{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"roboflow","slug":"roboflow","name":"Roboflow","type":"platform","url":"https://roboflow.com","page_url":"https://unfragile.ai/roboflow","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"roboflow__cap_0","uri":"capability://planning.reasoning.one.click.automated.model.training.with.metric.reporting","name":"one-click automated model training with metric reporting","description":"Roboflow Train accepts annotated datasets and automatically trains computer vision models using two pre-configured architectures, returning performance metrics (mAP, precision, recall) within 24 hours without requiring hyperparameter tuning or infrastructure setup. The system abstracts away model selection, optimization, and hardware provisioning, using a credit-based consumption model where training jobs consume credits based on dataset size and augmentation settings.","intents":["I want to train a custom object detection model without managing GPU infrastructure or deep learning frameworks","I need to quickly iterate on model performance by retraining with updated annotations","I want to compare model performance metrics across multiple training runs on the same dataset"],"best_for":["teams without ML engineering expertise building production computer vision systems","rapid prototyping scenarios where 24-hour training latency is acceptable","organizations wanting to avoid GPU infrastructure management overhead"],"limitations":["24-hour training turnaround prevents real-time iteration during development","Only two model architectures available — no ability to select specific backbones (ResNet, EfficientNet, etc.) or customize training hyperparameters","Concurrent training limited on Core plan; Enterprise required for unlimited parallel training jobs","No access to training logs, loss curves, or intermediate checkpoints for debugging convergence issues"],"requires":["Annotated dataset with minimum 10-50 images (exact threshold unknown)","Core plan ($79/year) or higher for model training; Public plan limited to inference only","Dataset uploaded to Roboflow workspace with supported annotation format (COCO, Pascal VOC, YOLO, etc.)","Sufficient credits in account (exact credit consumption per training job unknown)"],"input_types":["annotated image dataset (JPG, PNG, BMP)","annotation metadata (COCO JSON, Pascal VOC XML, YOLO txt, or Roboflow native format)"],"output_types":["trained model weights (downloadable on Core+ plans)","performance metrics JSON (mAP, precision, recall, confusion matrix)","inference-ready model endpoint (hosted on Roboflow cloud)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"roboflow__cap_1","uri":"capability://data.processing.analysis.dataset.annotation.and.labeling.with.auto.labeling.foundation.models","name":"dataset annotation and labeling with auto-labeling foundation models","description":"Roboflow provides web-based annotation tools for bounding boxes, polygons, keypoints, and classifications, with optional auto-labeling powered by foundation models (via Autodistill integration) that pre-populate annotations for human review. The platform supports both manual annotation and outsourced labeling services at per-annotation pricing ($0.10 bounding box, $0.20 polygon, $0.05 classification/keypoint), with version control tracking annotation changes across dataset iterations.","intents":["I need to annotate raw images with bounding boxes or polygons for object detection training","I want to speed up annotation by auto-labeling with a foundation model, then have humans correct predictions","I need to outsource annotation work to a labeling service without managing external vendors"],"best_for":["teams building custom object detection datasets with domain-specific objects","organizations with limited annotation budgets wanting to use foundation models for pre-labeling","enterprises requiring audit trails and version control for annotation changes"],"limitations":["Auto-labeling quality depends on foundation model choice; no guidance on which models work best for specific domains","Web-based annotation UI may be slow for large-scale labeling (100k+ images); no batch annotation API mentioned","Outsourced labeling services add 5-10 day turnaround (estimated) vs. in-house annotation","No inter-annotator agreement metrics or consensus labeling workflows for quality assurance"],"requires":["Raw images uploaded to Roboflow workspace (JPG, PNG, BMP formats)","Public plan or higher for manual annotation; Core+ required for auto-labeling features","API key for foundation model provider if using Autodistill (e.g., OpenAI, Anthropic)","Credits for outsourced labeling if using Roboflow's labeling services"],"input_types":["raw images (JPG, PNG, BMP)","optional: existing annotations in COCO, Pascal VOC, or YOLO format for correction"],"output_types":["annotated dataset with bounding boxes, polygons, keypoints, or classifications","annotation metadata in COCO JSON, Pascal VOC XML, YOLO txt, or Roboflow native format","version history tracking annotation changes per image"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"roboflow__cap_10","uri":"capability://search.retrieval.roboflow.universe.public.registry.for.dataset.and.model.discovery","name":"roboflow universe public registry for dataset and model discovery","description":"Roboflow Universe is a public registry hosting open-source datasets and trained models, enabling community sharing and discovery of computer vision artifacts. Users can browse, download, and fork public datasets and models without authentication. The registry supports versioning and provides download links for direct integration into training pipelines.","intents":["I want to find a public dataset for a specific computer vision task (e.g., object detection of specific objects)","I need a pre-trained model for inference without training from scratch","I want to contribute my dataset or model to the community"],"best_for":["researchers and hobbyists looking for public datasets to benchmark models","teams wanting to bootstrap projects with existing datasets or models","open-source contributors sharing computer vision artifacts"],"limitations":["Dataset and model quality not curated or validated; no ratings or reviews system documented","Search and discovery mechanisms unknown — no documentation on filtering, sorting, or recommendation algorithms","No social features (likes, follows, discussions) documented; limited community engagement","Licensing and attribution requirements unclear for downloaded artifacts"],"requires":["Internet connectivity to browse Roboflow Universe","No Roboflow account required for downloading public artifacts","Compatible training framework for downloaded datasets/models"],"input_types":["search queries (text-based, exact mechanism unknown)"],"output_types":["dataset downloads (in multiple formats: COCO, YOLO, etc.)","pre-trained model weights","dataset/model metadata and version history"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"roboflow__cap_11","uri":"capability://automation.workflow.credit.based.consumption.model.with.flexible.pricing.tiers","name":"credit-based consumption model with flexible pricing tiers","description":"Roboflow uses a credit-based system for consumption tracking across training, inference, augmentation, and storage. Public plan includes $60/month free credits; Core plan ($79/year or $99/month) includes 50 credits/month; additional credits available at $4 (prepaid) or $6 (flex) per credit. Outsourced labeling services priced per annotation ($0.10 bounding box, $0.20 polygon, $0.05 classification/keypoint). Enterprise plans offer custom pricing with priority GPU access.","intents":["I want to understand how much my computer vision project will cost before committing","I need flexible pricing that scales with my usage without long-term contracts","I want to budget for outsourced annotation services"],"best_for":["startups and small teams with variable usage patterns","organizations wanting pay-as-you-go pricing without infrastructure costs","enterprises with custom requirements (HIPAA, SSO, audit logs)"],"limitations":["Credit consumption rates opaque — no documentation on how many credits each operation consumes (training, inference, augmentation)","No usage dashboard or cost forecasting tools documented","Enterprise pricing requires sales contact; no transparency on custom pricing","Flex credits ($6 each) more expensive than prepaid ($4 each) — incentivizes bulk purchasing"],"requires":["Roboflow account with payment method on file","Public plan or higher for any paid operations"],"input_types":["usage metrics (training jobs, inference requests, augmentation operations, annotations)"],"output_types":["credit consumption tracking","billing statements (monthly)","usage reports (exact format unknown)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"roboflow__cap_12","uri":"capability://safety.moderation.enterprise.compliance.and.access.control.with.hipaa.sso.and.audit.logs","name":"enterprise compliance and access control with hipaa, sso, and audit logs","description":"Roboflow Enterprise plans include HIPAA compliance with Business Associate Agreement (BAA), single sign-on (SSO) integration, custom role-based access control (RBAC), and audit logs tracking all user actions. These features enable regulated industries (healthcare, finance) to use Roboflow while meeting compliance requirements. Data retention is unlimited across all plans.","intents":["I need to use Roboflow in a healthcare application with HIPAA compliance","I want to enforce access control across my organization with SSO and custom roles","I need audit trails for regulatory compliance and security investigations"],"best_for":["healthcare organizations building medical imaging AI systems","financial services firms deploying computer vision for fraud detection","enterprises with strict data governance and compliance requirements"],"limitations":["HIPAA compliance requires Enterprise plan — no option for smaller organizations with healthcare use cases","Data residency options unknown — no documentation on geographic data storage or compliance with GDPR/CCPA","Encryption at rest/transit not explicitly mentioned; security posture unclear","Custom RBAC granularity unknown — no documentation on permission model or role templates"],"requires":["Enterprise plan (custom pricing, requires sales contact)","HIPAA BAA signed with Roboflow for healthcare use cases","SSO provider (Okta, Azure AD, Google Workspace, etc.) for SSO integration","Audit log retention policy and monitoring infrastructure"],"input_types":["user identity and access requests (via SSO provider)"],"output_types":["audit logs (JSON or CSV format, exact schema unknown)","access control enforcement (allow/deny decisions)","compliance reports (format unknown)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"roboflow__cap_2","uri":"capability://data.processing.analysis.intelligent.dataset.augmentation.with.version.management","name":"intelligent dataset augmentation with version management","description":"Roboflow Augmentation applies 15+ transformation techniques (rotation, brightness, blur, mosaic, etc.) to images while preserving annotation integrity, generating multiple augmented versions per source image. The system stores augmented datasets as separate versions with metadata tracking, allowing users to compare model performance across different augmentation strategies without duplicating storage. Public plan limited to 3 augmented versions per image; Core+ supports up to 50 versions with pay-as-you-go credits.","intents":["I want to increase training dataset diversity without manually collecting more images","I need to test which augmentation strategies improve model robustness to real-world variations","I want to generate augmented datasets without managing image processing pipelines or annotation preservation"],"best_for":["teams with small annotated datasets (100-1000 images) needing synthetic diversity","computer vision projects targeting edge deployment where robustness to lighting/angle variations is critical","rapid prototyping scenarios where augmentation can substitute for additional data collection"],"limitations":["Augmentation is deterministic and rule-based; no learned augmentation policies (AutoAugment, RandAugment) mentioned","Public plan capped at 3 augmented versions per image — insufficient for comprehensive augmentation studies","No ability to customize augmentation parameters (e.g., rotation range, brightness delta) — presets only","Augmentation applied uniformly across dataset; no per-class or per-image conditional augmentation"],"requires":["Annotated dataset uploaded to Roboflow workspace","Public plan or higher; Core+ required for >3 augmented versions per image","Credits for augmentation generation (exact consumption rate unknown)"],"input_types":["annotated images (JPG, PNG, BMP) with bounding boxes, polygons, or classifications"],"output_types":["augmented image versions (JPG, PNG, BMP)","augmented annotation metadata (COCO JSON, Pascal VOC XML, YOLO txt)","dataset version metadata with augmentation parameters applied"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"roboflow__cap_3","uri":"capability://tool.use.integration.hosted.inference.api.with.autoscaling.and.multi.format.input.support","name":"hosted inference api with autoscaling and multi-format input support","description":"Roboflow provides HTTP-based inference endpoints that automatically scale to handle variable request load, accepting images and videos via URL or base64 encoding and returning predictions with confidence scores. The inference API uses a model ID format (project/version) to route requests to specific trained models, with built-in load balancing and burst capacity. Autoscaling infrastructure handles traffic spikes without manual configuration; Enterprise plans include priority access to faster GPU hardware.","intents":["I want to deploy a trained model as a REST API without managing servers or containers","I need to run inference on images and videos from my application with automatic scaling","I want to monitor inference requests and collect sample predictions to improve model performance"],"best_for":["teams deploying computer vision models to production without DevOps expertise","applications with variable inference load (traffic spikes) requiring automatic scaling","organizations wanting managed inference without container orchestration (Kubernetes) overhead"],"limitations":["Cold start latency unknown; no SLA specified for inference response time","Video processing requires frame extraction at specified sample rate — not true streaming inference","Supported formats limited to JPG, PNG, BMP (images) and MOV, MP4, AVI (video); no WebP or HEIC support mentioned","No batch inference API documented; examples show single-image inference only","Inference monitoring vague — 'collect sample inferences' mentioned but no real-time dashboards or alerting documented"],"requires":["Trained model deployed via Roboflow Train or uploaded model weights","API key for authentication (provided in Roboflow dashboard)","Public plan or higher; inference included in all paid tiers","Network connectivity to Roboflow cloud (no on-premise inference API gateway mentioned)"],"input_types":["image URL (HTTP/HTTPS)","image base64-encoded string","video URL (HTTP/HTTPS) with frame sample rate parameter","video file upload (MOV, MP4, AVI)"],"output_types":["JSON predictions with bounding boxes, confidence scores, class labels","segmentation masks (if model supports segmentation)","keypoint coordinates (if model supports pose estimation)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"roboflow__cap_4","uri":"capability://automation.workflow.edge.device.deployment.with.hardware.specific.optimization","name":"edge device deployment with hardware-specific optimization","description":"Roboflow supports one-click deployment to edge devices including NVIDIA Jetson, Luxonis OAK (hardware accelerator + camera), iOS mobile devices, and web browsers via roboflow.js, with automatic model optimization for target hardware constraints. The platform handles model quantization, pruning, and format conversion (ONNX, TensorFlow Lite, CoreML) without requiring manual optimization. Self-hosted and VPC deployment options available for on-premise inference.","intents":["I want to deploy a trained model to a Jetson device without manually optimizing for GPU/memory constraints","I need to run inference on mobile devices (iOS) with minimal latency and battery impact","I want to deploy models in a private VPC or on-premise without sending data to Roboflow cloud"],"best_for":["robotics and autonomous systems teams deploying to NVIDIA Jetson platforms","mobile app developers adding computer vision features without ML expertise","enterprises with data residency requirements prohibiting cloud inference"],"limitations":["Hardware support limited to specific devices (Jetson, OAK, iOS); no Android, Raspberry Pi, or x86 CPU inference documented","Model optimization approach unknown — no transparency on quantization strategy (INT8, FP16) or accuracy loss","Web deployment (roboflow.js) likely limited to small models due to browser memory constraints; no documentation on supported model sizes","Self-hosted deployment requires managing inference server infrastructure; no managed self-hosted option"],"requires":["Trained model exported from Roboflow Train or uploaded model weights","Target hardware (NVIDIA Jetson with JetPack OS, Luxonis OAK device, iOS 13+, or modern web browser)","Core plan or higher for edge deployment; Public plan limited to cloud inference","Network connectivity for initial model download; offline inference supported after deployment"],"input_types":["trained model weights (Roboflow format or ONNX/TensorFlow)","hardware target specification (Jetson model, OAK device type, iOS version)"],"output_types":["optimized model binary for target hardware (TensorFlow Lite for mobile, ONNX for Jetson, etc.)","deployment package with inference runtime and dependencies","inference results (bounding boxes, confidence scores, class labels)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"roboflow__cap_5","uri":"capability://data.processing.analysis.dataset.versioning.and.format.conversion.with.15.export.formats","name":"dataset versioning and format conversion with 15+ export formats","description":"Roboflow maintains version history for datasets, tracking changes across annotations, augmentations, and preprocessing steps. The platform supports exporting datasets in 15+ formats including COCO JSON, Pascal VOC XML, YOLO txt, TensorFlow TFRecord, and others, enabling seamless integration with external training frameworks. Version control allows rolling back to previous dataset states and comparing model performance across versions.","intents":["I want to export my annotated dataset in YOLO format to train with YOLOv8 outside Roboflow","I need to track dataset changes over time and understand which annotation version produced the best model","I want to use my Roboflow dataset with multiple training frameworks (PyTorch, TensorFlow, Detectron2)"],"best_for":["teams using multiple training frameworks and needing format-agnostic dataset management","researchers requiring dataset versioning and reproducibility tracking","organizations avoiding vendor lock-in by exporting datasets in standard formats"],"limitations":["Export format support list incomplete in documentation; exact count and supported formats unknown","No streaming export API for large datasets (100k+ images); export likely requires full download","Format conversion may lose metadata (e.g., annotation confidence scores, annotator IDs) depending on target format","No automatic format validation — exported datasets may have compatibility issues with target frameworks"],"requires":["Annotated dataset in Roboflow workspace","Public plan or higher for dataset export","Target framework documentation to verify format compatibility"],"input_types":["Roboflow dataset (any version)"],"output_types":["COCO JSON format","Pascal VOC XML format","YOLO txt format","TensorFlow TFRecord format","PyTorch format","Detectron2 format","other formats (exact list unknown)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"roboflow__cap_6","uri":"capability://data.processing.analysis.dataset.quality.analytics.with.class.balance.and.dimension.insights","name":"dataset quality analytics with class balance and dimension insights","description":"Roboflow provides automated dataset analysis tools including class balance visualization (showing class distribution imbalance), dimension insights (image size and aspect ratio analysis), annotation heatmaps (spatial distribution of annotations), and health checks with improvement suggestions. These analytics help identify dataset biases and quality issues before training, with Enterprise plans offering metrics filtering by custom tags.","intents":["I want to understand if my dataset has class imbalance that might hurt model performance","I need to identify images with unusual dimensions or aspect ratios that might cause training issues","I want to see where annotations are concentrated spatially to detect annotation bias"],"best_for":["data scientists validating dataset quality before training","teams debugging poor model performance by analyzing dataset characteristics","organizations ensuring dataset diversity and reducing annotation bias"],"limitations":["Analytics are descriptive only — no automated rebalancing or bias mitigation recommendations","Metrics filtering by tags available only on Enterprise plan; Public/Core plans show aggregate statistics only","No statistical significance testing or confidence intervals for class imbalance metrics","Heatmap visualization may be difficult to interpret for large datasets with thousands of images"],"requires":["Annotated dataset uploaded to Roboflow workspace","Public plan or higher for dataset analytics","Enterprise plan for advanced filtering by custom tags"],"input_types":["annotated dataset (images + annotations)"],"output_types":["class distribution chart (bar chart showing count per class)","dimension distribution (histogram of image sizes and aspect ratios)","annotation heatmap (spatial density visualization)","health check report with improvement suggestions (text)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"roboflow__cap_7","uri":"capability://memory.knowledge.inference.monitoring.and.active.learning.with.confidence.based.sampling","name":"inference monitoring and active learning with confidence-based sampling","description":"Roboflow collects sample inferences from deployed models at configurable time intervals, random sampling rates, or based on confidence thresholds, storing predictions for analysis and retraining. The system enables active learning workflows where low-confidence predictions are flagged for human review and annotation, creating feedback loops to improve model performance. Collected inferences can be added back to training datasets as new versions.","intents":["I want to monitor my deployed model's performance by collecting sample predictions in production","I need to identify cases where my model is uncertain (low confidence) and collect them for retraining","I want to build an active learning loop where production failures automatically improve the model"],"best_for":["teams deploying models to production and wanting continuous improvement feedback","organizations with limited labeled data wanting to prioritize annotation on uncertain predictions","systems requiring model monitoring without external APM tools"],"limitations":["Inference monitoring details vague — no documentation on sampling strategies, storage limits, or retention policies","No real-time alerting or dashboards for monitoring model drift or performance degradation","Active learning workflow requires manual annotation of collected samples; no automatic retraining trigger documented","Confidence-based sampling may miss systematic failures (e.g., specific object types or lighting conditions)"],"requires":["Deployed inference model on Roboflow cloud or edge device","Core plan or higher for inference monitoring features","Mechanism to send inference requests through Roboflow API (not automatic for edge deployments)"],"input_types":["inference requests (images or videos sent to Roboflow API)"],"output_types":["sampled predictions with confidence scores","flagged low-confidence predictions for human review","annotated samples ready for retraining"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"roboflow__cap_8","uri":"capability://tool.use.integration.python.sdk.and.http.client.for.programmatic.model.access","name":"python sdk and http client for programmatic model access","description":"Roboflow provides two Python packages: `roboflow` for general platform access (dataset management, training) and `inference` for inference operations. The inference SDK uses HTTP-based client library with model ID routing (project/version format), supporting both cloud and local inference server modes. Local inference server can be started via CLI (`inference server start`) for on-device inference without cloud dependency.","intents":["I want to programmatically train models and manage datasets without using the web UI","I need to run inference from Python code with automatic cloud/local fallback","I want to deploy a local inference server on my machine or edge device for offline inference"],"best_for":["Python developers integrating Roboflow into ML pipelines and applications","teams automating model training and deployment workflows","organizations requiring offline inference without cloud dependency"],"limitations":["Python-only SDKs; no official JavaScript, Go, or Java clients documented","HTTP-based inference client adds network latency vs. in-process inference libraries","Local inference server requires managing separate process; no Docker image or systemd service template documented","SDK documentation incomplete — full API reference and error handling patterns unknown"],"requires":["Python 3.7+ (exact minimum version unknown)","pip package manager","API key from Roboflow dashboard for cloud operations","Node.js or Docker for local inference server (exact requirements unknown)"],"input_types":["image file paths (local or URL)","video file paths","dataset configuration (JSON or Python dict)"],"output_types":["inference predictions (JSON with bounding boxes, confidence scores)","training job status and metrics","dataset metadata and version information"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"roboflow__cap_9","uri":"capability://tool.use.integration.open.source.ecosystem.with.supervision.autodistill.and.inference.libraries","name":"open-source ecosystem with supervision, autodistill, and inference libraries","description":"Roboflow maintains five open-source projects: `supervision` (annotation and object tracking utilities), `autodistill` (foundation model-based auto-labeling), `inference` (production-ready inference server), `trackers` (multi-object tracking algorithms), and `notebooks` (Jupyter notebooks for training). These libraries are Apache 2.0 licensed and can be used independently of Roboflow platform, enabling custom workflows and reducing vendor lock-in.","intents":["I want to use Roboflow's open-source tools (supervision, autodistill) without committing to the platform","I need a production-ready inference server that works with any trained model, not just Roboflow models","I want to implement custom multi-object tracking without building from scratch"],"best_for":["developers building custom computer vision pipelines with open-source tools","teams wanting to avoid vendor lock-in by using platform-agnostic libraries","researchers and hobbyists without budget for commercial platforms"],"limitations":["Open-source libraries have separate maintenance cycles from platform; feature parity not guaranteed","Community support for open-source projects may be slower than commercial platform support","Integration between open-source libraries and Roboflow platform not seamless; requires manual glue code","Documentation for open-source projects may be less comprehensive than platform documentation"],"requires":["Python 3.7+ for most libraries","pip package manager","Git for cloning repositories","No Roboflow account required for open-source usage"],"input_types":["images or video frames","trained model weights (ONNX, TensorFlow, PyTorch)","annotation data in standard formats"],"output_types":["annotated images with bounding boxes, masks, or keypoints (supervision)","auto-labeled annotations using foundation models (autodistill)","inference predictions from local server (inference)","tracked object trajectories (trackers)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"roboflow__headline","uri":"capability://image.visual.end.to.end.computer.vision.platform","name":"end-to-end computer vision platform","description":"Roboflow is an end-to-end computer vision platform that simplifies dataset management, model training, and deployment, making it easy for developers to build and deploy computer vision applications.","intents":["best computer vision platform","computer vision model training for beginners","how to deploy computer vision models","top tools for dataset management in computer vision","computer vision annotation tools comparison"],"best_for":["developers looking for a comprehensive computer vision solution"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":56,"verified":false,"data_access_risk":"high","permissions":["Annotated dataset with minimum 10-50 images (exact threshold unknown)","Core plan ($79/year) or higher for model training; Public plan limited to inference only","Dataset uploaded to Roboflow workspace with supported annotation format (COCO, Pascal VOC, YOLO, etc.)","Sufficient credits in account (exact credit consumption per training job unknown)","Raw images uploaded to Roboflow workspace (JPG, PNG, BMP formats)","Public plan or higher for manual annotation; Core+ required for auto-labeling features","API key for foundation model provider if using Autodistill (e.g., OpenAI, Anthropic)","Credits for outsourced labeling if using Roboflow's labeling services","Internet connectivity to browse Roboflow Universe","No Roboflow account required for downloading public artifacts"],"failure_modes":["24-hour training turnaround prevents real-time iteration during development","Only two model architectures available — no ability to select specific backbones (ResNet, EfficientNet, etc.) or customize training hyperparameters","Concurrent training limited on Core plan; Enterprise required for unlimited parallel training jobs","No access to training logs, loss curves, or intermediate checkpoints for debugging convergence issues","Auto-labeling quality depends on foundation model choice; no guidance on which models work best for specific domains","Web-based annotation UI may be slow for large-scale labeling (100k+ images); no batch annotation API mentioned","Outsourced labeling services add 5-10 day turnaround (estimated) vs. in-house annotation","No inter-annotator agreement metrics or consensus labeling workflows for quality assurance","Dataset and model quality not curated or validated; no ratings or reviews system documented","Search and discovery mechanisms unknown — no documentation on filtering, sorting, or recommendation algorithms","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":"2026-05-24T12:16:25.061Z","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=roboflow","compare_url":"https://unfragile.ai/compare?artifact=roboflow"}},"signature":"h2xTnia2xskN3PKe25nz1p8I26gK6UUcTw2C0jNnCmnsmjJiHr1MKuiljleae1C0sgL94leHG3dIIWhhdc4OAw==","signedAt":"2026-07-08T02:24:02.503Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/roboflow","artifact":"https://unfragile.ai/roboflow","verify":"https://unfragile.ai/api/v1/verify?slug=roboflow","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"}}