{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_datature","slug":"datature","name":"Datature","type":"product","url":"https://datature.io","page_url":"https://unfragile.ai/datature","categories":["model-training","deployment-infra"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_datature__cap_0","uri":"capability://data.preparation.visual.image.annotation.for.computer.vision.datasets","name":"visual image annotation for computer vision datasets","description":"Provides a graphical interface to manually label images with bounding boxes, polygons, or classification tags for training computer vision models. Supports collaborative annotation workflows and quality control mechanisms.","intents":["I need to label images for object detection without writing code","I want my team to annotate images together with version control","I need to ensure annotation quality across a large dataset"],"best_for":["product teams","SMBs","non-technical annotators","teams without ML expertise"],"limitations":["freemium tier has volume restrictions","limited to 2D image annotation","no support for video frame annotation"],"requires":["image files in common formats (JPG, PNG)","web browser access","dataset organization"],"input_types":["image files"],"output_types":["annotated dataset with labels and coordinates"],"categories":["data-preparation","no-code"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_datature__cap_1","uri":"capability://data.preparation.automated.dataset.splitting.and.preprocessing","name":"automated dataset splitting and preprocessing","description":"Automatically partitions annotated images into training, validation, and test sets with configurable ratios. Applies image normalization and augmentation techniques without manual configuration.","intents":["I need to split my dataset properly without data leakage","I want to augment my training data to improve model robustness","I need preprocessing applied consistently across my dataset"],"best_for":["teams without ML expertise","rapid prototyping teams","SMBs"],"limitations":["limited control over augmentation parameters","no custom preprocessing pipelines"],"requires":["annotated dataset","minimum dataset size"],"input_types":["annotated images"],"output_types":["train/validation/test dataset splits"],"categories":["data-preparation","no-code"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_datature__cap_10","uri":"capability://machine.learning.model.export.to.standard.formats","name":"model export to standard formats","description":"Exports trained models to industry-standard formats (ONNX, TensorFlow, PyTorch) enabling use outside Datature platform and integration with custom pipelines.","intents":["I need to use my model in my own ML pipeline","I want to export my model to ONNX for edge deployment","I need to integrate my model with custom inference code"],"best_for":["teams with custom ML infrastructure","edge deployment scenarios","advanced users"],"limitations":["export options may be limited in freemium tier","some model types may not be exportable"],"requires":["trained model"],"input_types":["trained model"],"output_types":["ONNX","TensorFlow SavedModel","PyTorch checkpoint files"],"categories":["machine-learning","integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_datature__cap_11","uri":"capability://data.preparation.dataset.quality.analysis.and.labeling.consistency.checks","name":"dataset quality analysis and labeling consistency checks","description":"Analyzes annotated datasets for quality issues including label inconsistencies, missing annotations, and outliers. Provides recommendations for dataset improvement.","intents":["I need to check if my annotations are consistent across the dataset","I want to find problematic or mislabeled images","I need to understand if my dataset quality is sufficient for training"],"best_for":["teams with large datasets","quality-focused teams","annotation teams"],"limitations":["analysis may be limited in freemium tier","recommendations are automated and may miss domain-specific issues"],"requires":["annotated dataset"],"input_types":["annotated images"],"output_types":["quality report","inconsistency flags","improvement recommendations"],"categories":["data-preparation","analytics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_datature__cap_12","uri":"capability://machine.learning.transfer.learning.with.custom.fine.tuning","name":"transfer learning with custom fine-tuning","description":"Enables users to leverage pre-trained models and fine-tune them on custom datasets without training from scratch, reducing training time and data requirements.","intents":["I have a small dataset and need to train a model quickly","I want to adapt a pre-trained model to my specific use case","I need to reduce training time and computational resources"],"best_for":["teams with limited data","rapid prototyping","resource-constrained teams"],"limitations":["limited control over fine-tuning parameters","pre-trained models are Datature-provided"],"requires":["custom dataset","pre-trained model selection"],"input_types":["annotated custom dataset"],"output_types":["fine-tuned model"],"categories":["machine-learning","no-code"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_datature__cap_2","uri":"capability://machine.learning.no.code.model.training.with.automatic.hyperparameter.optimization","name":"no-code model training with automatic hyperparameter optimization","description":"Trains computer vision models (object detection, classification) without requiring code or GPU expertise. Automatically selects and tunes hyperparameters based on dataset characteristics.","intents":["I want to train a vision model without learning TensorFlow or PyTorch","I need to train a model quickly without GPU knowledge","I want the system to optimize hyperparameters automatically"],"best_for":["non-ML engineers","product teams","SMBs","rapid prototyping teams"],"limitations":["constrained to pre-configured model architectures","no custom neural network design","limited advanced tuning options"],"requires":["annotated and preprocessed dataset","cloud compute resources"],"input_types":["preprocessed image dataset"],"output_types":["trained model weights and configuration"],"categories":["machine-learning","no-code"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_datature__cap_3","uri":"capability://machine.learning.model.performance.comparison.and.versioning","name":"model performance comparison and versioning","description":"Tracks multiple model versions with side-by-side performance metrics (accuracy, precision, recall, mAP). Provides visual dashboards to compare results and select the best performing model.","intents":["I need to compare different model versions to pick the best one","I want to track how my model performance changes over iterations","I need to understand which model version performs best on my metrics"],"best_for":["product teams","iterative development teams","teams evaluating multiple approaches"],"limitations":["limited to Datature-trained models","metrics are predefined"],"requires":["multiple trained models","validation dataset"],"input_types":["trained models"],"output_types":["performance comparison dashboard","metrics reports"],"categories":["machine-learning","analytics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_datature__cap_4","uri":"capability://machine.learning.pre.built.model.template.selection","name":"pre-built model template selection","description":"Offers a library of pre-configured model architectures optimized for common vision tasks (object detection, classification, segmentation). Users select a template matching their use case rather than designing architectures from scratch.","intents":["I need a model for object detection but don't know which architecture to use","I want to start with a proven template rather than designing from scratch","I need a model for image classification without ML expertise"],"best_for":["teams without ML expertise","rapid prototyping","common use cases"],"limitations":["limited to predefined templates","no custom architecture design","templates may not fit niche use cases"],"requires":["understanding of task type (detection/classification/segmentation)"],"input_types":["task specification"],"output_types":["selected model template configuration"],"categories":["machine-learning","no-code"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_datature__cap_5","uri":"capability://deployment.one.click.model.deployment.to.cloud.endpoints","name":"one-click model deployment to cloud endpoints","description":"Deploys trained models to cloud infrastructure with a single action, generating API endpoints for inference without requiring DevOps or containerization knowledge.","intents":["I need to deploy my model to production without Docker or Kubernetes knowledge","I want to generate an API endpoint for my model immediately","I need to make my model accessible to applications without infrastructure setup"],"best_for":["product teams","SMBs","teams without DevOps expertise","rapid deployment scenarios"],"limitations":["limited deployment customization","vendor lock-in to Datature infrastructure","scaling options may be limited"],"requires":["trained model","cloud account or Datature hosting"],"input_types":["trained model"],"output_types":["API endpoint URL","deployment configuration"],"categories":["deployment","no-code"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_datature__cap_6","uri":"capability://inference.batch.inference.on.image.collections","name":"batch inference on image collections","description":"Runs trained models against multiple images in batch mode, processing entire folders or datasets and returning predictions for all images without manual API calls.","intents":["I need to run predictions on hundreds of images at once","I want to process an entire folder of images without writing code","I need batch results exported in a structured format"],"best_for":["teams processing large image collections","quality assurance workflows","data analysis teams"],"limitations":["may have processing time limits","output format options may be limited"],"requires":["trained model","collection of images"],"input_types":["image files or folder paths"],"output_types":["predictions CSV/JSON","annotated images"],"categories":["inference","no-code"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_datature__cap_7","uri":"capability://inference.real.time.inference.via.api","name":"real-time inference via api","description":"Provides REST API endpoints for deployed models to accept image inputs and return predictions in real-time, enabling integration with applications and workflows.","intents":["I need to integrate my model into my application via API","I want real-time predictions for incoming images","I need to build a service that uses my vision model"],"best_for":["developers integrating models into applications","production systems","real-time processing needs"],"limitations":["API rate limits may apply","latency depends on model size and infrastructure"],"requires":["deployed model","API key","image input capability"],"input_types":["image data via HTTP request"],"output_types":["JSON predictions with confidence scores and bounding boxes"],"categories":["inference","api"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_datature__cap_8","uri":"capability://monitoring.model.performance.monitoring.and.drift.detection","name":"model performance monitoring and drift detection","description":"Tracks deployed model performance metrics in production and alerts when prediction accuracy degrades or data distribution shifts occur.","intents":["I need to monitor if my deployed model is still performing well","I want to know when my model's accuracy drops in production","I need to detect when new data differs from training data"],"best_for":["production teams","quality assurance","teams with deployed models"],"limitations":["monitoring features may be limited in freemium tier","alert customization may be restricted"],"requires":["deployed model","production inference data"],"input_types":["production predictions and ground truth labels"],"output_types":["performance dashboards","drift alerts"],"categories":["monitoring","analytics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_datature__cap_9","uri":"capability://collaboration.collaborative.project.workspace.management","name":"collaborative project workspace management","description":"Provides team collaboration features including role-based access control, project organization, and activity tracking for multiple users working on the same vision project.","intents":["I need my team to work on annotation together with proper permissions","I want to organize multiple vision projects in one workspace","I need to track who made changes to annotations and models"],"best_for":["teams","enterprises","collaborative workflows"],"limitations":["freemium tier may limit team size","role customization may be limited"],"requires":["team members with accounts","project setup"],"input_types":["user invitations","project configurations"],"output_types":["shared workspace","activity logs"],"categories":["collaboration","productivity"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":46,"verified":false,"data_access_risk":"high","permissions":["image files in common formats (JPG, PNG)","web browser access","dataset organization","annotated dataset","minimum dataset size","trained model","custom dataset","pre-trained model selection","annotated and preprocessed dataset","cloud compute resources"],"failure_modes":["freemium tier has volume restrictions","limited to 2D image annotation","no support for video frame annotation","limited control over augmentation parameters","no custom preprocessing pipelines","export options may be limited in freemium tier","some model types may not be exportable","analysis may be limited in freemium tier","recommendations are automated and may miss domain-specific issues","limited control over fine-tuning parameters","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.39999999999999997,"quality":0.82,"ecosystem":0.30000000000000004,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:30.282Z","last_scraped_at":"2026-04-05T13:23:42.548Z","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=datature","compare_url":"https://unfragile.ai/compare?artifact=datature"}},"signature":"LO38xX9tGGvCGuAiC0scZasawDvSgZumSOQGcKyPUZKFr/qd/NmjUPlIXaoR98jqTKKyGOxI+u9e3Y8veiVSDQ==","signedAt":"2026-06-20T19:34:09.878Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/datature","artifact":"https://unfragile.ai/datature","verify":"https://unfragile.ai/api/v1/verify?slug=datature","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"}}