{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-ultralytics-ultralytics-snippets","slug":"ultralytics-snippets","name":"Ultralytics Snippets","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=Ultralytics.ultralytics-snippets","page_url":"https://unfragile.ai/ultralytics-snippets","categories":["code-editors"],"tags":["__web_extension","ai","classification","computer vision","machine learning","object detection","python","segmentation","snippet","snippets","ultralytics","yolo"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-ultralytics-ultralytics-snippets__cap_0","uri":"capability://code.generation.editing.tab.triggered.code.snippet.expansion.for.ultralytics.library.imports","name":"tab-triggered code snippet expansion for ultralytics library imports","description":"Expands predefined code templates for Ultralytics library imports (e.g., `ultra.import-model`, `ultra.import-results`) via VS Code's native snippet system. User types the snippet alias, presses Tab, and the extension inserts a fully-formed import statement with placeholder fields for navigation. Uses VS Code's built-in snippet expansion engine with TextMate-compatible syntax, requiring no custom parsing or code generation.","intents":["Quickly insert standard Ultralytics import statements without manual typing","Reduce boilerplate when starting a new YOLO detection or segmentation script","Maintain consistent import patterns across team projects"],"best_for":["Python developers using Ultralytics YOLO for computer vision tasks","Teams standardizing on Ultralytics library conventions","Developers new to the Ultralytics API who need reference patterns"],"limitations":["Snippets are static templates — no context-aware import optimization based on project dependencies","Cannot detect which Ultralytics submodules are already imported to avoid duplicates","No support for conditional imports or version-specific import paths","Requires manual navigation between placeholder fields; no automatic field population"],"requires":["Visual Studio Code (version unspecified, likely 1.50+)","Ultralytics library installed locally (for code to execute, not for snippet expansion)","Python 3.8+ (for the actual Ultralytics library, not the extension itself)"],"input_types":["text (snippet alias typed by user)"],"output_types":["code (Python import statements with placeholder fields)"],"categories":["code-generation-editing","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ultralytics-ultralytics-snippets__cap_1","uri":"capability://code.generation.editing.model.instantiation.snippet.templates.with.configurable.keyword.arguments","name":"model instantiation snippet templates with configurable keyword arguments","description":"Provides pre-written code templates for instantiating Ultralytics YOLO models (YOLO11, YOLO-World, SAM2) with dropdown-selectable keyword arguments. When expanded, snippets include placeholder fields for model paths, confidence thresholds, device selection, and other hyperparameters. Dropdown menus (added in Jan 2025 update) allow users to select boolean flags and parameter values without manual typing, reducing syntax errors and API misuse.","intents":["Instantiate a YOLO model with correct syntax and common parameter patterns","Quickly toggle between CPU/GPU device selection without memorizing parameter names","Explore available model configuration options through dropdown suggestions"],"best_for":["Computer vision engineers prototyping detection/segmentation pipelines","Teams standardizing model initialization patterns across codebases","Developers unfamiliar with Ultralytics YOLO API parameter names and valid values"],"limitations":["Dropdown options are static and predefined — cannot dynamically list available model weights or local model files","No validation of parameter combinations (e.g., conflicting device/precision settings)","Snippets do not auto-populate with project-specific model paths or configuration files","No support for advanced features like custom model architectures or fine-tuned weight loading beyond basic path placeholders"],"requires":["Visual Studio Code with snippet expansion support","Ultralytics library installed (for actual model loading at runtime)","Model weights downloaded locally or accessible via Ultralytics Hub (not handled by extension)"],"input_types":["text (snippet alias)","dropdown selection (for kwargs)"],"output_types":["code (Python YOLO instantiation with kwargs)"],"categories":["code-generation-editing","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ultralytics-ultralytics-snippets__cap_10","uri":"capability://automation.workflow.snippet.library.version.synchronization.with.ultralytics.releases","name":"snippet library version synchronization with ultralytics releases","description":"Automatically updates snippet templates to match new Ultralytics library releases, including new model variants (YOLO11, SAM2), API changes, and new features (tracking, export formats). Updates are released through the VS Code Extension Marketplace and applied automatically or on-demand. Snippet library is maintained by Ultralytics developers alongside the main library, ensuring accuracy and completeness.","intents":["Keep snippet templates in sync with the latest Ultralytics library version without manual updates","Access new features (e.g., YOLO-World, SAM2) via snippets immediately after library release","Avoid using deprecated API patterns due to automatic snippet updates"],"best_for":["Teams using the latest Ultralytics library versions","Developers who want to stay current with new features and API changes","Organizations with automated dependency update workflows"],"limitations":["Snippet updates are released on Ultralytics' schedule — may lag behind library releases by days or weeks","No mechanism to pin snippets to a specific Ultralytics version; all users receive the latest snippets","Backward compatibility is not guaranteed — snippets may reflect breaking API changes","No changelog or release notes for snippet updates; users must infer changes from version history"],"requires":["Visual Studio Code with automatic extension updates enabled (default)","Internet connection to download extension updates"],"input_types":["automatic (no user input required)"],"output_types":["updated snippet library"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ultralytics-ultralytics-snippets__cap_2","uri":"capability://code.generation.editing.results.object.field.accessor.snippet.templates","name":"results object field accessor snippet templates","description":"Provides code snippets for accessing detection and segmentation output fields from Ultralytics Results objects (e.g., `ultra.results-boxes`, `ultra.results-masks`, `ultra.results-keypoints`). Snippets expand to show correct attribute access patterns (e.g., `results[0].boxes.xyxy`, `results[0].masks.data`) with placeholder fields for iteration and field selection. Enables developers to quickly reference the nested structure of Results without consulting documentation.","intents":["Access bounding box coordinates, confidence scores, and class IDs from detection results","Extract segmentation masks or keypoint coordinates from model outputs","Iterate over multiple detections in a batch without memorizing Results object structure"],"best_for":["Computer vision engineers post-processing YOLO model outputs","Developers building custom annotation or visualization pipelines","Teams standardizing how Results objects are accessed across codebases"],"limitations":["Snippets show only common field accessors — does not cover all Results attributes (e.g., custom metadata, auxiliary outputs)","No type hints or IDE autocomplete enhancement — snippets are plain text, not integrated with Python language server","Does not validate that accessed fields exist for the model type (e.g., keypoints only available for pose models)","No support for batch processing patterns or vectorized operations on Results"],"requires":["Visual Studio Code","Ultralytics library with Results class (all recent versions)","Model inference already performed (snippets assume results object exists)"],"input_types":["text (snippet alias)"],"output_types":["code (Python attribute access patterns)"],"categories":["code-generation-editing","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ultralytics-ultralytics-snippets__cap_3","uri":"capability://code.generation.editing.annotation.format.conversion.function.import.snippets","name":"annotation format conversion function import snippets","description":"Provides import statements for Ultralytics format conversion utilities (e.g., `ultra.import-coco2yolo`, `ultra.import-bbox2seg`, `ultra.import-seg2bbox`, `ultra.import-box-convert`). Snippets expand to import the correct conversion function from `ultralytics.data.converter` or related modules, with placeholder fields for source/destination paths. Enables developers to quickly set up dataset format conversion workflows without searching for the correct module path.","intents":["Import COCO-to-YOLO format converter for dataset preparation","Set up bounding box to segmentation mask conversion pipeline","Access Ultralytics' built-in format conversion utilities without manual module path lookup"],"best_for":["Machine learning engineers preparing datasets for YOLO training","Teams migrating from other annotation formats (COCO, Pascal VOC) to YOLO format","Developers building automated dataset preprocessing pipelines"],"limitations":["Snippets only import the conversion functions — do not provide full conversion pipeline templates (e.g., no file I/O or batch processing boilerplate)","No validation of source dataset format or structure before conversion","Conversion parameters (e.g., class mapping, coordinate system) must be manually configured after snippet expansion","Does not handle edge cases like missing annotations or malformed input files"],"requires":["Visual Studio Code","Ultralytics library with converter module (version 8.0+)","Source dataset files in the format being converted from"],"input_types":["text (snippet alias)"],"output_types":["code (Python import statements for converter functions)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ultralytics-ultralytics-snippets__cap_4","uri":"capability://code.generation.editing.multi.object.tracking.boilerplate.snippet.templates","name":"multi-object tracking boilerplate snippet templates","description":"Provides code snippets for setting up multi-object tracking (MOT) workflows with Ultralytics YOLO models. Snippets expand to show the correct pattern for initializing a tracker, processing video frames, and accessing track IDs and trajectories. Includes placeholder fields for tracker type selection, video source configuration, and output handling. Added in Aug 2024 update to support tracking-specific use cases.","intents":["Set up a multi-object tracking pipeline for video analysis without manual boilerplate","Configure tracker parameters (e.g., tracker type, persistence settings) with correct syntax","Iterate over tracked objects and access track IDs and historical positions"],"best_for":["Computer vision engineers building video surveillance or sports analytics applications","Developers prototyping multi-object tracking pipelines for autonomous systems","Teams standardizing MOT workflows across projects"],"limitations":["Snippets provide basic tracking setup — do not include advanced features like re-identification (ReID) or custom tracker algorithms","No built-in support for multi-camera tracking or cross-camera re-identification","Tracker configuration options are limited to common parameters; advanced tuning requires manual code editing","No integration with video I/O libraries (e.g., OpenCV) — snippets assume video source is already available"],"requires":["Visual Studio Code","Ultralytics library with tracking support (version 8.0+)","Video file or camera stream accessible at runtime"],"input_types":["text (snippet alias)"],"output_types":["code (Python tracking pipeline boilerplate)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ultralytics-ultralytics-snippets__cap_5","uri":"capability://code.generation.editing.model.export.snippet.templates.for.format.conversion","name":"model export snippet templates for format conversion","description":"Provides code snippets for exporting trained YOLO models to different deployment formats (ONNX, TensorRT, CoreML, TensorFlow SavedModel, etc.). Snippets expand to show the correct method call pattern (e.g., `model.export(format='onnx')`) with placeholder fields for format selection, export path, and optional parameters. Enables developers to quickly set up model export workflows without consulting the export API documentation.","intents":["Export a trained YOLO model to ONNX format for cross-platform inference","Convert a PyTorch model to TensorRT for optimized GPU inference","Generate CoreML or TensorFlow SavedModel for mobile or edge deployment"],"best_for":["Machine learning engineers deploying YOLO models to production environments","Developers optimizing models for specific hardware (GPU, mobile, edge devices)","Teams standardizing model export workflows across projects"],"limitations":["Snippets show only basic export parameters — advanced optimization options (e.g., quantization, pruning) require manual configuration","No validation of export format compatibility with target hardware or framework","Export success depends on external dependencies (e.g., ONNX Runtime, TensorRT) not managed by the extension","Does not provide post-export validation or performance benchmarking templates"],"requires":["Visual Studio Code","Ultralytics library with export functionality (version 8.0+)","Trained YOLO model file (.pt) available locally","Target framework dependencies installed (e.g., onnx, tensorrt) for actual export"],"input_types":["text (snippet alias)"],"output_types":["code (Python model export method calls)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ultralytics-ultralytics-snippets__cap_6","uri":"capability://code.generation.editing.yolo.world.custom.prompt.snippet.template","name":"yolo-world custom prompt snippet template","description":"Provides a code snippet for setting up YOLO-World models with custom text prompts for zero-shot object detection. Snippet expands to show the correct pattern for initializing a YOLO-World model and configuring custom class names as text prompts. Includes placeholder fields for prompt text and inference parameters. Added in July 2024 to support YOLO-World's unique prompt-based detection capability.","intents":["Set up zero-shot object detection with custom text prompts without manual API exploration","Configure YOLO-World model for detecting arbitrary object classes defined by text descriptions","Quickly prototype detection tasks that don't require labeled training data"],"best_for":["Computer vision engineers building flexible detection systems for novel object classes","Researchers prototyping zero-shot detection applications","Developers building interactive applications where users can define detection targets via text"],"limitations":["Snippet provides basic prompt configuration — does not include advanced prompt engineering techniques or multi-prompt strategies","No guidance on prompt quality or optimization; detection accuracy depends entirely on prompt wording","YOLO-World models are larger and slower than standard YOLO models; snippet does not address performance trade-offs","No built-in support for prompt caching or batch prompt processing"],"requires":["Visual Studio Code","Ultralytics library with YOLO-World support (version 8.0+)","YOLO-World model weights downloaded (e.g., yoloworld-l)"],"input_types":["text (snippet alias and custom prompts)"],"output_types":["code (Python YOLO-World initialization with custom prompts)"],"categories":["code-generation-editing","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ultralytics-ultralytics-snippets__cap_7","uri":"capability://code.generation.editing.sam2.segmentation.model.snippet.templates","name":"sam2 segmentation model snippet templates","description":"Provides code snippets for using Segment Anything Model 2 (SAM2) for interactive and automatic segmentation tasks. Snippets expand to show the correct pattern for initializing SAM2 models, configuring prompts (points, boxes, masks), and processing segmentation outputs. Includes placeholder fields for prompt type selection and output handling. Added in July 2024 following SAM2 release.","intents":["Set up SAM2 for interactive segmentation with point or bounding box prompts","Configure SAM2 for automatic segmentation of entire images or video frames","Access segmentation masks and confidence scores from SAM2 outputs"],"best_for":["Computer vision engineers building interactive annotation or segmentation tools","Researchers prototyping segmentation pipelines with foundation models","Developers integrating SAM2 into larger vision applications"],"limitations":["Snippets provide basic SAM2 setup — do not include advanced features like multi-mask prediction or mask refinement","No support for video-specific SAM2 features (e.g., temporal consistency, frame-to-frame tracking)","Prompt engineering (point placement, box selection) is not automated; users must manually define prompts","SAM2 is computationally expensive; snippets do not address optimization or batching strategies"],"requires":["Visual Studio Code","Ultralytics library with SAM2 support (version 8.0+)","SAM2 model weights downloaded (e.g., sam2-large)"],"input_types":["text (snippet alias)"],"output_types":["code (Python SAM2 initialization and segmentation code)"],"categories":["code-generation-editing","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ultralytics-ultralytics-snippets__cap_8","uri":"capability://code.generation.editing.yolo11.model.variant.snippet.templates","name":"yolo11 model variant snippet templates","description":"Provides code snippets for instantiating different YOLO11 model variants (nano, small, medium, large, extra-large) with correct model weight names and default configurations. Snippets expand to show the proper syntax for loading each variant (e.g., `YOLO('yolov11n.pt')`) with placeholder fields for task type (detection, segmentation, pose) and inference parameters. YOLO11 is the default model variant as of Oct 2024.","intents":["Quickly instantiate a YOLO11 model variant without manually typing weight file names","Select the appropriate model size for a given hardware/latency constraint","Explore available YOLO11 variants and their naming conventions"],"best_for":["Computer vision engineers selecting model variants for production deployment","Developers prototyping detection pipelines and comparing model sizes","Teams standardizing on YOLO11 across projects"],"limitations":["Snippets show only standard YOLO11 variants — do not support custom or fine-tuned model weights","No automatic selection based on hardware constraints or latency requirements","Does not provide performance benchmarks or memory usage estimates for each variant","Assumes model weights are downloaded from Ultralytics Hub; does not handle local weight paths"],"requires":["Visual Studio Code","Ultralytics library with YOLO11 support (version 8.0+)","Internet connection for automatic weight download (first use)"],"input_types":["text (snippet alias)"],"output_types":["code (Python YOLO11 model instantiation)"],"categories":["code-generation-editing","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ultralytics-ultralytics-snippets__cap_9","uri":"capability://code.generation.editing.neovim.snippet.compatibility.layer","name":"neovim snippet compatibility layer","description":"Extends snippet support to Neovim editor via a compatibility layer that translates VS Code TextMate snippet syntax to Neovim's native snippet format (likely using vim-snipmate or LuaSnip). Allows Neovim users to access the same Ultralytics snippet library as VS Code users, though implementation details are not documented. Mentioned in documentation as supported but mechanism is unclear.","intents":["Use Ultralytics snippets in Neovim without maintaining separate snippet sets","Maintain consistent code patterns across VS Code and Neovim development environments","Access Ultralytics library templates in a lightweight terminal-based editor"],"best_for":["Neovim users working with Ultralytics library","Teams with mixed VS Code and Neovim developers","Developers preferring terminal-based editors but needing library-specific snippets"],"limitations":["Neovim compatibility mechanism is undocumented — unclear which snippet plugin is required (vim-snipmate, LuaSnip, etc.)","Dropdown-based kwargs selection (added Jan 2025) may not work in Neovim due to different snippet engine capabilities","No official Neovim plugin or package manager integration documented","Snippet trigger mechanism may differ between VS Code and Neovim, requiring user configuration"],"requires":["Neovim (version unspecified)","Snippet plugin compatible with TextMate syntax (vim-snipmate, LuaSnip, or equivalent)","Manual configuration to load Ultralytics snippets (method not documented)"],"input_types":["text (snippet alias)"],"output_types":["code (Python Ultralytics templates)"],"categories":["code-generation-editing","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"moderate","permissions":["Visual Studio Code (version unspecified, likely 1.50+)","Ultralytics library installed locally (for code to execute, not for snippet expansion)","Python 3.8+ (for the actual Ultralytics library, not the extension itself)","Visual Studio Code with snippet expansion support","Ultralytics library installed (for actual model loading at runtime)","Model weights downloaded locally or accessible via Ultralytics Hub (not handled by extension)","Visual Studio Code with automatic extension updates enabled (default)","Internet connection to download extension updates","Visual Studio Code","Ultralytics library with Results class (all recent versions)"],"failure_modes":["Snippets are static templates — no context-aware import optimization based on project dependencies","Cannot detect which Ultralytics submodules are already imported to avoid duplicates","No support for conditional imports or version-specific import paths","Requires manual navigation between placeholder fields; no automatic field population","Dropdown options are static and predefined — cannot dynamically list available model weights or local model files","No validation of parameter combinations (e.g., conflicting device/precision settings)","Snippets do not auto-populate with project-specific model paths or configuration files","No support for advanced features like custom model architectures or fine-tuned weight loading beyond basic path placeholders","Snippet updates are released on Ultralytics' schedule — may lag behind library releases by days or weeks","No mechanism to pin snippets to a specific Ultralytics version; all users receive the latest snippets","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.45,"quality":0.32,"ecosystem":0.35000000000000003,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:34.803Z","last_scraped_at":"2026-05-03T15:20:36.253Z","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=ultralytics-snippets","compare_url":"https://unfragile.ai/compare?artifact=ultralytics-snippets"}},"signature":"FN5OFjli8MZB0XH9o/05KFZlIySb8VLPgLL8Q0VrmqlXM7U3Q6/ktfTsWCpoZxvjorddObO88oEhQj9q8tCZAA==","signedAt":"2026-06-22T00:12:58.337Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ultralytics-snippets","artifact":"https://unfragile.ai/ultralytics-snippets","verify":"https://unfragile.ai/api/v1/verify?slug=ultralytics-snippets","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"}}