{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"albumentations","slug":"albumentations","name":"Albumentations","type":"repo","url":"https://albumentations.ai","page_url":"https://unfragile.ai/albumentations","categories":["image-generation"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"albumentations__cap_0","uri":"capability://data.processing.analysis.composable.image.augmentation.pipeline.construction","name":"composable image augmentation pipeline construction","description":"Declarative pipeline composition via the Compose() abstraction that sequences multiple Transform objects with probability-based stochastic application. Each transform is a stateless strategy that operates on NumPy arrays, enabling reproducible augmentation chains serializable to YAML/JSON for version control and experiment tracking. Transforms are applied sequentially with configurable per-transform probability, allowing fine-grained control over augmentation intensity without modifying source images.","intents":["I want to define a reusable augmentation pipeline that applies multiple transforms in sequence with controlled randomness","I need to save and reload augmentation configurations as code/config files for reproducibility across experiments","I want to apply different augmentation strategies to different dataset splits (train vs validation) without code duplication"],"best_for":["ML engineers building training pipelines for computer vision models","researchers requiring reproducible augmentation for paper submissions","teams managing multiple augmentation strategies across projects"],"limitations":["Pipelines are stateless and single-sample focused — no built-in batch processing or streaming","Serialization to YAML/JSON requires manual pipeline definition; no automatic pipeline discovery or optimization","Probability-based application adds non-determinism; reproducibility requires fixed random seeds"],"requires":["Python 3.7+","NumPy (for array interchange format)","PyYAML (optional, for YAML serialization)"],"input_types":["NumPy arrays (images as uint8 or float32)","YAML/JSON pipeline definitions"],"output_types":["NumPy arrays (augmented images)","YAML/JSON serialized pipelines"],"categories":["data-processing-analysis","pipeline-configuration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"albumentations__cap_1","uri":"capability://data.processing.analysis.spatial.aware.bounding.box.transformation","name":"spatial-aware bounding box transformation","description":"Automatically adjusts axis-aligned bounding box coordinates when spatial transforms (rotation, scaling, perspective, elastic deformation) are applied to images. The framework maintains a target-aware visitor pattern where each spatial transform knows how to recompute bbox coordinates in the transformed coordinate space, preserving annotation validity without manual recalculation. Supports both standard axis-aligned bboxes and oriented bounding boxes (OBB) for rotated object detection.","intents":["I need to augment object detection training data while keeping bounding box annotations synchronized with image transformations","I want to apply perspective or rotation transforms without manually recalculating bbox coordinates","I'm training a rotated object detector and need OBB coordinates updated during augmentation"],"best_for":["computer vision engineers training object detection models (YOLO, Faster R-CNN, RetinaNet)","teams working with rotated object detection in aerial/satellite imagery","researchers requiring pixel-perfect annotation alignment during augmentation"],"limitations":["Bbox transformation assumes perspective transforms preserve rectangular shapes — non-affine transforms may produce invalid bboxes","No built-in validation for out-of-bounds or zero-area bboxes after transformation; requires post-processing","OBB support mentioned but implementation details unknown; may have limitations with extreme rotation angles"],"requires":["Python 3.7+","NumPy","Bounding box coordinates in [x_min, y_min, x_max, y_max] or OBB format"],"input_types":["NumPy arrays (images)","Bounding box arrays (N x 4 for axis-aligned, N x 5+ for OBB)"],"output_types":["NumPy arrays (augmented images)","Transformed bounding box arrays with updated coordinates"],"categories":["data-processing-analysis","annotation-preservation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"albumentations__cap_10","uri":"capability://tool.use.integration.dual.license.model.with.commercial.support","name":"dual-license model with commercial support","description":"Offers dual licensing: open-source AGPL-3.0 for research and open-source projects, and commercial AlbumentationsX license for proprietary use without source disclosure requirements. Commercial license includes priority support, unlimited developers/products/deployments, and HIPAA compliance guarantees. Pricing is contact-based and flexible based on company size and use case, with 1 business day response time for sales inquiries.","intents":["I need to use Albumentations in a proprietary product without open-sourcing my code","I want commercial support and HIPAA compliance guarantees for production medical imaging systems","I need a flexible licensing model that scales with my company size and deployment scale"],"best_for":["commercial software companies building proprietary computer vision products","healthcare organizations requiring HIPAA compliance","enterprises needing priority support and SLAs"],"limitations":["Open-source AGPL-3.0 version requires source disclosure if used in proprietary software; incompatible with MIT/Apache/BSD licenses","Commercial licensing requires contacting sales; no self-serve pricing or instant activation","Commercial license cost is unknown without contacting sales; may be prohibitive for small teams","License enforcement is legal-only; no technical keys or DRM to prevent unauthorized use"],"requires":["Legal review of AGPL-3.0 vs commercial license implications","Sales contact for commercial license pricing and terms"],"input_types":[],"output_types":[],"categories":["tool-use-integration","licensing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"albumentations__cap_11","uri":"capability://data.processing.analysis.multi.task.augmentation.for.classification.detection.segmentation.and.keypoint.tasks","name":"multi-task augmentation for classification, detection, segmentation, and keypoint tasks","description":"Unified augmentation framework that handles multiple computer vision tasks simultaneously through target-aware transform application. Single pipeline definition works for classification (image-only), object detection (image + bbox), semantic segmentation (image + mask), instance segmentation (image + mask + bbox), and keypoint detection (image + keypoint) by routing transforms to appropriate target handlers. Eliminates need for task-specific augmentation code.","intents":["I want to use the same augmentation pipeline for multiple tasks (detection and segmentation) without duplicating code","I need to augment multi-task datasets where images have both bboxes and masks and keypoints","I want to switch between tasks without rewriting augmentation logic"],"best_for":["multi-task learning teams building models for multiple objectives","researchers working with richly-annotated datasets","organizations with diverse computer vision use cases"],"limitations":["Requires all target types to be present in data; missing targets must be handled explicitly","Transform selection must be compatible with all target types in use; some transforms may not support all targets","Debugging multi-task pipelines is more complex due to multiple target types","No built-in task-specific parameter tuning; same parameters apply across all tasks"],"requires":["Python 3.7+","NumPy","Annotations for all target types (image, mask, bbox, keypoint as needed)"],"input_types":["NumPy arrays (images)","NumPy arrays (masks, optional)","Bounding box arrays (optional)","Keypoint arrays (optional)"],"output_types":["NumPy arrays (augmented images)","NumPy arrays (augmented masks, optional)","Transformed bounding box arrays (optional)","Transformed keypoint arrays (optional)"],"categories":["data-processing-analysis","multi-task-learning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"albumentations__cap_2","uri":"capability://data.processing.analysis.keypoint.preserving.coordinate.transformation","name":"keypoint-preserving coordinate transformation","description":"Maintains keypoint (landmark) coordinate validity during spatial augmentations by applying the same geometric transformation to keypoint coordinates as applied to the image. The framework tracks keypoint positions through rotation, scaling, perspective, and elastic deformation transforms, recomputing coordinates in the transformed space while handling edge cases like points moving outside image bounds. Supports multi-keypoint objects with per-keypoint visibility flags.","intents":["I need to augment pose estimation training data while keeping joint/keypoint coordinates aligned with the transformed image","I want to apply rotation and scaling to facial landmark detection datasets without manually recalculating point positions","I'm training a multi-person pose detector and need keypoint coordinates updated consistently across augmentations"],"best_for":["pose estimation engineers (human pose, hand pose, animal pose)","facial landmark detection teams","researchers building keypoint-based computer vision models"],"limitations":["Keypoint transformation assumes points remain valid after transform — no automatic handling of occluded or out-of-bounds points","Visibility flags must be manually managed; framework doesn't infer occlusion from augmentation","Elastic deformation and other non-affine transforms may produce inaccurate keypoint positions due to interpolation approximations"],"requires":["Python 3.7+","NumPy","Keypoint coordinates in [x, y] or [x, y, visibility] format"],"input_types":["NumPy arrays (images)","Keypoint arrays (N x 2 or N x 3 for visibility)"],"output_types":["NumPy arrays (augmented images)","Transformed keypoint arrays with updated coordinates"],"categories":["data-processing-analysis","annotation-preservation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"albumentations__cap_3","uri":"capability://data.processing.analysis.semantic.segmentation.mask.aware.augmentation","name":"semantic segmentation mask-aware augmentation","description":"Applies spatial and pixel-level transforms to segmentation masks in perfect alignment with image augmentations, preserving class label integrity and mask topology. The framework treats masks as a distinct target type with specialized handling: spatial transforms use nearest-neighbor interpolation to preserve discrete class labels (avoiding label bleeding), while pixel-level transforms apply identically to masks. Supports multi-channel masks for multi-class segmentation and instance segmentation scenarios.","intents":["I need to augment semantic segmentation training data while keeping pixel-level class labels perfectly aligned with image transforms","I want to apply rotation and scaling to segmentation masks without interpolation artifacts that blur class boundaries","I'm training an instance segmentation model and need per-instance masks updated during augmentation"],"best_for":["semantic segmentation engineers (medical imaging, autonomous driving, scene understanding)","instance segmentation teams","researchers building pixel-level prediction models"],"limitations":["Nearest-neighbor interpolation for spatial transforms may produce jagged edges on rotated masks; no anti-aliasing option","Multi-channel masks require manual channel management; no built-in support for class-specific transform parameters","Large masks (e.g., 4K images) may have memory overhead during transformation; no streaming or tiled processing"],"requires":["Python 3.7+","NumPy","Segmentation masks as uint8 or uint16 arrays with class indices"],"input_types":["NumPy arrays (images as uint8/float32)","NumPy arrays (masks as uint8/uint16 with class indices)"],"output_types":["NumPy arrays (augmented images)","NumPy arrays (augmented masks with preserved class labels)"],"categories":["data-processing-analysis","annotation-preservation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"albumentations__cap_4","uri":"capability://data.processing.analysis.70.optimized.transformation.library.with.pixel.and.spatial.operations","name":"70+ optimized transformation library with pixel and spatial operations","description":"Provides a curated library of 70+ pre-implemented augmentation transforms covering pixel-level operations (brightness, contrast, color shifts, noise injection) and spatial operations (rotation, scaling, perspective, elastic deformation, morphological operations). Each transform is implemented in optimized C/C++ or NumPy with minimal Python overhead, enabling fast augmentation during training. Transforms are parameterized with sensible defaults and support both deterministic and stochastic application via probability parameters.","intents":["I want to apply standard augmentations (rotation, brightness, noise) without implementing them from scratch","I need a library of well-tested transforms with known performance characteristics for production training pipelines","I want to discover and experiment with different augmentation strategies without writing custom code"],"best_for":["ML practitioners building production computer vision models","researchers prototyping augmentation strategies quickly","teams without specialized image processing expertise"],"limitations":["Fixed set of 70+ transforms; custom transforms require subclassing Transform base class","Performance varies by transform — some pixel-level operations (e.g., complex color shifts) may be slower than hand-optimized code","Transform parameters are fixed at pipeline definition time; no dynamic parameter adjustment during training"],"requires":["Python 3.7+","NumPy","Optional: OpenCV (cv2) for some transforms, scikit-image for others"],"input_types":["NumPy arrays (images as uint8 or float32)"],"output_types":["NumPy arrays (augmented images)"],"categories":["data-processing-analysis","image-transformation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"albumentations__cap_5","uri":"capability://tool.use.integration.framework.agnostic.numpy.based.integration","name":"framework-agnostic numpy-based integration","description":"Operates on NumPy arrays as the universal interchange format, enabling seamless integration with PyTorch, TensorFlow, Keras, and any other framework that can convert to/from NumPy. No tight coupling to specific frameworks — transforms consume and produce NumPy arrays, allowing users to integrate Albumentations into existing pipelines via simple array conversion. Supports integration with PyTorch DataLoader and TensorFlow Dataset APIs through wrapper functions.","intents":["I want to use the same augmentation pipeline across PyTorch and TensorFlow projects without rewriting code","I need to integrate augmentation into my existing training loop without framework-specific dependencies","I want to switch ML frameworks without losing my augmentation pipeline investment"],"best_for":["teams using multiple ML frameworks","researchers prototyping across PyTorch and TensorFlow","organizations with heterogeneous ML infrastructure"],"limitations":["NumPy array conversion adds latency (~1-5ms per image depending on size) compared to native framework tensors","No native GPU acceleration — augmentation runs on CPU; GPU tensors must be converted to NumPy and back","Framework-specific optimizations (e.g., PyTorch's autograd) are not available for augmentation"],"requires":["Python 3.7+","NumPy","PyTorch (torch) or TensorFlow (tensorflow) for framework integration (optional)"],"input_types":["NumPy arrays","PyTorch tensors (via .numpy() conversion)","TensorFlow tensors (via .numpy() conversion)"],"output_types":["NumPy arrays","PyTorch tensors (via torch.from_numpy())","TensorFlow tensors (via tf.convert_to_tensor())"],"categories":["tool-use-integration","framework-agnostic"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"albumentations__cap_6","uri":"capability://code.generation.editing.custom.transform.creation.via.inheritance","name":"custom transform creation via inheritance","description":"Enables extension of the augmentation library by subclassing the Transform base class and implementing target-specific methods (apply, apply_to_mask, apply_to_bbox, apply_to_keypoint). Custom transforms integrate seamlessly into Compose() pipelines and inherit probability-based application, serialization support, and target-aware handling. Documentation explicitly supports custom transform creation as a primary extension mechanism.","intents":["I need to implement a domain-specific augmentation not in the standard library","I want to create a custom transform that respects bounding boxes and keypoints like built-in transforms","I need to integrate proprietary augmentation algorithms into my pipeline"],"best_for":["researchers implementing novel augmentation techniques","teams with domain-specific augmentation requirements (medical imaging, satellite imagery)","organizations building proprietary augmentation IP"],"limitations":["Requires understanding of Transform base class API and target-specific method signatures","No plugin registry or discovery mechanism — custom transforms must be manually imported","Debugging custom transforms requires understanding internal coordinate transformation logic","No automatic validation of custom transform correctness for all target types"],"requires":["Python 3.7+","NumPy","Understanding of Albumentations Transform API","Knowledge of coordinate transformation for bbox/keypoint support"],"input_types":["NumPy arrays (images)","Optional: masks, bboxes, keypoints depending on implementation"],"output_types":["NumPy arrays (augmented images)","Optional: transformed masks, bboxes, keypoints"],"categories":["code-generation-editing","extensibility"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"albumentations__cap_7","uri":"capability://automation.workflow.yaml.json.pipeline.serialization.and.versioning","name":"yaml/json pipeline serialization and versioning","description":"Serializes augmentation pipelines to YAML or JSON format, enabling version control, experiment tracking, and reproducibility without code changes. Pipeline definitions capture transform types, parameters, and probability settings in human-readable format, allowing non-technical stakeholders to understand augmentation strategies and enabling easy comparison of different augmentation approaches. Deserialization reconstructs identical pipelines from saved configurations.","intents":["I want to version control my augmentation pipelines alongside model checkpoints for reproducibility","I need to compare different augmentation strategies by examining their YAML/JSON definitions","I want to share augmentation configurations with team members without sharing Python code"],"best_for":["ML teams practicing experiment tracking and reproducibility","researchers publishing models with augmentation specifications","organizations with non-technical stakeholders reviewing augmentation strategies"],"limitations":["Serialization only captures standard transforms; custom transforms require manual code versioning","YAML/JSON format is verbose for complex pipelines with many transforms","No built-in diffing or comparison tools for pipeline versions","Deserialization requires transform classes to be importable; refactoring transform names breaks compatibility"],"requires":["Python 3.7+","PyYAML (for YAML serialization)","NumPy"],"input_types":["Python Compose() objects","YAML/JSON pipeline definitions"],"output_types":["YAML/JSON files","Python Compose() objects"],"categories":["automation-workflow","reproducibility"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"albumentations__cap_8","uri":"capability://data.processing.analysis.medical.imaging.augmentation.with.hipaa.compliance","name":"medical imaging augmentation with hipaa compliance","description":"Provides augmentation transforms optimized for medical imaging workflows with HIPAA-compliant processing (no data transmission, local-only computation). Supports medical imaging-specific requirements like preservation of Hounsfield units in CT scans, handling of 3D volumetric data, and augmentation of multi-modal imaging (CT, MRI, X-ray). Commercial license explicitly covers medical imaging use cases with compliance guarantees.","intents":["I need to augment medical imaging datasets while maintaining HIPAA compliance and data privacy","I want to apply augmentation to 3D volumetric CT/MRI data without external processing services","I need augmentation strategies that preserve medical imaging properties (Hounsfield units, intensity ranges)"],"best_for":["medical imaging teams building diagnostic AI models","healthcare organizations with strict data privacy requirements","researchers working with sensitive patient data"],"limitations":["Medical imaging-specific transforms may require domain expertise to configure correctly","3D volumetric augmentation may have high memory requirements for large datasets","HIPAA compliance is a commercial license feature; open-source AGPL version lacks compliance guarantees","No built-in DICOM support; requires conversion to NumPy arrays"],"requires":["Python 3.7+","NumPy","Commercial license for HIPAA compliance guarantees","DICOM conversion tools (external) for DICOM file support"],"input_types":["NumPy arrays (medical images as float32 or uint16)","3D volumetric arrays"],"output_types":["NumPy arrays (augmented medical images)","3D volumetric arrays"],"categories":["data-processing-analysis","domain-specific"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"albumentations__cap_9","uri":"capability://data.processing.analysis.3d.volumetric.and.video.frame.augmentation","name":"3d volumetric and video frame augmentation","description":"Extends augmentation beyond 2D images to 3D volumetric data (CT scans, MRI volumes) and video sequences by applying consistent spatial transforms across all frames/slices. Supports temporal consistency where the same transform parameters are applied to each frame in a video or each slice in a volume, preserving temporal/spatial coherence. Enables augmentation of 3D object detection and video understanding datasets.","intents":["I need to augment 3D medical imaging volumes while maintaining consistency across all slices","I want to apply augmentation to video datasets while keeping the same transform across all frames","I'm training a 3D object detector and need consistent spatial transforms across the entire volume"],"best_for":["medical imaging teams working with 3D CT/MRI data","video understanding researchers","3D object detection engineers"],"limitations":["3D augmentation memory overhead scales with volume size; no tiled or streaming processing","Temporal consistency requires applying identical transforms to all frames; no frame-specific variations","Video augmentation requires pre-loading entire video into memory; no streaming frame processing","Implementation details for 3D support are unclear from documentation"],"requires":["Python 3.7+","NumPy","Sufficient memory for 3D volumes or video sequences"],"input_types":["3D NumPy arrays (volumetric data as float32/uint16)","Video frame sequences (list of NumPy arrays)"],"output_types":["3D NumPy arrays (augmented volumes)","Video frame sequences (list of augmented frames)"],"categories":["data-processing-analysis","video-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"albumentations__headline","uri":"capability://image.visual.image.augmentation.library.for.machine.learning","name":"image augmentation library for machine learning","description":"Albumentations is a fast and flexible image augmentation library that provides over 70 transformations optimized for performance, supporting various computer vision tasks like classification, segmentation, and detection with composable pipelines.","intents":["best image augmentation library","image augmentation for deep learning","top tools for image preprocessing","image transformation library for machine learning","best library for computer vision augmentation"],"best_for":["computer vision tasks","deep learning models"],"limitations":["requires a commercial license for proprietary use"],"requires":["Python","NumPy"],"input_types":["images","segmentation masks","bounding boxes"],"output_types":["augmented images","augmented masks"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"low","permissions":["Python 3.7+","NumPy (for array interchange format)","PyYAML (optional, for YAML serialization)","NumPy","Bounding box coordinates in [x_min, y_min, x_max, y_max] or OBB format","Legal review of AGPL-3.0 vs commercial license implications","Sales contact for commercial license pricing and terms","Annotations for all target types (image, mask, bbox, keypoint as needed)","Keypoint coordinates in [x, y] or [x, y, visibility] format","Segmentation masks as uint8 or uint16 arrays with class indices"],"failure_modes":["Pipelines are stateless and single-sample focused — no built-in batch processing or streaming","Serialization to YAML/JSON requires manual pipeline definition; 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