; loose boxes may segment background","Cannot segment objects partially visible at image edges if box extends beyond image","Assumes single primary object per box; overlapping objects within a box may cause ambiguity"],"requires":["Python 3.8+","PyTorch 1.9+","Pre-trained SAM2 model checkpoint","Bounding box coordinates in [x1, y1, x2, y2] format"],"input_types":["image (numpy array, PIL Image, or file path)","bounding box coordinates (list of [x1, y1, x2, y2] tuples)","optional negative points for refinement"],"output_types":["binary segmentation mask (H×W boolean array)","confidence scores per mask","refined bounding box of actual segmented region"],"categories":["image-visual","interactive-segmentation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"segment-anything-2__cap_10","uri":"capability://automation.workflow.model.checkpoint.loading.and.variant.selection.across.parameter.sizes","name":"model checkpoint loading and variant selection across parameter sizes","description":"Provides a unified interface for loading pre-trained SAM2 checkpoints in multiple sizes (Tiny 38.9M, Small 46M, Base-Plus 80.8M, Large 224.4M parameters) from local files or Hugging Face Hub, with automatic architecture instantiation and weight loading. The system handles checkpoint versioning, device placement (CPU/GPU), and optional quantization for memory efficiency.","intents":["I need to load a pre-trained SAM2 model for inference without training","I want to choose a model size based on my hardware constraints and accuracy requirements","I need to deploy SAM2 on different devices (GPU, CPU, mobile) with appropriate model variants"],"best_for":["developers integrating SAM2 into production applications","researchers comparing model sizes and accuracy-latency tradeoffs","teams deploying SAM2 across heterogeneous hardware (cloud GPUs, edge devices, CPUs)"],"limitations":["Large model (224.4M) requires 12GB+ VRAM; not suitable for consumer GPUs without quantization","Checkpoint files are large (150MB–900MB); initial download may be slow on limited bandwidth","No automatic quantization; users must manually implement INT8 or FP16 for memory reduction","Checkpoint versioning may cause compatibility issues if model architecture changes between releases"],"requires":["Python 3.8+","PyTorch 1.9+","Disk space for checkpoint (150MB–900MB depending on variant)","Internet connection for Hugging Face Hub download (optional if using local checkpoints)"],"input_types":["checkpoint path (local file or Hugging Face model ID)","model variant name ('tiny', 'small', 'base_plus', 'large')","optional: device specification ('cuda', 'cpu'), dtype ('float32', 'float16')"],"output_types":["instantiated SAM2Base model with loaded weights","model configuration (architecture, parameter count, input resolution)","optional: checkpoint metadata (training dataset, performance benchmarks)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"segment-anything-2__cap_11","uri":"capability://automation.workflow.batch.inference.with.dynamic.batching.and.memory.pooling","name":"batch inference with dynamic batching and memory pooling","description":"Supports batch processing of multiple images or video frames through a single forward pass, with dynamic batching that groups inputs of similar sizes to maximize GPU utilization. The system uses memory pooling to reuse allocated tensors across batch items, reducing allocation overhead and enabling efficient processing of large image collections.","intents":["I need to process 100+ images efficiently without writing a manual batching loop","I want to maximize GPU utilization by batching similar-sized inputs together","I need to reduce memory fragmentation when processing large datasets"],"best_for":["batch annotation pipelines processing thousands of images","dataset preprocessing for computer vision projects","large-scale video frame extraction and segmentation"],"limitations":["Dynamic batching requires similar input sizes; highly variable image dimensions reduce batching efficiency","Memory pooling adds complexity; debugging memory issues becomes harder","Batch size is limited by GPU VRAM; very large batches may exceed memory despite pooling","No automatic batch size tuning; users must manually tune batch size for their hardware"],"requires":["Python 3.8+","PyTorch 1.9+","Pre-trained SAM2 model checkpoint","GPU with sufficient VRAM for batch size (8GB+ recommended)"],"input_types":["list of images (numpy arrays, PIL Images, or file paths)","list of prompts (points, boxes, or masks) per image","batch size parameter"],"output_types":["list of segmentation masks (one per image)","optional: per-image confidence scores and metadata"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"segment-anything-2__cap_12","uri":"capability://image.visual.confidence.scoring.and.uncertainty.estimation.for.mask.predictions","name":"confidence scoring and uncertainty estimation for mask predictions","description":"Estimates prediction confidence for each segmentation mask through multiple mechanisms: predicted IoU (intersection-over-union with ground truth, estimated by the model), stability score (mask consistency under input perturbations), and logit magnitude. These scores enable filtering unreliable predictions and ranking masks by confidence, supporting downstream applications that require quality thresholds.","intents":["I need to filter out low-confidence segmentation masks automatically","I want to rank multiple mask candidates by reliability for interactive selection","I need to estimate uncertainty for downstream decision-making (e.g., flag uncertain regions for human review)"],"best_for":["quality-aware annotation pipelines that flag uncertain predictions","interactive tools that rank mask candidates by confidence","applications requiring confidence thresholds (medical imaging, autonomous systems)"],"limitations":["Predicted IoU is a model estimate, not ground truth; may be miscalibrated on out-of-distribution data","Stability score requires multiple forward passes (with input perturbations), adding 2-3x latency","Confidence scores are not calibrated across different model sizes; thresholds may differ between Tiny and Large variants","No explicit uncertainty quantification (e.g., Bayesian confidence intervals); scores are point estimates"],"requires":["Python 3.8+","PyTorch 1.9+","Pre-trained SAM2 model checkpoint","Segmentation mask predictions"],"input_types":["segmentation mask logits (pre-sigmoid scores)","optional: input image for stability score computation","optional: ground truth mask for IoU calculation"],"output_types":["predicted IoU score (0-1 float)","stability score (0-1 float, higher = more stable)","logit magnitude (raw model confidence)","combined confidence score (weighted average of above)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"segment-anything-2__cap_2","uri":"capability://image.visual.mask.prompt.iterative.refinement.for.segmentation.correction","name":"mask-prompt iterative refinement for segmentation correction","description":"Accepts a previous segmentation mask (binary or soft) as input and refines it by encoding the mask as a spatial feature map, concatenating it with image features, and passing through the decoder to produce an improved mask. Supports iterative refinement where outputs from one iteration become inputs to the next, enabling progressive segmentation correction through multiple rounds.","intents":["I have a rough segmentation mask and need to refine it to remove false positives or fill holes","I want to iteratively improve a mask by providing feedback masks in a loop","I need to correct automatic segmentation results with user-provided mask hints"],"best_for":["interactive annotation workflows where users refine auto-generated masks","post-processing pipelines correcting segmentation errors","iterative segmentation loops in medical imaging or precision agriculture"],"limitations":["Iterative refinement adds latency; typically 2-3 rounds before diminishing returns","Mask encoder assumes binary or soft masks; noisy or ambiguous masks may degrade refinement","Cannot recover from completely incorrect initial masks; requires reasonable starting point"],"requires":["Python 3.8+","PyTorch 1.9+","Pre-trained SAM2 model checkpoint","Initial segmentation mask (binary or soft float array)"],"input_types":["image (numpy array, PIL Image, or file path)","previous mask (binary or soft float array, same spatial dimensions as image)","optional additional point or box prompts for combined refinement"],"output_types":["refined binary segmentation mask (H×W boolean array)","confidence scores per mask","mask change delta (difference from input mask)"],"categories":["image-visual","interactive-segmentation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"segment-anything-2__cap_3","uri":"capability://image.visual.automatic.unsupervised.mask.generation.for.image.panoptic.segmentation","name":"automatic unsupervised mask generation for image panoptic segmentation","description":"Generates comprehensive segmentation masks for all objects in an image without user prompts by systematically sampling point grids across the image, running inference for each point, and merging overlapping masks using IoU-based deduplication. The SAM2AutomaticMaskGenerator class orchestrates this process, filtering low-confidence masks and returning a set of non-overlapping masks covering the entire image.","intents":["I need to automatically segment all objects in an image without manual annotation","I want to generate a panoptic segmentation (stuff + things) as a starting point for refinement","I need to extract all distinct regions from an image for downstream analysis"],"best_for":["batch processing pipelines requiring automatic segmentation without user interaction","dataset annotation acceleration where automatic masks are refined by humans","exploratory analysis of image content without predefined object categories"],"limitations":["Computational cost scales with image resolution; high-res images (4K+) require significant GPU memory or batch processing","Grid sampling may miss small objects or objects at grid boundaries; requires tuning points_per_side parameter","Merging heuristics (IoU threshold) may over-merge similar objects or under-merge complex scenes","No semantic labels; masks are instance-level only, requiring separate classification if labels needed"],"requires":["Python 3.8+","PyTorch 1.9+","Pre-trained SAM2 model checkpoint","GPU with sufficient VRAM (8GB+ recommended for high-resolution images)"],"input_types":["image (numpy array, PIL Image, or file path)","configuration parameters: points_per_side (default 32), pred_iou_thresh (default 0.88), stability_score_thresh (default 0.95)"],"output_types":["list of mask dictionaries, each containing: binary mask, predicted IoU, stability score, bounding box, area","optional: sorted by area or confidence score"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"segment-anything-2__cap_4","uri":"capability://image.visual.streaming.memory.augmented.video.object.tracking.across.frames","name":"streaming memory-augmented video object tracking across frames","description":"Tracks multiple objects through video sequences by maintaining a streaming memory buffer of encoded features from previous frames, using cross-frame attention to propagate object masks forward in time. The SAM2VideoPredictor processes frames sequentially, storing compressed representations of segmented objects in memory, then uses these memories to predict masks in subsequent frames without re-encoding the entire history, enabling real-time processing.","intents":["I need to track the same object across multiple video frames without re-annotating each frame","I want to segment multiple objects in a video and maintain consistent object IDs across frames","I need to process long video sequences efficiently without storing full-resolution features for every frame"],"best_for":["video annotation and labeling tools requiring temporal consistency","autonomous driving perception pipelines tracking vehicles and pedestrians","sports analytics and action recognition requiring object tracking","medical video analysis (endoscopy, ultrasound) with temporal object tracking"],"limitations":["Memory buffer has fixed capacity; very long videos (1000+ frames) may lose early-frame context","Tracking degrades if objects undergo significant appearance changes (occlusion, rotation, scale change)","Requires initial segmentation (point, box, or mask prompt) in first frame; cannot auto-detect new objects mid-video","Streaming design assumes sequential frame processing; random frame access requires recomputation"],"requires":["Python 3.8+","PyTorch 1.9+","Pre-trained SAM2 model checkpoint","Video frames as sequence of images or video file (MP4, MOV, etc.)","GPU with 8GB+ VRAM for real-time processing"],"input_types":["video frames (sequence of numpy arrays, PIL Images, or video file path)","initial prompt for frame 0 (point, box, or mask)","optional: object IDs for multi-object tracking"],"output_types":["per-frame segmentation masks for tracked objects","object IDs maintaining temporal consistency","confidence scores per mask per frame","optional: bounding boxes and centroids for tracking visualization"],"categories":["image-visual","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"segment-anything-2__cap_5","uri":"capability://image.visual.multi.object.video.segmentation.with.independent.prompt.per.object.tracking","name":"multi-object video segmentation with independent prompt-per-object tracking","description":"Extends video tracking to handle multiple objects simultaneously by maintaining separate memory streams for each tracked object, allowing independent prompts (points, boxes, masks) per object in the first frame. The system tracks each object through subsequent frames using dedicated memory buffers, enabling multi-object segmentation without object ID conflicts or cross-object interference.","intents":["I need to track 3+ distinct objects through a video with different segmentation prompts for each","I want to segment multiple people or vehicles in a video while maintaining separate object IDs","I need to handle object occlusion and re-identification when objects temporarily leave the frame"],"best_for":["crowd analysis and pedestrian tracking in surveillance video","sports analytics tracking multiple players or ball simultaneously","autonomous vehicle perception tracking vehicles, pedestrians, and cyclists","multi-animal behavior analysis in wildlife or laboratory settings"],"limitations":["Computational cost scales linearly with number of tracked objects; 10+ objects may exceed real-time performance","Memory buffer per object limits total tracking duration; very long videos require periodic memory reset","Cannot automatically detect and track new objects appearing mid-video; requires manual prompt for new objects","Occlusion handling relies on memory propagation; complete occlusion for many frames may cause tracking drift"],"requires":["Python 3.8+","PyTorch 1.9+","Pre-trained SAM2 model checkpoint","Video frames as sequence or file","GPU with 12GB+ VRAM for 5+ simultaneous object tracking"],"input_types":["video frames (sequence of numpy arrays or video file)","per-object prompts for frame 0 (dict mapping object_id to point/box/mask)","optional: object metadata (class, color, size hints)"],"output_types":["per-frame, per-object segmentation masks","object IDs with temporal consistency","per-object confidence scores across frames","optional: object trajectories (centroid paths)"],"categories":["image-visual","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"segment-anything-2__cap_6","uri":"capability://image.visual.torch.compile.optimized.video.inference.with.vos.specific.acceleration","name":"torch.compile-optimized video inference with vos-specific acceleration","description":"Provides SAM2VideoPredictorVOS, a specialized video predictor that wraps the base model with torch.compile() for graph-level optimization, reducing memory overhead and increasing throughput for video object segmentation (VOS) tasks. The optimization targets the streaming memory update and mask decoding loops, which are the computational bottlenecks in frame-by-frame processing.","intents":["I need to process video at real-time framerates (30+ FPS) on limited GPU resources","I want to reduce memory footprint for long video sequences to fit in GPU VRAM","I need to deploy video segmentation on edge devices or resource-constrained servers"],"best_for":["real-time video processing applications (live streaming, surveillance)","edge deployment on GPUs with limited VRAM (8GB or less)","batch video processing where throughput is critical","latency-sensitive applications (robotics, autonomous vehicles)"],"limitations":["torch.compile requires PyTorch 2.0+; not compatible with older PyTorch versions","First inference pass triggers compilation, adding 10-30 second overhead; subsequent passes are optimized","Compilation is GPU-specific; compiled graphs may not transfer between different GPU architectures","Dynamic shapes (variable frame sizes) may prevent compilation; requires fixed input dimensions"],"requires":["Python 3.8+","PyTorch 2.0+","CUDA 11.8+ (for GPU compilation)","Pre-trained SAM2 model checkpoint","GPU with compute capability 7.0+ (V100, A100, RTX series)"],"input_types":["video frames (fixed spatial dimensions, e.g., 1080p)","initial prompt for frame 0","optional: compilation mode ('default', 'reduce-overhead', 'max-autotune')"],"output_types":["per-frame segmentation masks (same as standard VideoPredictor)","performance metrics (FPS, memory usage, compilation time)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"segment-anything-2__cap_7","uri":"capability://image.visual.vision.transformer.image.encoder.with.hierarchical.feature.extraction","name":"vision-transformer image encoder with hierarchical feature extraction","description":"Encodes input images using a Vision Transformer (ViT) backbone that produces multi-scale hierarchical features through intermediate layer outputs, capturing both global semantic context and local spatial details. The encoder processes images at a fixed resolution (e.g., 1024×1024), producing feature pyramids that are used by both the mask decoder and memory systems for efficient cross-attention.","intents":["I need to extract rich semantic features from images for downstream segmentation tasks","I want to leverage pre-trained vision transformer knowledge for zero-shot segmentation","I need multi-scale features to handle objects at different sizes in the same image"],"best_for":["foundation model builders extending SAM2 with additional tasks","researchers analyzing what semantic information SAM2 learns","developers building custom segmentation pipelines on top of SAM2 features"],"limitations":["Fixed input resolution (1024×1024); images are resized/padded, potentially distorting aspect ratios","Encoder is frozen during inference; cannot fine-tune on domain-specific data without retraining","Feature extraction adds latency (~100-200ms per image); amortized across multiple prompts but still a bottleneck for single-prompt inference","Hierarchical features require storing intermediate activations, increasing memory usage"],"requires":["Python 3.8+","PyTorch 1.9+","Pre-trained SAM2 model checkpoint","Input image in standard formats"],"input_types":["image (numpy array, PIL Image, or file path)","optional: image normalization parameters (mean, std)"],"output_types":["multi-scale feature maps (list of tensors at different resolutions)","image embeddings (encoded representation)","optional: attention maps for interpretability"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"segment-anything-2__cap_8","uri":"capability://image.visual.lightweight.mask.decoder.with.iterative.refinement.loops","name":"lightweight mask decoder with iterative refinement loops","description":"Decodes segmentation masks from image features and prompt embeddings using a lightweight transformer decoder with iterative refinement, where each iteration refines the mask prediction by re-attending to image features and previous mask predictions. The decoder uses a small number of transformer blocks (2-4) to keep inference latency low while maintaining accuracy through multiple refinement iterations.","intents":["I need to generate precise segmentation masks from image features and prompts efficiently","I want to refine masks iteratively without re-encoding the image","I need to balance mask accuracy with inference latency for interactive applications"],"best_for":["interactive segmentation tools requiring sub-100ms latency","real-time video processing where mask decoding is the bottleneck","mobile or edge deployment with limited compute"],"limitations":["Iterative refinement adds latency; typically 2-4 iterations needed for high-quality masks","Lightweight decoder may struggle with complex object boundaries in cluttered scenes","Refinement iterations assume convergence; pathological cases may oscillate without improvement","No explicit boundary smoothing; raw decoder outputs may have jagged edges requiring post-processing"],"requires":["Python 3.8+","PyTorch 1.9+","Pre-trained SAM2 model checkpoint","Image features from encoder and prompt embeddings"],"input_types":["image features (multi-scale feature maps from encoder)","prompt embeddings (from point, box, or mask encoder)","optional: previous mask predictions for iterative refinement"],"output_types":["binary segmentation mask (H×W boolean array)","mask logits (pre-sigmoid scores for confidence estimation)","optional: intermediate masks from each refinement iteration"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"segment-anything-2__cap_9","uri":"capability://image.visual.cross.attention.fusion.of.image.features.and.prompt.embeddings","name":"cross-attention fusion of image features and prompt embeddings","description":"Fuses image features with prompt embeddings (from points, boxes, or masks) using cross-attention mechanisms, where prompt embeddings attend to image features to identify relevant regions, and image features are updated based on prompt context. This fusion enables the decoder to focus on prompt-relevant image regions, improving segmentation accuracy and enabling multi-prompt composition.","intents":["I need to combine multiple prompts (e.g., positive and negative points) for refined segmentation","I want to understand which image regions are most relevant to a given prompt","I need to handle ambiguous objects by combining point and box prompts"],"best_for":["interactive annotation tools supporting multi-prompt refinement","research on prompt-based segmentation and attention mechanisms","applications requiring explainability through attention visualization"],"limitations":["Cross-attention computation scales quadratically with feature map size; high-resolution features add latency","Multiple prompts require multiple attention passes; combining 5+ prompts may exceed real-time budget","Attention weights are learned; may not align with human intuition about prompt relevance","No explicit handling of prompt conflicts; contradictory prompts (e.g., overlapping positive/negative points) may produce undefined behavior"],"requires":["Python 3.8+","PyTorch 1.9+","Pre-trained SAM2 model checkpoint","Image features and prompt embeddings"],"input_types":["image features (multi-scale feature maps)","prompt embeddings (list of embeddings from point/box/mask encoders)","optional: prompt weights or priorities"],"output_types":["fused feature maps (image features updated with prompt context)","attention weights (for visualization and interpretability)","optional: per-prompt contribution scores"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"segment-anything-2__headline","uri":"capability://image.visual.promptable.visual.segmentation.model.for.images.and.videos","name":"promptable visual segmentation model for images and videos","description":"Segment Anything 2 is a cutting-edge foundation model designed for promptable visual segmentation, enabling zero-shot object segmentation in both images and videos using various input prompts like points, boxes, or text.","intents":["best visual segmentation model","visual segmentation for images and videos","top promptable segmentation tools","AI model for object segmentation","real-time video segmentation solutions"],"best_for":["developers needing flexible segmentation solutions"],"limitations":["may require substantial computational resources for large models"],"requires":["input images or videos"],"input_types":["images","videos"],"output_types":["segmented images","segmented video frames"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","PyTorch 1.9+","Pre-trained SAM2 model checkpoint (38.9M–224.4M parameters depending on variant)","Input image in standard formats (PNG, JPEG, etc.)","Pre-trained SAM2 model checkpoint","Bounding box coordinates in [x1, y1, x2, y2] format","Disk space for checkpoint (150MB–900MB depending on variant)","Internet connection for Hugging Face Hub download (optional if using local checkpoints)","GPU with sufficient VRAM for batch size (8GB+ recommended)","Segmentation mask predictions"],"failure_modes":["Requires at least one point per object; ambiguous objects may need multiple points for disambiguation","Point precision matters — points must land on the target object, not background","Single-frame processing; no temporal context for video sequences","Box must tightly contain the target object; loose boxes may segment background","Cannot segment objects partially visible at image edges if box extends beyond image","Assumes single primary object per box; overlapping objects within a box may cause ambiguity","Large model (224.4M) requires 12GB+ VRAM; not suitable for consumer GPUs without quantization","Checkpoint files are large (150MB–900MB); initial download may be slow on limited bandwidth","No automatic quantization; users must manually implement INT8 or FP16 for memory reduction","Checkpoint versioning may cause compatibility issues if model architecture changes between releases","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:05.296Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=segment-anything-2","compare_url":"https://unfragile.ai/compare?artifact=segment-anything-2"}},"signature":"OO1jRXVK2ZPXvmIYoO5jKcq5lC+0j+N8VcZlBvNC6STFNbJv6UKOR3t1ajMujojrWWQpq0togYxIa4bTz/eTBw==","signedAt":"2026-06-21T03:08:47.177Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/segment-anything-2","artifact":"https://unfragile.ai/segment-anything-2","verify":"https://unfragile.ai/api/v1/verify?slug=segment-anything-2","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}