{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"supervisely","slug":"supervisely","name":"Supervisely","type":"platform","url":"https://supervisely.com","page_url":"https://unfragile.ai/supervisely","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"supervisely__cap_0","uri":"capability://data.processing.analysis.multi.modal.dataset.annotation.with.ai.assisted.labeling","name":"multi-modal dataset annotation with ai-assisted labeling","description":"Provides collaborative annotation tools for images, videos, point clouds, and DICOM medical data with built-in AI models (YOLOv11, RT-DETRv2, SAM2, ClickSEG) that generate automatic annotations to accelerate manual labeling workflows. Uses smart tool request quotas (500/day community, 5,000/day pro, unlimited for image max tier) to meter AI-assisted suggestions, reducing annotation time while maintaining human quality control through review workflows.","intents":["I need to label a large dataset of images/videos with bounding boxes, polygons, and segmentation masks while leveraging AI to auto-generate initial annotations","I want to annotate 3D point cloud data or medical DICOM scans with collaborative team review and version control","I need to set up nested ontologies and key-value tags for flexible, structured labeling across multi-modal data"],"best_for":["computer vision teams building training datasets for object detection, segmentation, or tracking","medical imaging teams requiring HIPAA-compliant annotation with 3D volumetric support","autonomous vehicle/robotics teams managing multi-view video and point cloud annotation"],"limitations":["Smart tool requests are rate-limited by tier; community tier capped at 500 requests/day, requiring upgrade for high-volume auto-labeling workflows","Video and point cloud file limits (50 files without paid add-ons) constrain large-scale video annotation projects","No documented support for custom model format imports; limited to built-in model zoo (YOLOv11, RT-DETRv2, SAM2, ClickSEG)","Annotation quality assurance relies on manual review; no automated consensus or inter-annotator agreement metrics documented"],"requires":["Supervisely account (free community tier or paid pro/enterprise)","Supported data format (PNG/JPEG for images, MP4/WebM for video, LAS/LAZ for point clouds, DICOM for medical)","Web browser with WebGL support for 3D point cloud visualization","For medical 3D: Medical Max add-on (€149/month) or Enterprise plan"],"input_types":["image (multi-spectral, 64-bit depth, ultra-wide angles)","video (low-FPS, high-resolution, multi-view)","point cloud (LiDAR/RADAR, LAS/LAZ format)","DICOM medical imagery (2D/3D volumetric)"],"output_types":["annotation project (labeled dataset with metadata)","structured labels (bounding boxes, polygons, masks, keypoints, cuboids)","ontology definitions (nested classes, attributes, key-value tags)"],"categories":["data-processing-analysis","collaborative-annotation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"supervisely__cap_1","uri":"capability://automation.workflow.collaborative.team.annotation.with.role.based.access.and.quality.assurance.workflows","name":"collaborative team annotation with role-based access and quality assurance workflows","description":"Enables multiple team members to annotate the same dataset concurrently with role-based permissions (annotator, reviewer, admin), version control for annotation changes, and quality assurance workflows that route annotations through review and approval stages. Tracks annotation history and supports nested ontologies with key-value tags for flexible metadata assignment across team members.","intents":["I need to distribute annotation work across a team with different permission levels (annotators, reviewers, admins) and track who labeled what","I want to implement a QA workflow where annotations are reviewed and approved before being added to the training dataset","I need to manage annotation conflicts and maintain version history when multiple team members edit the same data"],"best_for":["distributed annotation teams (10-100+ annotators) requiring centralized project management","enterprises with compliance requirements (HIPAA, SOC2) needing audit trails and role-based access","organizations outsourcing annotation to labeling services while maintaining quality control"],"limitations":["No documented inter-annotator agreement metrics or consensus algorithms; quality assurance relies on manual review","Concurrent user limits and real-time collaboration performance not specified in documentation","Role-based access control granularity not detailed; unclear if custom roles or attribute-level permissions are supported","No built-in annotation conflict resolution or merge strategies documented"],"requires":["Supervisely pro or enterprise plan (community tier supports limited collaboration)","Team members with Supervisely accounts","Project admin permissions to configure roles and workflows"],"input_types":["annotation project (existing or new)","team member list with role assignments","workflow configuration (review stages, approval rules)"],"output_types":["annotated dataset with version history","audit log (who annotated what, when, changes made)","approved annotations ready for model training"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"supervisely__cap_10","uri":"capability://automation.workflow.professional.annotation.services.and.consulting","name":"professional annotation services and consulting","description":"Offers managed annotation services where Supervisely's team or certified partners handle annotation work on behalf of customers. Provides consulting services for dataset strategy, annotation workflow design, and ML pipeline optimization. Combines platform capabilities with human expertise to accelerate dataset creation and reduce time-to-model for customers without in-house annotation capacity.","intents":["I need to outsource annotation work to a trusted provider while maintaining quality control and data security","I want consulting advice on annotation strategy, dataset design, and ML workflow optimization","I need to scale annotation capacity quickly without hiring and training annotation teams"],"best_for":["startups and small teams without annotation capacity or expertise","enterprises with large-scale annotation needs exceeding internal capacity","organizations requiring specialized domain expertise (medical, geospatial, etc.)"],"limitations":["Pricing and service level agreements (SLAs) for annotation services not documented","Turnaround time, quality guarantees, and revision policies not specified","Data security and confidentiality agreements for outsourced annotation not detailed","Availability and capacity constraints not documented; unclear if services available globally","Consulting service scope and pricing not documented"],"requires":["Supervisely account (pro or enterprise)","Raw data (images, videos, point clouds, DICOM) to be annotated","Annotation specification (classes, attributes, guidelines)","Data sharing agreement and confidentiality terms"],"input_types":["raw dataset (images, videos, point clouds, DICOM)","annotation specification (classes, attributes, labeling guidelines)","quality requirements and acceptance criteria"],"output_types":["annotated dataset (ready for training)","annotation report (coverage, quality metrics)","consulting recommendations (strategy, workflow optimization)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"supervisely__cap_11","uri":"capability://tool.use.integration.ecosystem.index.and.app.marketplace.for.extensions","name":"ecosystem index and app marketplace for extensions","description":"Provides an ecosystem index of custom applications and extensions built by Supervisely and partners. Enables discovery and deployment of pre-built applications for specialized annotation tasks, model training, and workflow automation. Marketplace approach allows community and partner contributions, though specific app categories, discovery mechanisms, and installation process not documented in available materials.","intents":["I want to discover and install pre-built applications for specialized annotation tasks without building from scratch","I need to find community-contributed extensions for domain-specific workflows (medical, geospatial, etc.)","I want to share my custom applications with other Supervisely users"],"best_for":["teams seeking pre-built solutions for common annotation tasks","developers wanting to share custom applications with the community","organizations looking to reduce development effort for custom workflows"],"limitations":["Ecosystem index and marketplace details not documented; unclear if public or private","App discovery, rating, and review mechanisms not specified","App quality standards, security vetting, and support SLAs not documented","No documented community contribution guidelines or revenue sharing model","App installation and dependency management process not detailed","No public app count or community contribution metrics available"],"requires":["Supervisely account (free or paid)","Access to ecosystem index (URL or in-platform discovery)","For app development: Supervisely SDK and Python 3.9+"],"input_types":["app search query or category","custom application code (for contribution)"],"output_types":["discovered applications (with descriptions, ratings, reviews)","installed application (deployed in Supervisely)","published application (shared with community)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"supervisely__cap_12","uri":"capability://search.retrieval.search.and.filtering.across.datasets.with.semantic.and.metadata.queries","name":"search and filtering across datasets with semantic and metadata queries","description":"Provides search capabilities across images, annotations, and metadata using both keyword search (filename, class name) and semantic search (find similar images based on visual content). Supports filtering by annotation properties (class, confidence, annotator, date), metadata tags, and custom attributes. Search results can be exported as new datasets or used to create subsets for targeted annotation or analysis. Semantic search uses embeddings (model unknown) to find visually similar images.","intents":["I want to find all images with 'car' annotations that were labeled by annotator 'john' in the last week","I need to find images visually similar to a reference image to identify potential annotation errors or data drift","I want to export all images with low-confidence predictions to a new dataset for re-annotation"],"best_for":["Teams managing large datasets (10,000+ images) needing to find specific subsets","Data quality teams identifying annotation errors or data drift","Researchers analyzing dataset composition and bias"],"limitations":["Semantic search model is unknown; no documented embedding method or similarity metric","Search performance on large datasets (100,000+ images) is unknown; no documented indexing strategy","Complex boolean queries are not documented; appears to support simple AND/OR filters only","No integration with external search tools (Elasticsearch, Solr); search is Supervisely-only"],"requires":["Supervisely Pro or Enterprise tier (€199+/month)","Indexed dataset (automatic indexing on upload, latency unknown)"],"input_types":["Search query (keyword, semantic, or filter-based)","Filter criteria (class, annotator, date range, confidence threshold)"],"output_types":["Search results (list of matching images with annotations)","Result statistics (count, class distribution, annotator breakdown)","Exported dataset (new dataset containing search results)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"supervisely__cap_13","uri":"capability://automation.workflow.collaborative.real.time.annotation.with.conflict.detection.and.resolution","name":"collaborative real-time annotation with conflict detection and resolution","description":"Enables multiple annotators to work on the same image simultaneously with real-time synchronization of changes. Detects conflicts when two annotators modify the same annotation and flags them for resolution. Supports undo/redo with conflict awareness (undo by one user doesn't affect another user's changes). Annotation state is persisted to the server after each change, ensuring no data loss. Latency and conflict resolution strategy are unknown.","intents":["I want my team of 5 annotators to label the same image simultaneously and see each other's changes in real-time","I need the system to detect when two annotators modify the same bounding box and flag it for review","I want to undo my changes without affecting my teammate's annotations on the same image"],"best_for":["Teams with multiple annotators working on the same images (e.g., consensus labeling)","High-throughput annotation workflows where parallelization is critical","Organizations requiring real-time collaboration and conflict detection"],"limitations":["Real-time synchronization latency is unknown; no documented update frequency or network requirements","Conflict resolution is manual; no automatic adjudication based on annotator history or model confidence","Undo/redo behavior with conflicts is not documented; unclear how conflicts are handled across undo operations","No documented limits on concurrent annotators per image"],"requires":["Supervisely Pro or Enterprise tier (€199+/month)","Web browser with WebSocket support for real-time updates","Stable network connection (latency requirements unknown)"],"input_types":["Annotation changes (geometry, class, attributes)","User identity (for conflict detection and audit logging)"],"output_types":["Real-time annotation updates (synchronized across all users)","Conflict notifications (when two users modify same annotation)","Audit logs (who made what changes when)"],"categories":["automation-workflow","collaboration-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"supervisely__cap_2","uri":"capability://planning.reasoning.neural.network.training.with.built.in.model.zoo.and.custom.model.integration","name":"neural network training with built-in model zoo and custom model integration","description":"Provides integrated neural network training capabilities using built-in models (YOLOv11, RT-DETRv2, MM Segmentation, SAM2, ClickSEG) with support for custom model integration via SDK. Abstracts training infrastructure and hyperparameter configuration, allowing users to train models directly on annotated datasets without managing compute resources or writing training code. Custom models can be integrated for auto-labeling workflows, enabling iterative dataset improvement.","intents":["I want to train a YOLOv11 or RT-DETRv2 object detection model on my annotated dataset without writing training code or managing GPU infrastructure","I need to integrate my custom PyTorch/TensorFlow model into Supervisely for auto-labeling to accelerate annotation workflows","I want to compare multiple model architectures (YOLOv11 vs RT-DETRv2) on the same dataset to select the best performer"],"best_for":["computer vision teams without ML infrastructure expertise who need to train models quickly","annotation teams wanting to close the loop with auto-labeling using trained models","enterprises with custom models requiring integration into annotation workflows"],"limitations":["Training hardware specifications (GPU types, memory, training time estimates) not documented; unclear if distributed training or multi-GPU support available","Model zoo limited to 5 architectures (YOLOv11, RT-DETRv2, MM Segmentation, SAM2, ClickSEG); no custom architecture support or transfer learning from external checkpoints documented","No model versioning, experiment tracking, or hyperparameter comparison tools documented (unlike MLflow or Weights & Biases)","Custom model format support unclear; integration method via SDK not detailed in available documentation","No batch prediction service or inference API documented; models appear designed for annotation workflows only"],"requires":["Supervisely pro or enterprise plan","Annotated dataset with sufficient samples (minimum size not specified)","For custom models: Supervisely SDK and Python 3.9+","API key for Supervisely account"],"input_types":["annotated dataset (images with labels, bounding boxes, masks, keypoints)","model architecture selection (YOLOv11, RT-DETRv2, etc.)","hyperparameter configuration (learning rate, batch size, epochs)","custom model code (for SDK-based integration)"],"output_types":["trained model checkpoint (weights, architecture definition)","training metrics (loss, accuracy, mAP, per-class performance)","model artifact (for deployment or auto-labeling)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"supervisely__cap_3","uri":"capability://data.processing.analysis.dataset.management.with.versioning.archival.and.export","name":"dataset management with versioning, archival, and export","description":"Manages annotation projects with version control, data retention policies, and export capabilities. Community tier archives inactive projects after 30 days (available as download), while pro/enterprise tiers offer unlimited retention. Supports downloading archived projects and exporting datasets in standard formats, though export completeness and supported formats not fully documented. Provides storage quotas (5GB community, 50GB pro, expandable at €40/100GB) with file limits (10,000 community, 50,000 pro, expandable via add-ons).","intents":["I need to manage multiple annotation projects with version control and track changes over time","I want to archive old datasets and export them for backup or use in external tools","I need to understand my storage usage and plan capacity for growing datasets"],"best_for":["teams managing multiple annotation projects with long-term retention requirements","organizations requiring data portability and backup strategies","enterprises with compliance needs for data archival and audit trails"],"limitations":["Community tier enforces 30-day inactivity archival; projects must be re-downloaded to restore, creating friction for long-term projects","Export format and completeness not documented; unclear if all annotation types (3D, DICOM) are fully exportable","Storage expansion pricing (€40/100GB) becomes expensive at scale; no volume discounts documented","File limits without add-ons (10,000 community, 50,000 pro) constrain large-scale projects; add-ons required for video/medical/3D (€99-€399/month)","No documented backup/disaster recovery SLA or data redundancy guarantees"],"requires":["Supervisely account (free community or paid pro/enterprise)","Sufficient storage quota for dataset size","Web browser for project management interface"],"input_types":["annotation project (images, videos, point clouds, DICOM)","storage quota configuration","retention policy settings"],"output_types":["archived project (downloadable)","exported dataset (format unspecified in documentation)","storage usage report"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"supervisely__cap_4","uri":"capability://tool.use.integration.custom.application.development.and.deployment.via.appengine","name":"custom application development and deployment via appengine","description":"Enables developers to build custom labeling UIs and automation workflows using Supervisely SDK and deploy them via AppEngine. Custom applications can integrate with annotation projects, access datasets, and leverage built-in models for auto-labeling. AppEngine provides a runtime environment for interactive applications, though compute resources, scaling behavior, and deployment process details are not documented. Supports custom model integration for specialized labeling workflows.","intents":["I want to build a custom labeling UI for domain-specific annotation tasks (e.g., medical image analysis with custom tools)","I need to automate annotation workflows using custom logic (e.g., conditional labeling based on image properties)","I want to integrate my proprietary model into Supervisely for auto-labeling without modifying the core platform"],"best_for":["developers building specialized annotation tools for niche domains (medical, geospatial, industrial)","teams with custom labeling logic that doesn't fit built-in annotation tools","enterprises integrating Supervisely into larger ML workflows"],"limitations":["AppEngine compute resources, scaling behavior, and performance characteristics not documented; unclear if auto-scaling or resource limits apply","Deployment process and CI/CD integration not detailed; no documentation on versioning or rollback","SDK capabilities and API surface not fully documented in available materials; custom model format support unclear","No public marketplace or community app registry mentioned; custom apps appear to be private to organizations","Cold start latency and concurrent execution limits unknown"],"requires":["Supervisely SDK (Python 3.9+)","Supervisely account with API key","Developer knowledge of Supervisely API and annotation data structures","Custom application code (Python-based, likely)"],"input_types":["custom application code (Python SDK-based)","annotation project data (images, labels, metadata)","custom model weights or inference code"],"output_types":["deployed custom application (accessible within Supervisely UI)","generated annotations (from custom logic or models)","custom UI components (for specialized labeling)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"supervisely__cap_5","uri":"capability://data.processing.analysis.hipaa.compliant.medical.imaging.annotation.with.3d.volumetric.support","name":"hipaa-compliant medical imaging annotation with 3d volumetric support","description":"Provides HIPAA-compliant annotation tools for DICOM medical imagery with 3D volumetric labeling, anonymization features, and compliance certifications. Medical Max add-on (€149/month) unlocks 3D medical labeling capabilities, enabling annotation of volumetric CT/MRI scans with tools for segmentation, measurement, and keypoint marking. Platform claims privacy compliance ('data not shared or used') and supports on-prem deployment for enterprise customers requiring data residency.","intents":["I need to annotate 3D medical imaging data (CT, MRI) with volumetric segmentation and measurement tools while maintaining HIPAA compliance","I want to anonymize DICOM data before annotation to protect patient privacy","I need to deploy Supervisely on-premises to meet data residency requirements for regulated medical data"],"best_for":["medical imaging teams building datasets for diagnostic AI (radiology, pathology, oncology)","healthcare enterprises with strict data residency and compliance requirements","research institutions annotating clinical imaging studies"],"limitations":["Medical Max add-on (€149/month) required for 3D volumetric support; significant additional cost for medical teams","HIPAA compliance claims not independently verified; no SOC2 or ISO certification mentioned in documentation","Anonymization capabilities not detailed; unclear if DICOM tag stripping or re-identification risk assessment included","On-prem deployment available for enterprise only; no self-hosted community option","3D volumetric annotation performance and concurrent user limits not documented","No integration with PACS (Picture Archiving and Communication Systems) or EHR systems documented"],"requires":["Supervisely pro or enterprise plan","Medical Max add-on (€149/month) for 3D volumetric labeling","DICOM files in standard format","For on-prem: Enterprise plan and infrastructure to host Supervisely","HIPAA Business Associate Agreement (BAA) with Supervisely (not explicitly mentioned but implied)"],"input_types":["DICOM medical imagery (2D slices or 3D volumetric)","patient metadata (for anonymization)","annotation protocol (segmentation classes, measurement types)"],"output_types":["annotated DICOM dataset with labels","segmentation masks (3D volumetric)","measurement data (distances, volumes, areas)","anonymized dataset (DICOM with PHI removed)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"supervisely__cap_6","uri":"capability://data.processing.analysis.geospatial.and.multi.spectral.image.annotation","name":"geospatial and multi-spectral image annotation","description":"Supports annotation of multi-spectral imagery (beyond RGB), ultra-wide angle images, and high-depth 64-bit images for geospatial and remote sensing applications. Image Max add-on (€99/month) unlocks multi-spectral and medical 2D capabilities, enabling annotation of satellite imagery, aerial photography, and specialized imaging formats. Handles non-standard image dimensions and color spaces required for geospatial analysis.","intents":["I need to annotate multi-spectral satellite or aerial imagery for land use classification, change detection, or object detection","I want to label ultra-wide angle or high-depth images that don't fit standard RGB annotation tools","I need to manage large geospatial datasets with consistent annotation workflows across multiple image types"],"best_for":["geospatial and remote sensing teams building training datasets for satellite/aerial analysis","environmental monitoring organizations annotating multi-spectral imagery","agriculture and forestry teams using drone or satellite data"],"limitations":["Image Max add-on (€99/month) required for multi-spectral support; additional cost for geospatial teams","Supported multi-spectral formats and band counts not documented; unclear if arbitrary band counts supported","No documented integration with geospatial tools (QGIS, ArcGIS) or standard formats (GeoTIFF, NetCDF)","Ultra-wide angle and high-depth image handling not detailed; performance and visualization quality unknown","No built-in geospatial analysis tools (e.g., georeferencing, projection handling, spatial indexing)"],"requires":["Supervisely pro or enterprise plan","Image Max add-on (€99/month) for multi-spectral support","Multi-spectral image files in supported format (format list not documented)","Web browser with WebGL support for visualization"],"input_types":["multi-spectral imagery (satellite, aerial, drone)","ultra-wide angle images","high-depth 64-bit images","geospatial metadata (coordinates, projection, resolution)"],"output_types":["annotated multi-spectral dataset","segmentation masks (per-band or composite)","object detection labels (bounding boxes, polygons)","classification labels (land use, object type)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"supervisely__cap_7","uri":"capability://data.processing.analysis.video.annotation.with.multi.view.and.tracking.support","name":"video annotation with multi-view and tracking support","description":"Provides video annotation tools with frame-by-frame labeling, object tracking across frames, and multi-view video support for autonomous vehicle and robotics applications. Video Max add-on (€99/month) removes file limits (50 files without add-on) and enables advanced tracking features. Supports low-FPS and high-resolution video, with tracking algorithms to propagate labels across frames, reducing manual annotation effort for video sequences.","intents":["I need to annotate video sequences with bounding boxes or segmentation masks that track objects across frames","I want to label multi-view video from multiple camera angles simultaneously for autonomous vehicle datasets","I need to annotate high-resolution or low-FPS video without hitting file limits"],"best_for":["autonomous vehicle teams building perception datasets with multi-camera video","robotics teams annotating video for manipulation and navigation tasks","action recognition and activity detection teams"],"limitations":["Video Max add-on (€99/month) required to remove 50-file limit; significant cost for large-scale video projects","Tracking algorithm details not documented; unclear if manual tracking, semi-automatic, or fully automatic","Multi-view synchronization and temporal alignment not detailed; unclear if frame-level sync or timestamp-based","Video codec support and compression handling not documented; performance on high-resolution video unknown","No documented support for video streaming or real-time annotation; appears to require pre-recorded video files","Tracking accuracy and failure modes not documented"],"requires":["Supervisely pro or enterprise plan","Video Max add-on (€99/month) for advanced tracking and file limit removal","Video files in supported format (MP4, WebM, or other; full list not documented)","For multi-view: Multiple synchronized video files with timestamp alignment"],"input_types":["video files (MP4, WebM, or other formats)","frame rate and resolution specifications","multi-view video sequences (multiple camera angles)","tracking initialization (bounding boxes or masks for first frame)"],"output_types":["annotated video (frame-by-frame labels)","tracking trajectories (object positions across frames)","multi-view annotations (synchronized across camera angles)","video dataset (exportable for training)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"supervisely__cap_8","uri":"capability://data.processing.analysis.3d.point.cloud.annotation.with.lidar.radar.support","name":"3d point cloud annotation with lidar/radar support","description":"Provides 3D point cloud annotation tools for LiDAR and RADAR data with support for cuboid, polygon, and keypoint labeling in 3D space. Cloud Points Max add-on (€399/month) enables point cloud annotation and removes 50-file limit. Supports LAS/LAZ format point clouds with visualization and labeling tools for autonomous vehicle, robotics, and geospatial applications. Handles large point clouds with efficient rendering and multi-frame sequences.","intents":["I need to annotate 3D point clouds from LiDAR sensors with 3D bounding boxes for object detection","I want to label RADAR point clouds for autonomous vehicle perception datasets","I need to annotate multi-frame point cloud sequences with tracking across frames"],"best_for":["autonomous vehicle teams building 3D perception datasets with LiDAR/RADAR","robotics teams annotating 3D sensor data for manipulation and navigation","geospatial teams analyzing LiDAR point clouds for terrain classification"],"limitations":["Cloud Points Max add-on (€399/month) is expensive; significant cost barrier for point cloud annotation projects","Point cloud file limits (50 without add-on) constrain large-scale projects; add-on required for scaling","Point cloud size limits and performance characteristics not documented; unclear if sparse or dense clouds supported","RADAR-specific annotation tools and data format support not detailed","Multi-frame point cloud tracking and temporal alignment not documented","No documented integration with point cloud processing libraries (PCL, Open3D) or standard formats beyond LAS/LAZ"],"requires":["Supervisely pro or enterprise plan","Cloud Points Max add-on (€399/month) for point cloud annotation","Point cloud files in LAS/LAZ format","Web browser with WebGL support for 3D visualization","For RADAR: RADAR data in supported format (format not documented)"],"input_types":["point cloud files (LAS/LAZ format)","RADAR point cloud data (format unspecified)","multi-frame point cloud sequences","camera calibration data (for multi-modal annotation)"],"output_types":["3D bounding box annotations","polygon segmentation (3D)","keypoint labels (3D coordinates)","point cloud dataset with labels"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"supervisely__cap_9","uri":"capability://tool.use.integration.api.and.sdk.automation.for.annotation.workflows","name":"api and sdk automation for annotation workflows","description":"Provides REST API and Python SDK for programmatic access to annotation projects, datasets, and models. Enables automation of annotation workflows, dataset management, and model training through code. API and SDK allow integration with external tools and CI/CD pipelines, though specific API endpoints, rate limits, and SDK capabilities are not documented in available materials. Supports custom automation scripts for batch operations and workflow orchestration.","intents":["I want to automate dataset management tasks (upload, organize, export) using scripts or CI/CD pipelines","I need to integrate Supervisely with external tools (data pipelines, model registries, experiment tracking) via API","I want to build custom automation workflows that combine annotation, training, and deployment"],"best_for":["ML engineers building end-to-end annotation-to-training pipelines","teams integrating Supervisely into larger ML infrastructure","organizations automating repetitive annotation and dataset management tasks"],"limitations":["API endpoints, rate limits, and quota details not documented; unclear if rate limiting or throttling applied","SDK capabilities and supported operations not fully detailed in available materials","No documented CI/CD integration examples or best practices","Authentication mechanism and API key management not detailed","Webhook support for event-driven automation not mentioned","No documented SDK for languages other than Python"],"requires":["Supervisely account with API key","Python 3.9+ for SDK usage","Developer knowledge of Supervisely data structures and API","Network access to Supervisely API endpoints"],"input_types":["API requests (REST endpoints)","SDK method calls (Python)","authentication credentials (API key)","dataset or project identifiers"],"output_types":["API responses (JSON)","SDK return values (Python objects)","automation results (datasets created, models trained, etc.)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"supervisely__headline","uri":"capability://image.visual.computer.vision.platform.for.collaborative.annotation.and.model.training","name":"computer vision platform for collaborative annotation and model training","description":"A comprehensive computer vision platform that enables teams to collaboratively annotate datasets, train neural networks, and manage MLOps for images, videos, and more, tailored for enterprise use.","intents":["best computer vision platform","computer vision tools for dataset annotation","AI model training platform for images","MLOps automation for computer vision","collaborative annotation tools for AI projects"],"best_for":["enterprise teams","researchers in computer vision"],"limitations":[],"requires":[],"input_types":["images","videos","point clouds","DICOM formats"],"output_types":[],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":56,"verified":false,"data_access_risk":"high","permissions":["Supervisely account (free community tier or paid pro/enterprise)","Supported data format (PNG/JPEG for images, MP4/WebM for video, LAS/LAZ for point clouds, DICOM for medical)","Web browser with WebGL support for 3D point cloud visualization","For medical 3D: Medical Max add-on (€149/month) or Enterprise plan","Supervisely pro or enterprise plan (community tier supports limited collaboration)","Team members with Supervisely accounts","Project admin permissions to configure roles and workflows","Supervisely account (pro or enterprise)","Raw data (images, videos, point clouds, DICOM) to be annotated","Annotation specification (classes, attributes, guidelines)"],"failure_modes":["Smart tool requests are rate-limited by tier; community tier capped at 500 requests/day, requiring upgrade for high-volume auto-labeling workflows","Video and point cloud file limits (50 files without paid add-ons) constrain large-scale video annotation projects","No documented support for custom model format imports; limited to built-in model zoo (YOLOv11, RT-DETRv2, SAM2, ClickSEG)","Annotation quality assurance relies on manual review; no automated consensus or inter-annotator agreement metrics documented","No documented inter-annotator agreement metrics or consensus algorithms; quality assurance relies on manual review","Concurrent user limits and real-time collaboration performance not specified in documentation","Role-based access control granularity not detailed; unclear if custom roles or attribute-level permissions are supported","No built-in annotation conflict resolution or merge strategies documented","Pricing and service level agreements (SLAs) for annotation services not documented","Turnaround time, quality guarantees, and revision policies not specified","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.15,"match_graph":0.25,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:28.696Z","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=supervisely","compare_url":"https://unfragile.ai/compare?artifact=supervisely"}},"signature":"SC1rHADx3wD2mrvGRG7DSl2w/CrBMH0SuSVfYPP2qxVxHmq1dQaWYCFFaPskvNAsbdRjF9yUb4TOuixqi6oKDQ==","signedAt":"2026-06-21T07:52:31.617Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/supervisely","artifact":"https://unfragile.ai/supervisely","verify":"https://unfragile.ai/api/v1/verify?slug=supervisely","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"}}