{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"ultralytics","slug":"ultralytics","name":"Ultralytics","type":"repo","url":"https://github.com/ultralytics/ultralytics","page_url":"https://unfragile.ai/ultralytics","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"ultralytics__cap_0","uri":"capability://image.visual.unified.multi.task.vision.model.inference.with.autobackend.runtime.abstraction","name":"unified multi-task vision model inference with autobackend runtime abstraction","description":"Provides a single YOLO model class that abstracts inference across detection, segmentation, classification, pose estimation, and OBB tasks through a unified predict() interface. Internally uses AutoBackend to dynamically select optimal inference runtime (PyTorch, ONNX, TensorRT, CoreML, OpenVINO, etc.) based on exported model format and hardware availability, eliminating need for task-specific inference code. The Results object standardizes output across all tasks with unified annotation and visualization methods.","intents":["I want to load a YOLO model once and run inference on multiple vision tasks without rewriting inference code","I need to switch between CPU, GPU, and edge device inference without changing application code","I want to export a model to multiple formats and have the framework automatically select the best runtime at inference time"],"best_for":["computer vision engineers building multi-task pipelines","production teams deploying models across heterogeneous hardware","developers migrating from task-specific frameworks to unified APIs"],"limitations":["AutoBackend selection is deterministic but not always optimal for mixed workloads — may require manual runtime specification for performance tuning","Results object abstraction adds ~5-15ms overhead per inference due to post-processing standardization","Some advanced task-specific optimizations (e.g., custom NMS variants) are not exposed through unified API"],"requires":["Python 3.8+","PyTorch 1.7+ OR ONNX Runtime OR TensorRT (format-dependent)","Pre-trained YOLO model weights or custom trained model"],"input_types":["image file paths (str)","numpy arrays (uint8, float32)","PIL Image objects","video file paths","camera streams (OpenCV VideoCapture)"],"output_types":["Results object with boxes, masks, keypoints, or class probabilities","annotated images (numpy arrays)","JSON serializable detection dictionaries"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ultralytics__cap_1","uri":"capability://automation.workflow.end.to.end.model.training.pipeline.with.configuration.driven.hyperparameter.management","name":"end-to-end model training pipeline with configuration-driven hyperparameter management","description":"Implements a complete training loop (Trainer class) that orchestrates data loading, forward passes, loss computation, backward passes, and validation checkpointing. Uses YAML-based configuration files (ultralytics/cfg/) to define hyperparameters, augmentation strategies, and training schedules without code changes. Integrates callback system for extensibility (logging, early stopping, learning rate scheduling, platform integrations). Supports distributed training via PyTorch DDP and automatic mixed precision (AMP) for memory efficiency.","intents":["I want to train a YOLO model on custom data without writing training loops","I need to experiment with different hyperparameters and augmentation strategies using config files","I want to integrate training with external platforms (Weights & Biases, Ultralytics HUB, TensorBoard) via callbacks"],"best_for":["ML engineers training custom object detection models","teams managing hyperparameter experiments across multiple runs","researchers integrating YOLO into larger training pipelines"],"limitations":["YAML config system is rigid for complex custom loss functions — requires subclassing Trainer for non-standard objectives","Distributed training (DDP) requires manual process spawning; no built-in multi-node orchestration","Callback system adds ~2-5% training time overhead due to hook invocations at each epoch"],"requires":["Python 3.8+","PyTorch 1.7+ with CUDA 11.0+ (for GPU training)","Dataset in YOLO format (images + txt annotations) or convertible format","8GB+ GPU memory for standard model sizes"],"input_types":["YAML configuration files","image directories with corresponding label files","dataset paths (coco.yaml, data.yaml)"],"output_types":["trained model weights (.pt files)","training metrics (CSV logs)","validation results (mAP, precision, recall)","checkpoint files for resuming training"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ultralytics__cap_10","uri":"capability://image.visual.interactive.dataset.explorer.with.filtering.and.visualization","name":"interactive dataset explorer with filtering and visualization","description":"Explorer GUI provides interactive browsing of datasets with filtering by class, annotation type, and image properties. Built on Gradio for web-based UI and supports local or remote dataset paths. Enables visual inspection of annotations, detection of labeling errors, and dataset statistics (class distribution, image sizes). Can be launched via CLI (yolo explorer) or Python API.","intents":["I want to visually inspect my dataset to check for labeling errors before training","I need to understand class distribution and identify imbalanced classes","I want to filter and view specific subsets of my dataset (e.g., images with small objects)"],"best_for":["data engineers validating dataset quality","teams identifying and fixing labeling errors","researchers analyzing dataset characteristics"],"limitations":["Explorer is read-only — cannot edit annotations directly in UI","Performance degrades with very large datasets (100k+ images) due to Gradio limitations","No built-in export of filtered subsets","Requires local or network access to dataset files (no cloud storage integration)"],"requires":["Python 3.8+","Gradio library","Dataset in YOLO format (images + labels)"],"input_types":["dataset directory paths","YOLO format annotations"],"output_types":["interactive web UI","dataset statistics (JSON)","filtered image lists"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ultralytics__cap_11","uri":"capability://data.processing.analysis.benchmark.mode.for.performance.profiling.across.hardware.and.formats","name":"benchmark mode for performance profiling across hardware and formats","description":"Benchmark utility profiles model inference speed, memory usage, and accuracy across different hardware (CPU, GPU, TPU) and export formats (PyTorch, ONNX, TensorRT, CoreML, etc.). Measures latency (ms/image), throughput (images/sec), and memory footprint (MB). Generates comparison tables and plots. Can be run via CLI (yolo benchmark) or Python API.","intents":["I want to compare inference speed of different export formats to choose the best for deployment","I need to measure memory usage to ensure the model fits on edge devices","I want to profile model performance across different hardware (CPU vs GPU vs TPU)"],"best_for":["ML engineers optimizing models for production deployment","teams selecting hardware for inference (CPU vs GPU vs edge devices)","researchers comparing inference frameworks"],"limitations":["Benchmark results are hardware-specific and may not generalize to different devices","Warm-up runs are required for accurate GPU measurements (adds 10-30s overhead)","Memory profiling is approximate and may not capture peak memory usage","No support for batch inference profiling (single-image benchmarks only)"],"requires":["Python 3.8+","Trained YOLO model","Format-specific dependencies (onnx, tensorrt, coremltools, etc.)","Optional: GPU for GPU benchmarking"],"input_types":["trained model (.pt file)","export formats to benchmark","hardware specifications"],"output_types":["benchmark results (CSV, JSON)","comparison plots (latency, throughput, memory)","formatted tables (markdown, HTML)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ultralytics__cap_12","uri":"capability://code.generation.editing.neural.network.architecture.customization.via.yaml.task.definitions","name":"neural network architecture customization via yaml task definitions","description":"Neural network architectures are defined in YAML files (ultralytics/cfg/models/) that specify layer types, connections, and parameters. Task-specific heads (DetectionHead, SegmentationHead, PoseHead, ClassificationHead) are selected based on task type. Custom architectures can be created by modifying YAML files without touching Python code. Backbone, neck, and head components are modular and can be mixed-and-matched.","intents":["I want to create a custom YOLO architecture by modifying YAML config without writing Python code","I need to experiment with different backbone and head combinations","I want to reduce model size by removing unnecessary layers"],"best_for":["researchers experimenting with architecture designs","engineers optimizing models for specific hardware constraints","teams building domain-specific model variants"],"limitations":["YAML syntax is rigid — complex custom operations require Python subclassing","No automatic architecture search (NAS) — manual experimentation required","Limited documentation on architecture design best practices","Changing architecture requires retraining from scratch (no transfer learning from different architectures)"],"requires":["Python 3.8+","Understanding of YOLO architecture (backbone, neck, head)","YAML editing capability"],"input_types":["YAML architecture definition files","layer specifications (type, channels, kernel size, etc.)"],"output_types":["custom YOLO model class","model summary (layer counts, parameters)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ultralytics__cap_13","uri":"capability://image.visual.results.object.with.unified.output.format.and.visualization.methods","name":"results object with unified output format and visualization methods","description":"Results class standardizes output across all vision tasks (detection, segmentation, classification, pose, OBB) with unified attributes (boxes, masks, keypoints, probs, etc.). Provides visualization methods (plot(), show(), save()) that handle task-specific rendering (bounding boxes, masks, keypoints, class labels). Results are JSON-serializable for API responses. Supports filtering and post-processing (NMS, confidence thresholding) on Results objects.","intents":["I want a consistent output format across different vision tasks (detection, segmentation, pose)","I need to visualize predictions with task-specific overlays (boxes, masks, keypoints)","I want to serialize predictions to JSON for API responses"],"best_for":["developers building multi-task vision applications","teams standardizing output formats across different models","API developers serializing predictions for REST endpoints"],"limitations":["Results object is immutable — filtering creates new objects, adding memory overhead","Visualization methods are CPU-bound and can be slow for large images (4K+)","JSON serialization loses some information (e.g., confidence scores are rounded)","No built-in support for custom attributes — requires subclassing Results"],"requires":["Python 3.8+","YOLO inference output"],"input_types":["YOLO model predictions (boxes, masks, keypoints, etc.)","original images (for visualization)"],"output_types":["Results objects with unified attributes","annotated images (numpy arrays)","JSON serialized predictions","visualization plots"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ultralytics__cap_2","uri":"capability://automation.workflow.multi.format.model.export.with.quantization.and.optimization","name":"multi-format model export with quantization and optimization","description":"Exporter class converts trained PyTorch models to 10+ deployment formats (ONNX, TensorRT, CoreML, OpenVINO, NCNN, Paddle, etc.) with optional quantization (INT8, FP16) and graph optimization. Each exporter subclass handles format-specific preprocessing (input normalization, shape inference, operator mapping). Validates exported models against original PyTorch outputs to ensure numerical consistency. Generates platform-specific deployment code snippets and metadata.","intents":["I want to export a trained YOLO model to edge-friendly formats (TensorRT, CoreML, NCNN) with minimal latency","I need to quantize models for mobile/embedded deployment while maintaining accuracy","I want to validate that exported models produce identical outputs to the original PyTorch model"],"best_for":["ML engineers optimizing models for production deployment","embedded systems developers targeting mobile/edge hardware","teams requiring model portability across inference frameworks"],"limitations":["Quantization is post-training only — no quantization-aware training (QAT) support built-in","Some advanced PyTorch operations (dynamic shapes, custom ops) may not export cleanly to all formats","Export validation requires running inference on sample data, adding 30-60s overhead per format","TensorRT export requires NVIDIA GPU and CUDA toolkit installed"],"requires":["Python 3.8+","PyTorch trained model (.pt file)","Format-specific dependencies (onnx, onnxruntime, tensorrt, coremltools, openvino-dev, etc.)","For TensorRT: NVIDIA GPU with CUDA 11.0+"],"input_types":["trained PyTorch model (.pt file)","model configuration (task type, input shape)"],"output_types":["exported model files (.onnx, .engine, .mlmodel, .xml, .bin, etc.)","metadata JSON (input/output shapes, normalization params)","deployment code snippets (Python, C++, Java)"],"categories":["automation-workflow","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ultralytics__cap_3","uri":"capability://image.visual.real.time.object.tracking.with.configurable.tracker.algorithms","name":"real-time object tracking with configurable tracker algorithms","description":"Integrates tracker algorithms (BoT-SORT, ByteTrack, DeepSORT) that maintain object identity across video frames by associating detections using appearance features and motion models. Tracker class wraps detection pipeline and applies Hungarian algorithm for frame-to-frame assignment. Supports custom distance metrics (Euclidean, cosine, Mahalanobis) and configurable association thresholds. Outputs track IDs alongside bounding boxes and segmentation masks.","intents":["I want to track objects across video frames while maintaining consistent IDs","I need to count or monitor specific objects over time in video streams","I want to use different tracking algorithms (BoT-SORT vs ByteTrack) without changing application code"],"best_for":["video analytics engineers building surveillance or monitoring systems","developers implementing object counting or trajectory analysis","teams requiring multi-object tracking with identity preservation"],"limitations":["Tracker performance degrades with occlusions and fast-moving objects — no built-in occlusion handling","Association thresholds are global; no per-class or per-object adaptive thresholds","Appearance features (ReID) are not learned end-to-end — uses generic CNN features or hand-crafted metrics","Tracking adds 10-30ms per frame overhead depending on number of objects and tracker complexity"],"requires":["Python 3.8+","Video input (file path or camera stream)","YOLO detection model","Optional: pre-trained ReID model for appearance features"],"input_types":["video file paths","camera streams (OpenCV VideoCapture)","image sequences","detection results from YOLO inference"],"output_types":["Results objects with track IDs added to boxes","annotated video frames with track trails","track history (frame-by-frame ID assignments)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ultralytics__cap_4","uri":"capability://data.processing.analysis.dataset.format.conversion.and.standardization","name":"dataset format conversion and standardization","description":"Converter utilities transform between common dataset formats (COCO, Pascal VOC, YOLO txt, Roboflow, etc.) and standardize annotations into YOLO format. Handles bounding box coordinate system conversions (normalized vs pixel, COCO vs YOLO), class remapping, and image resizing. Dataset class provides lazy-loading interface with caching to avoid redundant I/O. Supports streaming from cloud storage (S3, GCS) via fsspec integration.","intents":["I have a dataset in COCO format and need to convert it to YOLO format for training","I want to merge multiple datasets in different formats into a single training set","I need to load large datasets efficiently without loading all images into memory"],"best_for":["data engineers preparing datasets for YOLO training","teams working with multi-source datasets in heterogeneous formats","researchers building custom datasets from public benchmarks"],"limitations":["Conversion is lossy for some formats (e.g., COCO panoptic segmentation → YOLO instance masks loses stuff classes)","No built-in handling for 3D bounding boxes or point clouds — 2D formats only","Class remapping requires manual specification; no automatic semantic alignment","Cloud storage loading adds 50-200ms latency per image vs local disk"],"requires":["Python 3.8+","Source dataset files (images + annotations)","Optional: cloud credentials (AWS, GCP) for remote datasets"],"input_types":["COCO JSON annotation files","Pascal VOC XML files","YOLO txt label files","image directories"],"output_types":["YOLO format directory structure (images/, labels/)","data.yaml configuration file","converted annotation files"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ultralytics__cap_5","uri":"capability://data.processing.analysis.data.augmentation.with.composition.and.on.the.fly.application","name":"data augmentation with composition and on-the-fly application","description":"Augmentation system applies geometric (rotation, flip, perspective, mosaic) and photometric (brightness, contrast, saturation, blur) transformations during training via Albumentations integration. Augmentations are composed into pipelines defined in YAML config and applied on-the-fly during data loading (GPU-accelerated where possible). Mosaic augmentation (combining 4 images) and mixup are implemented as custom ops. Augmentation parameters are randomized per batch to increase diversity.","intents":["I want to apply consistent augmentation strategies across training without hardcoding transforms","I need to use advanced augmentations like mosaic and mixup to improve model robustness","I want to experiment with different augmentation pipelines by changing config files"],"best_for":["ML engineers training robust detection models on small datasets","teams experimenting with augmentation strategies for domain adaptation","researchers studying the impact of augmentation on model generalization"],"limitations":["Augmentation is applied only during training; inference uses no augmentation (no test-time augmentation by default)","Mosaic augmentation requires 4 images per sample, increasing memory usage by ~4x during that batch","Some augmentations (perspective, mosaic) are CPU-bound and can become bottleneck with large batches","Custom augmentations require subclassing BaseTransform; Albumentations integration is read-only"],"requires":["Python 3.8+","PyTorch DataLoader for batching","Albumentations library (optional, for advanced transforms)"],"input_types":["image arrays (numpy, uint8)","bounding box annotations (normalized or pixel coordinates)","segmentation masks (binary or multi-class)"],"output_types":["augmented image arrays","transformed bounding boxes","transformed segmentation masks"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ultralytics__cap_6","uri":"capability://data.processing.analysis.validation.and.metric.computation.with.task.specific.evaluation","name":"validation and metric computation with task-specific evaluation","description":"Validator class computes task-specific metrics during training and inference: mAP (mean Average Precision) for detection, mIoU (mean Intersection over Union) for segmentation, accuracy for classification, OKS (Object Keypoint Similarity) for pose, and mAP for OBB. Uses COCO API for mAP computation with configurable IoU thresholds. Generates per-class metrics and confusion matrices. Integrates with callback system for custom metric logging and early stopping.","intents":["I want to evaluate my model on a validation set and get standard metrics (mAP, precision, recall)","I need per-class performance metrics to identify which classes are underperforming","I want to use validation metrics to implement early stopping during training"],"best_for":["ML engineers evaluating model performance on standard benchmarks","teams monitoring per-class metrics for class imbalance issues","researchers comparing models using standard evaluation protocols"],"limitations":["mAP computation uses COCO API which is slow for large datasets (100k+ images) — can add 5-10 minutes per epoch","Per-class metrics are not computed for classes with <5 samples — no statistical significance testing","Confusion matrices are not generated by default; requires custom callback","No support for custom metrics without subclassing Validator"],"requires":["Python 3.8+","Validation dataset with ground truth annotations","COCO API (pycocotools) for mAP computation"],"input_types":["validation image paths","ground truth annotations (YOLO format or COCO JSON)","model predictions (Results objects)"],"output_types":["mAP scores (mAP50, mAP50-95)","per-class precision and recall","confusion matrices (optional)","validation plots (PR curves, confusion matrix heatmaps)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ultralytics__cap_7","uri":"capability://automation.workflow.command.line.interface.for.training.validation.and.inference","name":"command-line interface for training, validation, and inference","description":"CLI module provides command-line access to all YOLO operations (train, val, predict, export, track) without writing Python code. Uses argparse to parse arguments and maps them to Python API calls. Supports both positional arguments (model, data) and flag-based options (--epochs, --batch-size, --device). Config files can be passed via --cfg flag to override defaults. CLI is auto-generated from Python function signatures.","intents":["I want to train a YOLO model from the command line without writing Python code","I need to run inference on images/videos using a trained model via CLI","I want to export a model to multiple formats using a single command"],"best_for":["data scientists and non-engineers using YOLO for quick prototyping","DevOps engineers integrating YOLO into CI/CD pipelines","researchers running batch experiments via shell scripts"],"limitations":["CLI is less flexible than Python API — complex custom workflows require Python code","Error messages from CLI are less informative than Python tracebacks","No built-in support for piping results between commands (e.g., train → export → inference)","Config file merging with CLI flags can be ambiguous (unclear precedence)"],"requires":["Python 3.8+","Ultralytics package installed (pip install ultralytics)","YOLO model weights or training data"],"input_types":["command-line arguments (strings)","YAML config files","model paths","image/video file paths"],"output_types":["trained model weights","inference results (images, JSON)","validation metrics (printed to stdout)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ultralytics__cap_8","uri":"capability://image.visual.pre.built.computer.vision.solutions.with.task.specific.templates","name":"pre-built computer vision solutions with task-specific templates","description":"Solutions framework provides ready-to-use templates for common CV applications (people counting, parking space detection, safety helmet detection, etc.) that combine YOLO detection with domain-specific post-processing. Each solution is a Python class that wraps YOLO inference and adds custom logic (e.g., line crossing detection, zone-based counting). Solutions can be deployed as standalone scripts or integrated into larger applications via Python API.","intents":["I want to quickly build a people counting application without implementing counting logic from scratch","I need a safety helmet detection solution that alerts when workers are not wearing helmets","I want to deploy a pre-built solution to production with minimal customization"],"best_for":["non-technical users building quick CV applications","teams deploying standard CV tasks (counting, detection, segmentation) to production","integrators building customer-specific solutions on top of YOLO"],"limitations":["Solutions are opinionated and may not fit all use cases — customization requires code changes","Performance is not optimized for real-time processing on edge devices","Solutions are tightly coupled to YOLO detection; cannot use other detectors","Limited documentation for extending solutions with custom post-processing"],"requires":["Python 3.8+","YOLO detection model","Video input (file or camera stream)"],"input_types":["video file paths","camera streams","image sequences"],"output_types":["annotated video frames with solution-specific overlays","metrics (counts, alerts, zone occupancy)","JSON logs of events"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ultralytics__cap_9","uri":"capability://tool.use.integration.ultralytics.hub.integration.for.cloud.based.model.management.and.training","name":"ultralytics hub integration for cloud-based model management and training","description":"HUB integration enables uploading datasets and models to Ultralytics cloud platform for collaborative management, training, and deployment. Trainer class includes HUB callbacks that log metrics, upload checkpoints, and sync model versions. Authentication is handled via API keys stored in ~/.config/Ultralytics/settings.yaml. Models trained locally can be pushed to HUB for sharing and inference via web API.","intents":["I want to train models locally but sync results to a cloud dashboard for team visibility","I need to share trained models with teammates via a web interface","I want to deploy models to production via Ultralytics HUB inference API"],"best_for":["teams collaborating on model development and deployment","organizations wanting centralized model versioning and management","users preferring managed cloud infrastructure over self-hosted training"],"limitations":["HUB integration requires internet connectivity and Ultralytics account","Free tier has storage and API rate limits","Data uploaded to HUB is stored on Ultralytics servers (privacy considerations)","HUB inference API is slower than local inference due to network latency"],"requires":["Python 3.8+","Ultralytics account and API key","Internet connectivity","Optional: paid HUB subscription for higher limits"],"input_types":["trained model weights (.pt files)","training metrics (logged during training)","dataset metadata"],"output_types":["model URLs for sharing","inference API endpoints","training dashboards (web UI)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ultralytics__headline","uri":"capability://model.training.unified.api.for.yolo.based.computer.vision.tasks","name":"unified api for yolo-based computer vision tasks","description":"Ultralytics provides a unified API for state-of-the-art computer vision tasks including object detection, segmentation, classification, and pose estimation, making it easy to train and deploy models across various formats.","intents":["best YOLO model for object detection","YOLO framework for image segmentation","how to train a YOLO model","deploying YOLO models for real-time applications","YOLO API for pose estimation"],"best_for":["developers looking for computer vision solutions","researchers in AI and machine learning"],"limitations":[],"requires":[],"input_types":["images","video streams"],"output_types":["detection results","segmentation masks"],"categories":["model-training"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","PyTorch 1.7+ OR ONNX Runtime OR TensorRT (format-dependent)","Pre-trained YOLO model weights or custom trained model","PyTorch 1.7+ with CUDA 11.0+ (for GPU training)","Dataset in YOLO format (images + txt annotations) or convertible format","8GB+ GPU memory for standard model sizes","Gradio library","Dataset in YOLO format (images + labels)","Trained YOLO model","Format-specific dependencies (onnx, tensorrt, coremltools, etc.)"],"failure_modes":["AutoBackend selection is deterministic but not always optimal for mixed workloads — may require manual runtime specification for performance tuning","Results object abstraction adds ~5-15ms overhead per inference due to post-processing standardization","Some advanced task-specific optimizations (e.g., custom NMS variants) are not exposed through unified API","YAML config system is rigid for complex custom loss functions — requires subclassing Trainer for non-standard objectives","Distributed training (DDP) requires manual process spawning; no built-in multi-node orchestration","Callback system adds ~2-5% training time overhead due to hook invocations at each epoch","Explorer is read-only — cannot edit annotations directly in UI","Performance degrades with very large datasets (100k+ images) due to Gradio limitations","No built-in export of filtered subsets","Requires local or network access to dataset files (no cloud storage integration)","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":"2026-06-17T09:51:05.297Z","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=ultralytics","compare_url":"https://unfragile.ai/compare?artifact=ultralytics"}},"signature":"OS4F3F9W5a9v5/vMGsQOB5RBt9sTFO94pO/SDnP9/WvMfxJAWVKFtjEP/xB7rVYas7MeANTPrL1DYmunik18Ag==","signedAt":"2026-06-19T21:01:16.647Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ultralytics","artifact":"https://unfragile.ai/ultralytics","verify":"https://unfragile.ai/api/v1/verify?slug=ultralytics","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"}}