{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-andaoai-yolo-labeling-vs","slug":"yolo-labeling","name":"YOLO Labeling","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=AnDaoAi.yolo-labeling-vs","page_url":"https://unfragile.ai/yolo-labeling","categories":["model-training"],"tags":["ai","annotation","attention mechanism","bounding box","coco","computer vision","cv","darknet","data labeling","dataset","deep learning","detection","image annotation","instance segmentation","keybindings","labeling","machine learning","neural network","object detection","pytorch","real-time","segmentation","tensorflow","transformer","ultralytics","yaml","yolo","yolov5","yolov8"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-andaoai-yolo-labeling-vs__cap_0","uri":"capability://image.visual.yaml.driven.yolo.dataset.visualization.with.embedded.image.preview","name":"yaml-driven yolo dataset visualization with embedded image preview","description":"Parses YOLO-format YAML configuration files within VS Code workspace to dynamically load and display associated image files in a sidebar panel. The extension reads YAML metadata (dataset paths, image references, class definitions) and renders images with overlaid bounding box annotations without requiring external tools. Integration occurs via right-click context menu on YAML files, establishing a direct link between configuration and visual preview.","intents":["View labeled images directly from YOLO dataset YAML configs without switching to external annotation tools","Quickly inspect dataset structure and image-label pairs during model training setup","Verify YAML configuration correctness by seeing actual images it references"],"best_for":["ML engineers building YOLO detection/segmentation models who want annotation review integrated into their IDE","Dataset curators validating YOLO dataset configurations without context switching","Teams using VS Code as primary development environment for computer vision projects"],"limitations":["Requires YAML files to be properly formatted and image paths to be relative or absolute within workspace — malformed configs will fail silently","No validation of YAML schema — incorrect field names or structure won't trigger warnings","Performance with large datasets (>1000 images) unknown — no documented optimization for batch loading","Image loading speed depends on disk I/O and image file sizes; no caching mechanism documented"],"requires":["VS Code 1.85.0 or higher","YOLO-format YAML configuration file in workspace","Image files referenced in YAML must exist in workspace or accessible via relative paths","Supported dataset format (COCO8, COCO128, COCO8-seg, COCO8-pose, Tiger-pose, DOTA8)"],"input_types":["YAML configuration files (YOLO format)","Image files (PNG, JPG, JPEG — specific formats not documented)"],"output_types":["Rendered image with bounding box overlays","Class label text annotations","Visual representation of detection/segmentation masks"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-andaoai-yolo-labeling-vs__cap_1","uri":"capability://image.visual.real.time.bounding.box.and.segmentation.mask.overlay.rendering","name":"real-time bounding box and segmentation mask overlay rendering","description":"Renders YOLO annotation data (bounding boxes for detection, polygon masks for segmentation, keypoints for pose) as visual overlays on images within the extension's preview panel. The extension parses annotation coordinates from YAML/text format and draws them as geometric shapes (rectangles, polygons, points) with class labels and confidence scores. Rendering occurs client-side in VS Code's webview component without external rendering libraries.","intents":["Visually verify that bounding box coordinates in YOLO format are correct before training","Inspect segmentation masks to ensure pixel-level annotations match object boundaries","Review pose keypoint annotations to validate joint positions for pose estimation models"],"best_for":["Computer vision engineers validating annotation quality across detection, segmentation, and pose datasets","Dataset QA teams identifying labeling errors before model training","Researchers comparing annotation formats (COCO vs YOLO) visually"],"limitations":["Overlay rendering performance degrades with high annotation density (>100 boxes per image) — no documented optimization","No interactive editing of bounding boxes — read-only visualization only","Mask rendering limited to supported formats (COCO8-seg, COCO8-pose, DOTA8) — other segmentation formats not planned","No confidence score filtering — all annotations rendered regardless of detection confidence"],"requires":["VS Code 1.85.0 or higher","Supported annotation format (COCO8, COCO128, COCO8-seg, COCO8-pose, Tiger-pose, DOTA8)","Valid YOLO-format annotation data in YAML or associated text files"],"input_types":["YOLO annotation coordinates (normalized or pixel format)","Class IDs and labels","Segmentation polygon coordinates (for mask formats)","Keypoint coordinates (for pose formats)"],"output_types":["Rendered image with geometric overlays (rectangles, polygons, points)","Class label text with optional confidence scores","Color-coded annotations by class"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-andaoai-yolo-labeling-vs__cap_2","uri":"capability://automation.workflow.sequential.image.navigation.through.yolo.datasets","name":"sequential image navigation through yolo datasets","description":"Provides keyboard-driven navigation (previous/next image) through images in a YOLO dataset, maintaining state of current image index and automatically loading associated annotations. Navigation is implemented via keyboard shortcuts (specific bindings unknown from documentation) that iterate through image file list derived from YAML configuration. State is preserved in the sidebar panel during the VS Code session.","intents":["Quickly browse through dataset images to spot-check annotation quality across the full dataset","Navigate to specific images during dataset review without manually opening files","Iterate through train/val splits to verify dataset composition"],"best_for":["Dataset curators performing manual QA on large image collections","ML engineers reviewing dataset splits before training","Researchers analyzing annotation patterns across datasets"],"limitations":["Navigation order determined by file system order — no sorting by class, confidence, or custom criteria","No jump-to-image functionality — must navigate sequentially, slow for large datasets (>10k images)","Keyboard shortcuts not documented — users must discover bindings through trial or extension settings","No bookmarking or flagging of problematic images during review"],"requires":["VS Code 1.85.0 or higher","YOLO dataset YAML configuration loaded in sidebar panel","Image files must be accessible in workspace"],"input_types":["Keyboard input (previous/next navigation commands)","YAML configuration defining image file list"],"output_types":["Current image displayed in preview panel","Associated annotations rendered as overlays","Image index/count indicator (unknown if implemented)"],"categories":["automation-workflow","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-andaoai-yolo-labeling-vs__cap_3","uri":"capability://data.processing.analysis.multi.format.yolo.annotation.format.support.detection.segmentation.pose.obb","name":"multi-format yolo annotation format support (detection, segmentation, pose, obb)","description":"Supports parsing and rendering of multiple YOLO annotation formats through format-specific parsers: COCO8/COCO128 for object detection (bounding boxes), COCO8-seg for instance segmentation (polygon masks), COCO8-pose and Tiger-pose for keypoint detection (joint coordinates), and DOTA8 for oriented bounding boxes (OBB). Each format has dedicated parsing logic to extract coordinates, class IDs, and metadata from YAML/annotation files and render them appropriately. Format detection occurs automatically based on YAML configuration structure.","intents":["Work with detection, segmentation, and pose datasets in a single tool without format conversion","Validate that dataset format matches intended YOLO model task (detection vs. segmentation vs. pose)","Review oriented bounding boxes for aerial/rotated object detection (DOTA format)"],"best_for":["Multi-task computer vision teams using different YOLO model variants (YOLOv8 detection, YOLOv8-seg, YOLOv8-pose)","Researchers working with specialized datasets (aerial imagery with DOTA, pose estimation with Tiger-pose)","Dataset curators managing multiple annotation types across projects"],"limitations":["Classification formats (MNIST160, ImageNet-10) explicitly not supported and not planned","Multi-object tracking (VisDrone) not supported — no temporal annotation handling","Format auto-detection relies on YAML structure — ambiguous configs may fail silently","No format conversion utilities — users must provide data in supported formats","OBB (DOTA8) rendering may have performance issues with high-density rotated boxes"],"requires":["VS Code 1.85.0 or higher","YOLO dataset in one of: COCO8, COCO128, COCO8-seg, COCO8-pose, Tiger-pose, DOTA8 format","YAML configuration file with proper format specification"],"input_types":["YOLO-format YAML configuration files","Annotation data in format-specific structure (bounding boxes, masks, keypoints, OBB coordinates)"],"output_types":["Rendered annotations matching format type (boxes, polygons, points, rotated boxes)","Class labels and metadata"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-andaoai-yolo-labeling-vs__cap_4","uri":"capability://data.processing.analysis.annotation.editing.and.modification.within.vs.code.sidebar","name":"annotation editing and modification within vs code sidebar","description":"Allows users to edit existing YOLO annotations (bounding box coordinates, class labels, segmentation masks) directly in the extension's sidebar panel without leaving VS Code or using external tools. Editing mechanism unknown from documentation — likely involves text input fields or direct coordinate manipulation. Changes are written back to YAML/annotation files in the workspace, maintaining file system consistency.","intents":["Fix incorrect bounding box coordinates discovered during review without opening external tools","Correct misclassified objects by changing class labels in-place","Adjust segmentation masks or keypoints for minor annotation errors"],"best_for":["Dataset curators performing rapid annotation corrections during QA","ML engineers fixing annotation errors discovered during model training","Teams using VS Code as single source of truth for dataset management"],"limitations":["Editing UI/UX not documented — unclear if coordinate input is text-based, graphical, or slider-based","No undo/redo functionality documented — edits may be permanent without recovery","No validation of edited coordinates — invalid values may corrupt annotation files","No batch editing — must edit annotations one-by-one","No conflict resolution if multiple users edit same annotation file simultaneously","Changes written directly to files — no staging or review workflow"],"requires":["VS Code 1.85.0 or higher","Write permissions to YAML/annotation files in workspace","Annotation file must be in supported format"],"input_types":["User input (coordinate values, class labels, mask points)","Existing annotation data from YAML files"],"output_types":["Modified YAML/annotation files","Updated rendered annotations in preview"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-andaoai-yolo-labeling-vs__cap_5","uri":"capability://data.processing.analysis.yolo.dataset.configuration.validation.and.file.association","name":"yolo dataset configuration validation and file association","description":"Automatically detects YOLO-format YAML configuration files in VS Code workspace and establishes associations with referenced image files and annotation data. The extension validates that YAML structure conforms to YOLO format expectations (required fields: path, train, val, nc, names) and that referenced image files exist in the workspace. Validation occurs on file open or via right-click context menu trigger. Invalid configurations are flagged (mechanism unknown — likely error messages or visual indicators).","intents":["Verify YOLO dataset YAML configuration is correctly formatted before training","Identify missing image files or broken path references in dataset configs","Ensure dataset structure matches YOLO model expectations (class count, split definitions)"],"best_for":["ML engineers setting up YOLO datasets for training","Dataset curators validating dataset structure before distribution","Teams migrating datasets from other formats to YOLO"],"limitations":["Validation logic not documented — unclear what checks are performed","No schema validation against official YOLO spec — custom validation rules unknown","Error messages and recovery suggestions not documented","No automatic path correction — users must manually fix broken references","Validation only checks file existence — does not verify image format or integrity","No validation of class names or label consistency across dataset"],"requires":["VS Code 1.85.0 or higher","YOLO-format YAML file in workspace","Image files referenced in YAML must be accessible in workspace"],"input_types":["YAML configuration file","File system (for checking image file existence)"],"output_types":["Validation status (success/error)","Error messages or warnings (format unknown)","File associations established for image loading"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-andaoai-yolo-labeling-vs__cap_6","uri":"capability://image.visual.class.label.visualization.and.management","name":"class label visualization and management","description":"Displays class names and IDs from YOLO dataset configuration (defined in YAML 'names' field) and associates them with rendered annotations. Each annotation overlay includes class label text color-coded or labeled by class ID. The extension reads class definitions from YAML and maintains a mapping between numeric class IDs in annotation data and human-readable class names for display.","intents":["Quickly identify object classes in annotations by reading class labels on rendered overlays","Verify that class definitions in YAML match the actual classes used in annotations","Review class distribution across dataset by observing label frequency during browsing"],"best_for":["Dataset curators verifying class consistency across annotations","ML engineers reviewing class definitions before model training","Teams with large class sets (>50 classes) needing visual class reference"],"limitations":["Class labels displayed as text overlays — no color-coding scheme documented","No class filtering — all classes rendered regardless of user interest","No class statistics (count, distribution) — only visual display","Class name length not constrained — long names may overlap with annotations","No class hierarchy or grouping support — flat list only"],"requires":["VS Code 1.85.0 or higher","YOLO YAML configuration with 'names' field defining class labels","Annotation data with valid class IDs matching defined classes"],"input_types":["YAML 'names' field (list of class labels)","Annotation class IDs"],"output_types":["Class label text overlaid on annotations","Class ID to name mapping"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":34,"verified":false,"data_access_risk":"high","permissions":["VS Code 1.85.0 or higher","YOLO-format YAML configuration file in workspace","Image files referenced in YAML must exist in workspace or accessible via relative paths","Supported dataset format (COCO8, COCO128, COCO8-seg, COCO8-pose, Tiger-pose, DOTA8)","Supported annotation format (COCO8, COCO128, COCO8-seg, COCO8-pose, Tiger-pose, DOTA8)","Valid YOLO-format annotation data in YAML or associated text files","YOLO dataset YAML configuration loaded in sidebar panel","Image files must be accessible in workspace","YOLO dataset in one of: COCO8, COCO128, COCO8-seg, COCO8-pose, Tiger-pose, DOTA8 format","YAML configuration file with proper format specification"],"failure_modes":["Requires YAML files to be properly formatted and image paths to be relative or absolute within workspace — malformed configs will fail silently","No validation of YAML schema — incorrect field names or structure won't trigger warnings","Performance with large datasets (>1000 images) unknown — no documented optimization for batch loading","Image loading speed depends on disk I/O and image file sizes; no caching mechanism documented","Overlay rendering performance degrades with high annotation density (>100 boxes per image) — no documented optimization","No interactive editing of bounding boxes — read-only visualization only","Mask rendering limited to supported formats (COCO8-seg, COCO8-pose, DOTA8) — other segmentation formats not planned","No confidence score filtering — all annotations rendered regardless of detection confidence","Navigation order determined by file system order — no sorting by class, confidence, or custom criteria","No jump-to-image functionality — must navigate sequentially, slow for large datasets (>10k images)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.32,"quality":0.24,"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":"2026-05-24T12:16:34.118Z","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=yolo-labeling","compare_url":"https://unfragile.ai/compare?artifact=yolo-labeling"}},"signature":"t7bjpkYniVUejyv+HQmum/RQc85seb65TseHBs8ohrkDOk/f/lwThbB5PZ3gq8sSzZnHkhV+emvAe45owH8gCQ==","signedAt":"2026-06-22T03:56:41.114Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/yolo-labeling","artifact":"https://unfragile.ai/yolo-labeling","verify":"https://unfragile.ai/api/v1/verify?slug=yolo-labeling","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"}}