{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"cvat","slug":"cvat","name":"CVAT","type":"repo","url":"https://github.com/cvat-ai/cvat","page_url":"https://unfragile.ai/cvat","categories":["data-pipelines"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"cvat__cap_0","uri":"capability://data.processing.analysis.multi.format.dataset.import.and.export.with.datumaro.integration","name":"multi-format dataset import and export with datumaro integration","description":"Converts between 30+ annotation formats (COCO, YOLO, Pascal VOC, etc.) using the Datumaro library as a pluggable format registry. The system maintains a format registry (cvat/apps/dataset_manager/formats/registry.py) that dynamically loads importers and exporters, enabling lossless round-trip conversion of annotations across heterogeneous ML frameworks without manual format translation.","intents":["Import datasets from external sources in COCO or YOLO format and convert to CVAT's internal representation","Export annotated datasets in multiple formats for training with different ML frameworks","Migrate annotation projects between CVAT and competing tools without data loss","Build custom format adapters for proprietary or domain-specific annotation schemas"],"best_for":["ML teams working with multiple annotation tools in their pipeline","Data engineers building ETL workflows for computer vision datasets","Organizations migrating from legacy annotation systems to CVAT"],"limitations":["Format conversion may lose metadata not present in target schema (e.g., confidence scores in YOLO export)","Large dataset imports (>100k images) require background job processing and may timeout without proper worker configuration","Custom format plugins require Python development and restart of CVAT services to register"],"requires":["Datumaro library (included in CVAT dependencies)","Sufficient disk space for temporary format conversion buffers","PostgreSQL 15+ for metadata storage during import/export operations"],"input_types":["ZIP archives containing images and annotation files","Structured annotation files (JSON, XML, YAML)","Cloud storage paths (S3, Azure Blob, GCS) via cloud integration"],"output_types":["ZIP archives with images and annotations in target format","Structured annotation files in 30+ formats","Cloud storage uploads to configured buckets"],"categories":["data-processing-analysis","dataset-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cvat__cap_1","uri":"capability://automation.workflow.serverless.ai.assisted.auto.annotation.via.nuclio.function.orchestration","name":"serverless ai-assisted auto-annotation via nuclio function orchestration","description":"Integrates with Nuclio serverless framework to deploy and invoke custom AI models for automatic annotation. CVAT manages model lifecycle (upload, versioning, deployment) and provides a task-level interface to trigger inference jobs that process images/frames and generate annotations. Models run in isolated Nuclio containers with configurable resource limits, enabling on-demand scaling without dedicated GPU infrastructure.","intents":["Deploy custom object detection or segmentation models and run them on entire datasets without manual annotation","Reduce annotation time by 50-80% through automatic detection followed by human review","Experiment with different model versions and compare annotation quality across versions","Integrate proprietary or fine-tuned models into the annotation workflow without modifying CVAT core"],"best_for":["Teams with pre-trained models seeking to accelerate annotation workflows","ML engineers building annotation pipelines with custom detection models","Organizations with GPU infrastructure wanting to leverage existing model investments"],"limitations":["Requires Nuclio cluster setup and configuration; not available in single-machine deployments without additional infrastructure","Model inference latency directly impacts annotation speed; large models (>1GB) may cause timeouts on standard hardware","No built-in model versioning or A/B testing framework; requires manual tracking of model performance across annotation batches","Auto-annotations require human review before acceptance; no automatic quality thresholding"],"requires":["Nuclio 1.0+ cluster (Kubernetes or Docker-based)","Model packaged as Docker container or Python function with Nuclio SDK","GPU resources if using deep learning models (NVIDIA CUDA 11.8+ recommended)","Network connectivity between CVAT backend and Nuclio cluster"],"input_types":["Images (JPEG, PNG, WebP)","Video frames (extracted or streamed)","Model weights in ONNX, TensorFlow, PyTorch, or custom formats"],"output_types":["Bounding boxes with confidence scores","Segmentation masks","Keypoints and skeleton annotations","Multi-class predictions with per-class confidence"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cvat__cap_10","uri":"capability://automation.workflow.background.job.processing.with.celery.task.queue.and.worker.scaling","name":"background job processing with celery task queue and worker scaling","description":"Offloads long-running operations (dataset import/export, model inference, video transcoding) to Celery task queue with Redis or Kvrocks backend. CVAT enqueues tasks asynchronously and returns immediately to the client, allowing the UI to remain responsive. Workers process tasks in parallel, with configurable concurrency and resource limits. Task status is tracked in PostgreSQL and exposed via WebSocket for real-time progress updates.","intents":["Import large datasets (100GB+) without blocking the UI or timing out HTTP requests","Run expensive operations (model inference, video transcoding) in parallel across multiple workers","Scale annotation capacity by adding more workers without modifying CVAT core","Provide real-time progress updates to users during long-running operations"],"best_for":["Deployments with large datasets or compute-intensive operations","Teams wanting to scale annotation capacity horizontally","Organizations requiring reliable job processing with retry logic"],"limitations":["Celery adds operational complexity; requires Redis/Kvrocks cluster and worker process management","Task failures are not automatically retried; requires explicit retry configuration per task type","No built-in task prioritization; all tasks are processed FIFO unless explicitly prioritized","Worker scaling is manual; no auto-scaling based on queue depth (requires external orchestration like Kubernetes)"],"requires":["Redis 7.2+ or Kvrocks 2.12.1+ for task queue backend","Celery 5.0+ for task execution framework","PostgreSQL 15+ for task status tracking","Worker processes (can run on same machine as CVAT or separate machines)"],"input_types":["Task definitions (operation type, parameters, resource requirements)","Task priority (optional)","Retry configuration (max retries, backoff strategy)"],"output_types":["Task status (pending, running, completed, failed)","Task progress (percentage complete, items processed)","Task results (output data, error messages)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cvat__cap_11","uri":"capability://image.visual.canvas.rendering.system.with.webgl.acceleration.and.real.time.annotation.editing","name":"canvas rendering system with webgl acceleration and real-time annotation editing","description":"Implements a high-performance canvas system (cvat-core) that renders images/videos and annotation primitives (bounding boxes, polygons, masks) using WebGL for GPU acceleration. The canvas supports real-time editing (drag, resize, rotate annotations) with sub-100ms latency, keyboard shortcuts for rapid annotation, and undo/redo stacks. Annotations are stored in Redux state on the frontend and synced to the backend via REST API, enabling offline editing with eventual consistency.","intents":["Annotate images/videos with minimal UI latency (<100ms per interaction)","Edit annotations in real-time (drag, resize, rotate) without server round-trips","Support keyboard-driven annotation workflows for power users","Enable offline annotation with automatic sync when connectivity is restored"],"best_for":["Annotators requiring high-speed annotation workflows (>100 objects per hour)","Teams with unreliable network connectivity (mobile networks, remote locations)","Organizations prioritizing annotator productivity and ergonomics"],"limitations":["WebGL rendering requires modern GPU; older machines or headless environments may have degraded performance","Large images (>4K resolution) may cause memory pressure on client; requires image tiling or downsampling","Offline editing can cause conflicts if multiple users edit the same task; requires manual conflict resolution","Undo/redo stacks are client-side only; closing the browser loses undo history"],"requires":["Modern browser with WebGL 2.0 support (Chrome 56+, Firefox 51+, Safari 15+)","GPU with sufficient VRAM for image rendering (1GB+ recommended)","React 18.2.0+ for frontend state management","Redux for annotation state management"],"input_types":["Images (JPEG, PNG, WebP) or video frames","Annotation primitives (bounding boxes, polygons, masks)","User interactions (mouse, keyboard, touch)"],"output_types":["Rendered images with annotations overlaid","Updated annotation coordinates and properties","Undo/redo history"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cvat__cap_12","uri":"capability://memory.knowledge.caching.layer.with.redis.and.kvrocks.for.session.and.job.state.management","name":"caching layer with redis and kvrocks for session and job state management","description":"Uses Redis 7.2+ and Kvrocks 2.12.1+ as distributed caching layers to reduce database load. Session data, job assignments, and frequently accessed metadata are cached in Redis with configurable TTLs. Kvrocks (Redis-compatible key-value store) provides persistent caching for larger datasets. Cache invalidation is event-driven; when annotations are updated, related cache entries are invalidated automatically.","intents":["Reduce database load by caching frequently accessed data (job assignments, user sessions)","Improve API response times for metadata queries (task lists, job status)","Enable horizontal scaling by sharing session state across multiple backend instances","Provide fast access to annotation state without database round-trips"],"best_for":["High-concurrency deployments (100+ concurrent users)","Teams with large datasets where database queries are slow","Organizations requiring horizontal scaling across multiple backend instances"],"limitations":["Cache invalidation is complex; bugs can lead to stale data being served","Redis is in-memory; cache loss on restart requires warm-up period","Cache key design is critical; poor key design leads to cache misses and wasted memory","No built-in cache coherence across distributed Redis instances; requires careful TTL tuning"],"requires":["Redis 7.2+ or Kvrocks 2.12.1+ cluster","Cache key design and TTL configuration","Event-driven cache invalidation logic in Django backend","Monitoring for cache hit rates and eviction rates"],"input_types":["Cache keys (derived from resource IDs and query parameters)","Cache values (serialized annotation state, session data)","TTL configuration (time-to-live for cache entries)"],"output_types":["Cached data (job assignments, session state, metadata)","Cache hit/miss metrics","Eviction events (when cache is full)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cvat__cap_13","uri":"capability://data.processing.analysis.analytics.and.event.tracking.with.clickhouse.time.series.database","name":"analytics and event tracking with clickhouse time-series database","description":"Logs all user actions (annotation events, API calls, state transitions) to ClickHouse 23.11, a columnar time-series database optimized for analytics. Events include timestamps, user IDs, action types, and resource IDs. ClickHouse enables fast aggregation queries (e.g., 'annotations per user per day') without impacting operational databases. Analytics dashboards query ClickHouse directly, providing real-time insights into annotation progress and team productivity.","intents":["Track annotation progress and identify bottlenecks in real-time","Measure team productivity (annotations per user per day, time per object)","Detect anomalies (sudden drop in annotation rate, unusual user behavior)","Generate compliance reports (audit trails, data access logs)"],"best_for":["Large annotation teams (10+ users) where productivity tracking is critical","Organizations requiring compliance reporting and audit trails","Teams wanting to optimize annotation workflows based on data"],"limitations":["ClickHouse is append-only; updating or deleting events requires special handling","Event schema is rigid; adding new event types requires schema migration","ClickHouse queries are optimized for aggregations; point lookups are slow","Operational overhead of maintaining separate analytics database"],"requires":["ClickHouse 23.11+ cluster","Event schema definition (table structure, column types)","Event logging middleware in Django backend","Analytics dashboard or BI tool (Grafana, Metabase, etc.)"],"input_types":["User action events (annotation created, task assigned, etc.)","API call logs (endpoint, user, timestamp, response time)","State transition events (job status changes)"],"output_types":["Aggregated analytics (annotations per user, time per object)","Time-series metrics (annotation rate over time)","Audit logs (who accessed what, when)","Anomaly alerts (unusual activity patterns)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cvat__cap_14","uri":"capability://automation.workflow.docker.compose.and.kubernetes.helm.deployment.with.multi.service.orchestration","name":"docker compose and kubernetes/helm deployment with multi-service orchestration","description":"Provides production-ready deployment configurations via Docker Compose (single-machine) and Kubernetes/Helm (distributed). The system is decomposed into microservices: frontend (React), backend (Django), database (PostgreSQL), cache (Redis/Kvrocks), analytics (ClickHouse), and workers (Celery). Helm charts define resource requests/limits, health checks, and auto-scaling policies. Deployment is declarative; infrastructure-as-code approach enables reproducible deployments across environments.","intents":["Deploy CVAT on a single machine for development/testing using Docker Compose","Scale CVAT to production across multiple machines using Kubernetes","Manage multiple CVAT instances (dev, staging, production) with consistent configuration","Enable CI/CD pipelines to automatically deploy CVAT updates"],"best_for":["DevOps teams managing CVAT deployments at scale","Organizations with Kubernetes infrastructure","Teams requiring reproducible, version-controlled deployments"],"limitations":["Docker Compose is single-machine; not suitable for high-availability deployments","Kubernetes requires operational expertise; steep learning curve for teams unfamiliar with K8s","Helm charts require tuning for specific environments (resource limits, storage classes, etc.)","Multi-service deployment increases operational complexity; debugging requires understanding service interactions"],"requires":["Docker 20.10+ for container runtime","Docker Compose 2.0+ for single-machine deployment","Kubernetes 1.24+ for distributed deployment","Helm 3.0+ for Kubernetes package management","Persistent storage (local volumes for Docker Compose, PVCs for Kubernetes)"],"input_types":["Docker Compose YAML files (service definitions, volumes, networks)","Kubernetes manifests or Helm values (resource requests, replicas, etc.)","Environment variables (database credentials, API keys)"],"output_types":["Running CVAT services (frontend, backend, workers, databases)","Logs from all services (aggregated via Docker or Kubernetes logging)","Metrics (CPU, memory, network usage per service)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cvat__cap_2","uri":"capability://image.visual.interactive.segmentation.with.segment.anything.model.sam.and.f.brs","name":"interactive segmentation with segment anything model (sam) and f-brs","description":"Provides client-side and server-side interactive segmentation tools that allow annotators to generate masks by clicking or drawing rough outlines. SAM (Segment Anything Model) runs server-side via Nuclio for high-quality zero-shot segmentation, while f-BRS (Fast Boundary Refinement Segmentation) offers lightweight interactive refinement. The canvas system captures user interactions (clicks, strokes) and sends them to the backend for mask generation, which is then rendered in real-time on the frontend.","intents":["Segment complex objects with irregular boundaries using only a few clicks instead of manual polygon drawing","Reduce segmentation annotation time from minutes per object to seconds using SAM's zero-shot capabilities","Refine automatically generated masks interactively without restarting the segmentation process","Enable non-expert annotators to produce high-quality segmentation masks with minimal training"],"best_for":["Teams annotating datasets with complex object boundaries (medical imaging, satellite imagery, product photography)","Projects with tight annotation deadlines where speed is critical","Organizations lacking domain expertise for manual segmentation"],"limitations":["SAM inference adds 2-5 second latency per click; not suitable for real-time annotation workflows","SAM may fail on small objects (<50 pixels) or objects with weak visual boundaries","f-BRS requires pre-trained weights; custom domain adaptation requires retraining","Interactive tools require WebSocket connection; latency increases significantly over high-latency networks (>100ms)"],"requires":["Nuclio cluster with GPU support (NVIDIA A100 or RTX 4090 recommended for <2s inference)","SAM model weights (ViT-H checkpoint ~2.5GB)","WebSocket support in network infrastructure (no HTTP/2 proxies that block upgrades)","Modern browser with WebGL support for real-time mask rendering"],"input_types":["Images (JPEG, PNG, WebP)","User interaction events (mouse clicks, stroke coordinates)","Bounding box hints (optional, improves SAM accuracy)"],"output_types":["Binary segmentation masks (PNG format)","Polygon approximations of masks","Confidence maps showing SAM's uncertainty"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cvat__cap_3","uri":"capability://automation.workflow.multi.user.collaborative.annotation.with.job.assignment.and.stage.tracking","name":"multi-user collaborative annotation with job assignment and stage tracking","description":"Implements a hierarchical workflow (Organization → Project → Task → Job) where tasks are subdivided into jobs assigned to individual annotators. The system tracks job state (annotation, validation, review) using a state machine, maintains per-user progress metrics, and enforces role-based access control via Open Policy Agent (OPA). Redis caches job assignments and user activity to minimize database load during concurrent annotation sessions.","intents":["Distribute large annotation tasks across teams without conflicts or duplicate work","Track annotation progress and identify bottlenecks in the workflow","Enforce quality gates by requiring validation and review stages before task completion","Manage permissions so annotators can only access assigned jobs and projects"],"best_for":["Teams of 5+ annotators working on shared datasets","Organizations requiring audit trails and quality control workflows","Projects with strict data governance requirements (healthcare, autonomous vehicles)"],"limitations":["Job reassignment requires manual intervention; no automatic load balancing if an annotator falls behind","State machine is rigid (annotation → validation → review); custom workflows require code changes","OPA policy evaluation adds ~50ms latency per API request; high-concurrency deployments may require policy caching","No built-in conflict resolution if two annotators edit the same job simultaneously; last-write-wins semantics"],"requires":["PostgreSQL 15+ for task/job metadata and state tracking","Redis 7.2+ for session management and job assignment caching","Open Policy Agent 0.63.0+ for authorization policy evaluation","Django REST Framework for API endpoints managing job lifecycle"],"input_types":["Task definitions (image/video lists, annotation types)","User and role assignments","Job state transitions (start annotation, submit for review, etc.)"],"output_types":["Job assignments and progress reports","Annotation statistics (objects per user, time per frame)","Audit logs of state transitions and user actions"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cvat__cap_4","uri":"capability://image.visual.video.annotation.with.frame.by.frame.tracking.and.automatic.interpolation","name":"video annotation with frame-by-frame tracking and automatic interpolation","description":"Enables annotation of video frames with automatic object tracking and keyframe-based interpolation. Annotators mark objects in keyframes, and CVAT automatically interpolates object positions/shapes in intermediate frames using tracking models (SiamMask, STARK). The canvas system renders video frame-by-frame with synchronized annotation state, and the backend stores only keyframe annotations plus interpolation parameters, reducing storage by 90% vs. per-frame annotation.","intents":["Annotate video datasets 10x faster by marking objects only in keyframes and auto-interpolating intermediate frames","Track objects across video sequences without manual per-frame annotation","Adjust interpolation results interactively if tracking drifts or fails","Export video annotations in formats compatible with action recognition and tracking models"],"best_for":["Teams annotating autonomous vehicle or surveillance video datasets","Projects with high frame rates (30+ fps) where per-frame annotation is infeasible","Organizations building object tracking datasets (MOT, KITTI format)"],"limitations":["Interpolation accuracy degrades with fast motion or occlusions; manual correction required for ~10-20% of frames","Tracking models (SiamMask, STARK) require GPU; CPU-only deployments see >5s latency per interpolation","Video codec support limited to H.264, VP9, and AV1; proprietary codecs require transcoding","Interpolation assumes linear motion; non-linear trajectories require additional keyframes"],"requires":["Video files in H.264, VP9, or AV1 codec (CVAT auto-transcodes unsupported formats)","GPU for tracking model inference (NVIDIA RTX 3080 or better recommended)","Nuclio cluster for serverless tracking model deployment","Sufficient storage for video frames (typically 1-2GB per hour of 1080p video)"],"input_types":["Video files (MP4, WebM, MOV with H.264/VP9/AV1 codec)","Frame rate and resolution metadata","Keyframe annotations (bounding boxes, polygons, cuboids)"],"output_types":["Per-frame annotations (interpolated from keyframes)","Tracking trajectories (sequences of bounding boxes across frames)","Video annotations in MOT, KITTI, or custom formats"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cvat__cap_5","uri":"capability://image.visual.3d.point.cloud.annotation.with.cuboid.and.polygon.support","name":"3d point cloud annotation with cuboid and polygon support","description":"Provides specialized canvas rendering for 3D point cloud data (LiDAR, depth sensors) with cuboid and polygon annotation primitives. The system loads point clouds from PCD, LAS, or PLY formats, renders them in WebGL with configurable camera controls, and stores 3D annotations in a normalized format. Cuboid annotations include 3D position, rotation, and dimensions; polygon annotations are projected onto 2D views of the point cloud.","intents":["Annotate autonomous vehicle LiDAR data with 3D bounding boxes for object detection training","Label point cloud segmentation datasets with 3D polygons or cuboids","Visualize multi-view 3D data (front, side, top views) and annotate consistently across views","Export 3D annotations in KITTI, Waymo, or custom 3D formats"],"best_for":["Autonomous vehicle teams annotating LiDAR datasets","Robotics companies building 3D perception models","Organizations working with depth sensors or structured-light cameras"],"limitations":["Point cloud rendering performance degrades with >10M points; requires downsampling or LOD techniques","3D cuboid annotation requires manual specification of 7 parameters (3D position, 3D rotation, 3D dimensions); no automatic detection","WebGL rendering limited to modern browsers; older browsers or headless environments not supported","No built-in 3D-to-2D projection consistency checking; annotators must manually verify consistency across views"],"requires":["Point cloud files in PCD, LAS, or PLY format","WebGL 2.0 support in browser (Chrome 56+, Firefox 51+, Safari 15+)","GPU with sufficient VRAM for point cloud rendering (2GB+ recommended)","Three.js or similar 3D rendering library (included in CVAT frontend)"],"input_types":["Point cloud files (PCD, LAS, PLY formats)","Calibration matrices for multi-sensor fusion (optional)","2D images for reference (optional, for multi-view annotation)"],"output_types":["3D cuboid annotations (position, rotation, dimensions)","3D polygon annotations","Point cloud segmentation masks","Annotations in KITTI, Waymo, or custom 3D formats"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cvat__cap_6","uri":"capability://safety.moderation.quality.control.via.ground.truth.jobs.and.honeypot.validation","name":"quality control via ground truth jobs and honeypot validation","description":"Implements quality assurance mechanisms where a subset of tasks are designated as 'ground truth' with known correct annotations. Annotators unknowingly receive honeypot tasks mixed with regular tasks; their annotations on honeypot tasks are compared against ground truth to compute accuracy metrics. The system generates quality reports per annotator and per task, identifying systematic errors (e.g., missed small objects) and flagging low-quality annotators for retraining.","intents":["Measure annotation quality objectively without manual spot-checking","Identify annotators who need retraining or reassignment","Detect systematic annotation errors (e.g., class confusion, boundary inaccuracy)","Enforce minimum quality thresholds before accepting annotations"],"best_for":["Large annotation teams (10+ annotators) where quality variance is high","Projects with strict quality requirements (medical imaging, autonomous vehicles)","Organizations building long-term annotation pipelines where quality trends matter"],"limitations":["Ground truth creation requires manual effort; typically 5-10% of dataset must be pre-annotated","Honeypot detection may cause annotator anxiety or gaming behavior if not communicated carefully","Quality metrics (IoU, F1) are task-specific; no universal metric across different annotation types","Requires statistical significance testing; small sample sizes (<50 honeypot tasks) produce unreliable metrics"],"requires":["Pre-annotated ground truth dataset (5-10% of total tasks)","PostgreSQL 15+ for storing quality metrics and honeypot assignments","Annotation comparison algorithms (IoU for bounding boxes, Dice for masks, etc.)","Statistical analysis tools for computing confidence intervals"],"input_types":["Ground truth annotations (manually verified, high-quality)","Annotator submissions on honeypot tasks","Task metadata (annotation type, object class, difficulty)"],"output_types":["Per-annotator quality scores (accuracy, precision, recall)","Per-task quality reports (inter-annotator agreement, outlier detection)","Systematic error analysis (class confusion matrices, boundary accuracy histograms)","Recommendations for annotator retraining or reassignment"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cvat__cap_7","uri":"capability://tool.use.integration.cloud.storage.integration.with.s3.azure.blob.and.google.cloud.storage","name":"cloud storage integration with s3, azure blob, and google cloud storage","description":"Abstracts cloud storage backends via a pluggable storage driver architecture, supporting AWS S3, Azure Blob Storage, and Google Cloud Storage. CVAT stores images/videos in cloud buckets and streams them to the frontend on-demand, avoiding local disk bottlenecks. The system handles authentication (IAM roles, SAS tokens, service accounts), multipart uploads for large files, and automatic cleanup of temporary files. Storage drivers are configured per-project, enabling multi-cloud deployments.","intents":["Store large image/video datasets (100GB+) without local disk constraints","Enable distributed teams to access shared datasets from cloud storage without downloading locally","Integrate CVAT with existing cloud data pipelines (e.g., data lakes in S3 or Azure Data Lake)","Reduce data transfer costs by streaming from cloud storage instead of downloading entire datasets"],"best_for":["Organizations with large datasets (>1TB) that exceed local storage capacity","Teams using AWS, Azure, or GCP as primary data infrastructure","Enterprises with strict data residency requirements (data must stay in specific regions)"],"limitations":["Streaming from cloud storage adds 100-500ms latency per frame vs. local SSD; noticeable on high-latency networks","Multipart uploads require resumable upload support; some cloud providers have size limits (e.g., Azure Blob 4.75TB max)","Cloud storage costs scale with data transfer; frequent frame scrubbing in video annotation can incur high egress charges","Authentication tokens expire; long-running annotation sessions may require token refresh logic"],"requires":["AWS S3, Azure Blob Storage, or Google Cloud Storage bucket with appropriate permissions","Cloud credentials (IAM role for S3, SAS token for Azure, service account for GCS)","Network connectivity to cloud storage (typically 100+ Mbps for smooth streaming)","CVAT backend configured with cloud storage driver (S3, Azure, or GCS)"],"input_types":["Cloud storage paths (s3://bucket/prefix, gs://bucket/prefix, etc.)","Cloud credentials (IAM roles, SAS tokens, service account keys)","Image/video files in cloud storage"],"output_types":["Streamed images/videos to frontend","Annotations stored in cloud storage or local PostgreSQL","Exported datasets uploaded back to cloud storage"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cvat__cap_8","uri":"capability://tool.use.integration.rest.api.with.openapi.schema.and.sdk.code.generation","name":"rest api with openapi schema and sdk code generation","description":"Exposes all CVAT functionality via a comprehensive REST API documented with OpenAPI 3.0 schema (cvat/schema.yml). The API is auto-generated from Django REST Framework serializers and viewsets, ensuring schema accuracy. CVAT provides auto-generated SDKs (Python, JavaScript) via OpenAPI code generation, enabling programmatic access to annotation workflows without direct HTTP calls. The API supports filtering, pagination, and bulk operations for efficient data access.","intents":["Build custom annotation workflows or integrations without modifying CVAT core","Automate annotation tasks (e.g., bulk task creation, job assignment) via scripts or CI/CD pipelines","Integrate CVAT with external ML pipelines or data management systems","Develop custom frontends or mobile apps on top of CVAT's annotation engine"],"best_for":["Developers building custom annotation workflows or integrations","ML engineers automating annotation pipelines in CI/CD systems","Organizations with existing REST API infrastructure seeking to integrate CVAT"],"limitations":["API rate limiting not enforced by default; high-concurrency clients may overwhelm backend","Pagination is cursor-based; large result sets (>10k items) require multiple requests","Bulk operations (e.g., create 1000 tasks) are not atomic; partial failures require manual retry logic","OpenAPI schema is auto-generated; custom endpoints or undocumented behavior may not appear in schema"],"requires":["CVAT backend running with REST API enabled (default)","Authentication token (JWT or session cookie)","Network connectivity to CVAT backend","OpenAPI client library (auto-generated or manual)"],"input_types":["HTTP requests (GET, POST, PATCH, DELETE)","JSON request bodies for task/job creation","Query parameters for filtering and pagination"],"output_types":["JSON responses with task/job/annotation data","OpenAPI schema (YAML or JSON)","Auto-generated SDK code (Python, JavaScript)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cvat__cap_9","uri":"capability://safety.moderation.role.based.access.control.rbac.with.open.policy.agent.opa.authorization","name":"role-based access control (rbac) with open policy agent (opa) authorization","description":"Implements fine-grained authorization using Open Policy Agent (OPA), a declarative policy engine. CVAT defines authorization policies in Rego language (OPA's policy language) that specify who can perform which actions on which resources. Policies are evaluated at the API gateway level (Traefik) and in the Django backend, enabling both coarse-grained (endpoint-level) and fine-grained (object-level) access control. Policies are versioned separately from code, enabling runtime updates without redeployment.","intents":["Enforce fine-grained permissions (e.g., annotators can only access assigned jobs, not all tasks)","Implement multi-tenant isolation where organizations cannot access each other's data","Define custom authorization rules (e.g., 'only senior annotators can review quality issues')","Audit authorization decisions for compliance (HIPAA, GDPR, SOC 2)"],"best_for":["Enterprises with complex authorization requirements (multiple roles, multi-tenant isolation)","Organizations requiring audit trails and compliance reporting","Teams wanting to update authorization policies without code deployment"],"limitations":["OPA policy evaluation adds ~50ms latency per API request; high-concurrency deployments may require policy caching or optimization","Rego language has steep learning curve; requires dedicated policy engineers for complex rules","Policy bugs can silently deny legitimate access; requires comprehensive testing and staging","No built-in policy versioning; requires external version control (Git) for policy history"],"requires":["Open Policy Agent 0.63.0+ cluster or sidecar","Rego policy files defining authorization rules","Traefik v3.6+ for API gateway-level policy evaluation","Django backend with OPA integration middleware"],"input_types":["Rego policy files defining authorization rules","User identity and role information","Resource metadata (task ID, project ID, etc.)","Action being requested (read, write, delete)"],"output_types":["Authorization decision (allow/deny)","Audit logs of authorization decisions","Policy evaluation metrics (latency, cache hit rate)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cvat__headline","uri":"capability://data.processing.analysis.web.based.computer.vision.annotation.tool","name":"web-based computer vision annotation tool","description":"CVAT is an open-source, web-based tool designed for annotating images and videos, supporting various annotation types and collaborative workflows for machine learning datasets.","intents":["best computer vision annotation tool","computer vision annotation for machine learning","open-source image labeling software","video annotation tool for AI projects","collaborative annotation platform for datasets"],"best_for":["machine learning projects","team-based annotation tasks"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["Datumaro library (included in CVAT dependencies)","Sufficient disk space for temporary format conversion buffers","PostgreSQL 15+ for metadata storage during import/export operations","Nuclio 1.0+ cluster (Kubernetes or Docker-based)","Model packaged as Docker container or Python function with Nuclio SDK","GPU resources if using deep learning models (NVIDIA CUDA 11.8+ recommended)","Network connectivity between CVAT backend and Nuclio cluster","Redis 7.2+ or Kvrocks 2.12.1+ for task queue backend","Celery 5.0+ for task execution framework","PostgreSQL 15+ for task status tracking"],"failure_modes":["Format conversion may lose metadata not present in target schema (e.g., confidence scores in YOLO export)","Large dataset imports (>100k images) require background job processing and may timeout without proper worker configuration","Custom format plugins require Python development and restart of CVAT services to register","Requires Nuclio cluster setup and configuration; not available in single-machine deployments without additional infrastructure","Model inference latency directly impacts annotation speed; large models (>1GB) may cause timeouts on standard hardware","No built-in model versioning or A/B testing framework; requires manual tracking of model performance across annotation batches","Auto-annotations require human review before acceptance; no automatic quality thresholding","Celery adds operational complexity; requires Redis/Kvrocks cluster and worker process management","Task failures are not automatically retried; requires explicit retry configuration per task type","No built-in task prioritization; all tasks are processed FIFO unless explicitly prioritized","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":0.3,"quality":0.2,"ecosystem":0.15,"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":"2026-06-17T09:51:04.690Z","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=cvat","compare_url":"https://unfragile.ai/compare?artifact=cvat"}},"signature":"mVVtWlwVum/yf00Sb8QflIkgIIZe66/QH1FnJpthG7Qr2ehUwsrwa9LM+7v65YMWFQPFCW2moHGwiIzRqCiRDA==","signedAt":"2026-06-22T01:11:30.980Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/cvat","artifact":"https://unfragile.ai/cvat","verify":"https://unfragile.ai/api/v1/verify?slug=cvat","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"}}