{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"mediapipe","slug":"mediapipe","name":"MediaPipe","type":"framework","url":"https://ai.google.dev/edge/mediapipe/solutions/guide","page_url":"https://unfragile.ai/mediapipe","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"mediapipe__cap_0","uri":"capability://image.visual.on.device.face.detection.with.multi.face.tracking","name":"on-device face detection with multi-face tracking","description":"Detects and localizes human faces in images and video streams using a lightweight neural network optimized for on-device inference, returning bounding boxes and confidence scores without requiring cloud connectivity. Implements hardware acceleration (GPU/NPU) on Android, iOS, and Web via platform-native APIs, enabling real-time processing at 30+ FPS on mobile devices with sub-100ms latency per frame.","intents":["detect faces in a live camera feed for a mobile app without sending data to the cloud","extract face regions from images for downstream processing like emotion recognition or face blur","build a face-unlock or attendance system that works offline on edge devices","track multiple faces across video frames for video conferencing or surveillance applications"],"best_for":["mobile app developers building privacy-first face detection features","embedded systems engineers deploying ML on IoT devices","teams building offline-first applications without cloud infrastructure"],"limitations":["accuracy degrades significantly for faces smaller than ~50x50 pixels or at extreme angles (>45° yaw/pitch)","no built-in face recognition or identity matching — only detection and localization","model size and latency not publicly documented; actual performance varies by device hardware","no streaming/async API documented — appears to be synchronous frame-by-frame processing only"],"requires":["Android 5.0+ (API 21) with GPU support for hardware acceleration, or iOS 11.0+, or modern browser with WebGL","camera permissions on mobile platforms","MediaPipe Tasks library installed (language-specific: Java/Kotlin for Android, Swift for iOS, JavaScript for Web, Python for desktop)"],"input_types":["image (JPEG, PNG, BMP)","video frame (raw pixel buffer, YUV420 or RGB)","live camera stream"],"output_types":["structured data: array of face detections with bounding box (x, y, width, height), confidence score (0-1), and optional rotation angle"],"categories":["image-visual","edge-computing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mediapipe__cap_1","uri":"capability://image.visual.hand.landmark.detection.with.gesture.recognition","name":"hand landmark detection with gesture recognition","description":"Detects and tracks 21 hand keypoints (knuckles, joints, fingertips, palm center) in real-time video or images, enabling gesture recognition and hand pose estimation. Processes hand regions through a multi-stage pipeline: hand detection → hand cropping → landmark localization, with built-in support for left/right hand classification and multi-hand tracking across frames.","intents":["recognize hand gestures (thumbs up, peace sign, OK sign) for touchless UI control in AR/VR applications","extract hand pose for sign language recognition or hand-based game controllers","track hand movement across video frames for motion capture or fitness tracking applications","detect hand presence and position for virtual try-on or augmented reality applications"],"best_for":["AR/VR developers building gesture-based interfaces","fitness app developers tracking exercise form via hand position","accessibility engineers creating touchless control systems","game developers implementing hand-based input for mobile or web games"],"limitations":["requires clear visibility of hands; performance degrades with occlusion, extreme angles, or motion blur","no built-in gesture classification — raw keypoints must be post-processed to recognize specific gestures","accuracy varies significantly based on hand size, lighting, and background complexity","no hand segmentation or depth estimation — 2D keypoints only, no 3D hand pose"],"requires":["Android 5.0+ (API 21) or iOS 11.0+ or modern browser (Chrome 90+, Safari 14+)","camera access with reasonable lighting conditions","MediaPipe Tasks library for target platform"],"input_types":["image (JPEG, PNG, BMP)","video frame (raw pixel buffer)","live camera stream"],"output_types":["structured data: array of hand detections, each containing 21 landmarks (x, y, z coordinates), handedness (left/right), and confidence scores"],"categories":["image-visual","edge-computing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mediapipe__cap_10","uri":"capability://image.visual.image.generation.with.text.to.image.synthesis","name":"image generation with text-to-image synthesis","description":"Generates images from text descriptions using a neural network-based generative model. Processes text prompts through a text encoder and diffusion model to produce novel images matching the description, supporting customization via negative prompts and generation parameters.","intents":["generate product mockups or design variations from text descriptions","create placeholder images for prototyping without stock photo licensing","enable users to generate custom images in creative applications","automate visual content creation for marketing or social media"],"best_for":["design teams prototyping visual concepts from text descriptions","e-commerce platforms generating product variations","content creators automating image generation for social media","teams building creative AI applications"],"limitations":["image generation quality and speed not documented; unclear if suitable for real-time use","model size and computational requirements not specified; may require high-end GPU","no fine-tuning or custom model training documented; unclear if customization supported","no control over specific image attributes (style, composition, etc.) beyond text prompt","potential copyright and ethical concerns with generated images not addressed"],"requires":["Android 5.0+ (API 21) or iOS 11.0+ or modern browser or Python 3.8+","MediaPipe Tasks library","sufficient GPU/computational resources (requirements unclear)"],"input_types":["text (string, UTF-8 encoded; text prompt describing desired image)","optional: negative prompt (text describing what to avoid)","optional: generation parameters (seed, guidance scale, etc. — exact parameters unclear)"],"output_types":["image (PNG or JPEG; resolution and format unclear)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mediapipe__cap_11","uri":"capability://data.processing.analysis.model.customization.via.fine.tuning.with.model.maker","name":"model customization via fine-tuning with model maker","description":"Enables fine-tuning of pre-trained MediaPipe models on custom datasets to adapt them for domain-specific tasks. Model Maker abstracts the training process, accepting labeled datasets and producing optimized models for deployment on Android, iOS, Web, or Python without requiring deep ML expertise.","intents":["train a custom object detector for proprietary products or objects not in COCO dataset","adapt a text classifier to domain-specific categories (e.g., medical document classification)","create a custom pose estimator for specific sports or exercises","build a gesture recognizer for custom hand gestures relevant to your application"],"best_for":["teams with domain-specific ML needs but limited ML expertise","enterprises building proprietary object or text classifiers","researchers prototyping custom models without extensive training infrastructure","product teams iterating on ML models without dedicated ML engineers"],"limitations":["training time, computational requirements, and data requirements not documented","no guidance on dataset size, quality, or labeling best practices","unclear which model types support fine-tuning (e.g., does pose estimation support fine-tuning?)","no evaluation metrics or validation tools documented; unclear how to assess model quality","no version control or model management — unclear how to track model iterations"],"requires":["labeled dataset in required format (format specifications unclear)","access to Model Maker tool (web-based or CLI — interface unclear)","computational resources for training (GPU/TPU requirements unclear)","basic understanding of ML concepts (train/validation/test split, overfitting, etc.)"],"input_types":["labeled dataset (images for vision tasks, text for NLP tasks; exact format and structure unclear)"],"output_types":["fine-tuned model optimized for target platform (Android, iOS, Web, Python); format and size unclear"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mediapipe__cap_12","uri":"capability://automation.workflow.cross.platform.model.deployment.with.hardware.acceleration","name":"cross-platform model deployment with hardware acceleration","description":"Deploys trained or pre-trained MediaPipe models to Android, iOS, Web, and Python with automatic hardware acceleration (GPU, NPU) on supported devices. Abstracts platform-specific optimization details, providing a unified API surface across platforms while leveraging native hardware acceleration for real-time inference.","intents":["deploy a single model to multiple platforms (mobile, web, desktop) without platform-specific code","leverage GPU/NPU acceleration on mobile devices for real-time inference without manual optimization","build cross-platform applications with consistent ML behavior across devices","reduce deployment complexity by using unified MediaPipe Tasks API across platforms"],"best_for":["cross-platform app developers building ML features for multiple platforms","teams lacking platform-specific optimization expertise","products requiring consistent ML behavior across Android, iOS, Web, and Python","startups minimizing engineering overhead for multi-platform deployment"],"limitations":["hardware acceleration availability varies by device and platform; fallback behavior on unsupported hardware unclear","performance characteristics (latency, throughput, memory) not documented per platform","no explicit control over hardware acceleration (GPU vs CPU) — automatic selection may not match application requirements","model size and inference latency vary significantly by platform; no guidance on platform-specific optimization","no multi-model inference or model ensemble support documented"],"requires":["Android 5.0+ (API 21) with optional GPU support, or iOS 11.0+, or modern browser (Chrome 90+, Safari 14+), or Python 3.8+","MediaPipe Tasks library for target platform","for GPU acceleration: compatible GPU/NPU hardware (requirements vary by platform)"],"input_types":["MediaPipe model (pre-trained or fine-tuned via Model Maker)"],"output_types":["platform-specific inference results (format varies by task: bounding boxes for detection, keypoints for pose, etc.)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mediapipe__cap_13","uri":"capability://data.processing.analysis.browser.based.model.evaluation.and.comparison.via.mediapipe.studio","name":"browser-based model evaluation and comparison via mediapipe studio","description":"Provides a web-based interface (MediaPipe Studio) for visualizing, evaluating, and comparing MediaPipe models on images and videos without requiring code. Enables interactive testing of models, side-by-side comparison of different models or parameter configurations, and visualization of model outputs (bounding boxes, keypoints, masks, etc.).","intents":["evaluate model performance on custom images before deploying to production","compare different model versions or configurations to select the best one","visualize model outputs (detections, keypoints, segmentation masks) for debugging","demonstrate model capabilities to stakeholders without requiring technical setup"],"best_for":["ML engineers evaluating models before deployment","product managers assessing model quality for feature decisions","teams without deep technical expertise wanting to test models","researchers comparing model variants"],"limitations":["no automated evaluation metrics (precision, recall, F1, etc.) — only visual inspection","no batch evaluation or dataset-level metrics — single image/video at a time","no model performance profiling (latency, memory, throughput) — visual evaluation only","unclear if custom models can be uploaded or only pre-trained models supported","no export of evaluation results or comparison reports"],"requires":["modern web browser (Chrome, Safari, Firefox, Edge)","internet connection to access MediaPipe Studio","image or video file to evaluate (format requirements unclear)"],"input_types":["image (JPEG, PNG, BMP — exact formats unclear)","video (MP4, WebM — exact formats unclear)"],"output_types":["visual output: annotated image/video with model predictions (bounding boxes, keypoints, masks, etc.)","optional: model output data (JSON or CSV — export format unclear)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mediapipe__cap_14","uri":"capability://text.generation.language.llm.inference.api.for.on.device.language.model.execution","name":"llm inference api for on-device language model execution","description":"Executes large language models (LLMs) on-device without cloud connectivity, enabling privacy-preserving text generation, completion, and reasoning tasks. Supports quantized or distilled LLM models optimized for mobile and edge devices, with configurable generation parameters (temperature, top-k, top-p, max tokens).","intents":["run LLM inference on mobile devices for privacy-sensitive applications without cloud API calls","build offline-capable chatbots or text generation features for mobile apps","enable local reasoning and planning for AI agents without external API dependencies","implement on-device code completion or text suggestion features"],"best_for":["privacy-focused app developers avoiding cloud LLM APIs","teams building offline-capable AI features","enterprises with data residency requirements","mobile app developers adding LLM capabilities without cloud infrastructure"],"limitations":["model selection, size, and capabilities not documented — unclear which LLMs supported","inference latency and throughput not documented; unclear if suitable for real-time use","no fine-tuning or custom model training documented","no streaming or token-by-token output documented; unclear if suitable for interactive use","memory and computational requirements not specified; unclear which devices supported","no context window or prompt length limits documented"],"requires":["Android 5.0+ (API 21) or iOS 11.0+ or modern browser or Python 3.8+","MediaPipe Tasks library","sufficient device memory and computational resources (requirements unclear)","LLM model file (pre-trained or fine-tuned; format and source unclear)"],"input_types":["text (string, UTF-8 encoded; prompt or input text for LLM)"],"output_types":["text (string, UTF-8 encoded; generated text output from LLM)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mediapipe__cap_15","uri":"capability://text.generation.language.llm.inference.api.for.on.device.language.model.execution","name":"llm inference api for on-device language model execution","description":"Enables running large language models (LLMs) on-device using MediaPipe's LLM Inference API. Supports quantized/compressed LLM models optimized for mobile and edge devices. Handles tokenization, inference, and token generation. Supports streaming token output for real-time text generation. Enables chatbots, text generation, and other LLM-based features without cloud calls. ARCHITECTURAL DETAILS UNKNOWN: documentation does not specify supported model formats, quantization methods, or provider support.","intents":["Build on-device chatbots that run locally without cloud dependency","Implement text generation features (autocomplete, summarization) on mobile","Create privacy-preserving AI assistants that process data locally","Deploy LLMs to edge devices with limited connectivity"],"best_for":["Mobile app developers building on-device chatbots","Privacy-conscious teams avoiding cloud LLM APIs","Edge device teams deploying LLMs with limited connectivity","Developers building offline-capable AI features"],"limitations":["Limited to quantized/compressed models; full-size LLMs too large for mobile","Inference latency higher than cloud APIs due to device constraints","Model selection limited to pre-optimized models (UNKNOWN which models supported)","No fine-tuning support (UNKNOWN); likely inference-only"],"requires":["Android SDK 21+, iOS 12+, modern web browser, or Python 3.9+","Quantized LLM model file (format UNKNOWN)","Sufficient device storage and memory for model (varies by model size)"],"input_types":["text prompt (UTF-8 string)"],"output_types":["text generation: generated tokens streamed or batched","numeric data: token probabilities (optional)"],"categories":["text-generation-language","on-device-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mediapipe__cap_16","uri":"capability://image.visual.image.generation.with.text.to.image.synthesis","name":"image generation with text-to-image synthesis","description":"Generates images from text descriptions using a pre-trained text-to-image model. Takes text prompt as input and outputs generated image. ARCHITECTURAL DETAILS UNKNOWN: documentation does not specify model architecture, inference approach, or customization options. Likely uses a diffusion model or similar generative architecture optimized for mobile.","intents":["Build creative tools that generate images from text descriptions","Implement AI-powered design features for content creation apps","Create visual content for marketing or social media","Enable users to generate custom images without design skills"],"best_for":["Content creation app developers adding image generation","Creative tools developers building AI-powered design features","Marketing teams automating visual content creation","Developers building user-facing generative AI features"],"limitations":["Image quality depends on text prompt clarity; vague prompts produce poor results","Inference latency likely high (seconds to minutes) due to generative model complexity","No fine-tuning support (UNKNOWN); limited to pre-trained model","Generated images may have artifacts or quality issues typical of generative models"],"requires":["Android SDK 21+, iOS 12+, modern web browser, or Python 3.9+ (UNKNOWN which platforms supported)","Sufficient device storage and memory for generative model (likely 500MB-2GB+)","GPU acceleration recommended for reasonable inference speed"],"input_types":["text prompt (UTF-8 string)"],"output_types":["image (JPEG, PNG, or raw pixel buffer)"],"categories":["image-visual","on-device-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mediapipe__cap_2","uri":"capability://image.visual.pose.landmark.detection.for.body.keypoint.tracking","name":"pose landmark detection for body keypoint tracking","description":"Detects and tracks 33 body keypoints (joints, landmarks across head, torso, arms, and legs) in images and video streams using a neural network-based approach. Outputs 3D coordinates (x, y, z) for each landmark with per-landmark visibility confidence, enabling pose estimation, fitness tracking, and motion analysis without cloud dependency.","intents":["track exercise form and provide real-time feedback in fitness apps (e.g., squat depth, push-up form)","estimate body pose for motion capture or animation applications","detect fall events or abnormal postures for elderly care or safety monitoring","build pose-based game controls or interactive fitness experiences"],"best_for":["fitness app developers building form-checking features","motion capture engineers working on animation or VR content","healthcare/elderly care teams implementing fall detection","game developers creating pose-based interactive experiences"],"limitations":["accuracy degrades with occlusion (body parts hidden behind objects or other people)","single-person pose estimation only — no multi-person pose tracking in crowded scenes","z-coordinate (depth) is estimated from 2D image, not true 3D depth; accuracy limited without depth camera","requires full or mostly-visible body in frame; partial body crops reduce accuracy significantly"],"requires":["Android 5.0+ (API 21) or iOS 11.0+ or modern browser","camera with clear view of person's body","MediaPipe Tasks library"],"input_types":["image (JPEG, PNG, BMP)","video frame","live camera stream"],"output_types":["structured data: 33 body landmarks with (x, y, z) coordinates, visibility confidence per landmark, and overall pose confidence"],"categories":["image-visual","edge-computing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mediapipe__cap_3","uri":"capability://image.visual.object.detection.with.bounding.box.localization","name":"object detection with bounding box localization","description":"Detects and localizes objects in images and video streams by identifying object categories and their spatial locations via bounding boxes. Supports multiple object detection models (COCO, Open Images, custom datasets) with configurable confidence thresholds, returning class labels, confidence scores, and bounding box coordinates for each detected object.","intents":["detect specific objects in images for inventory management or quality control applications","build real-time object detection for mobile apps (e.g., product recognition, pet detection)","implement object counting or tracking for retail analytics or traffic monitoring","create custom object detectors for domain-specific use cases via Model Maker fine-tuning"],"best_for":["mobile app developers building object recognition features","retail/e-commerce teams implementing product detection","industrial/manufacturing engineers doing quality control","teams needing custom object detection for proprietary objects"],"limitations":["pre-trained models optimized for common objects (COCO dataset); performance on domain-specific objects requires fine-tuning via Model Maker","no tracking across frames — each frame processed independently; multi-object tracking requires external tracking logic","bounding box output only — no pixel-level segmentation masks or instance segmentation","accuracy-latency trade-off not publicly documented; model selection process unclear"],"requires":["Android 5.0+ (API 21) or iOS 11.0+ or modern browser or Python 3.8+","MediaPipe Tasks library","for custom models: access to Model Maker tool and custom training dataset"],"input_types":["image (JPEG, PNG, BMP)","video frame","live camera stream"],"output_types":["structured data: array of detections, each with class label, confidence score (0-1), and bounding box (x, y, width, height)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mediapipe__cap_4","uri":"capability://image.visual.image.segmentation.with.semantic.and.instance.variants","name":"image segmentation with semantic and instance variants","description":"Segments images into semantic regions (pixel-level classification by category) or instance segments (individual object masks). Processes images through a neural network to produce dense pixel-level predictions, returning either per-pixel class labels (semantic) or per-object masks with instance IDs (instance segmentation).","intents":["remove or blur backgrounds in video calls or photos without requiring green screen","extract specific objects from images for e-commerce product photography","perform scene understanding for autonomous vehicles or robotics applications","create interactive segmentation tools where users select regions to refine masks"],"best_for":["video conferencing app developers implementing virtual backgrounds","e-commerce platforms automating product image background removal","robotics/autonomous vehicle teams doing scene understanding","content creators building image editing tools"],"limitations":["semantic segmentation only provides class labels, not instance boundaries — overlapping objects of same class merge","instance segmentation more computationally expensive; latency may exceed real-time requirements on lower-end devices","accuracy depends on training data; pre-trained models optimized for common scenes (indoor/outdoor); domain-specific segmentation requires fine-tuning","no temporal consistency across video frames — flickering artifacts possible in video applications"],"requires":["Android 5.0+ (API 21) or iOS 11.0+ or modern browser or Python 3.8+","MediaPipe Tasks library","sufficient GPU/NPU for real-time inference (latency requirements vary by model)"],"input_types":["image (JPEG, PNG, BMP)","video frame","live camera stream"],"output_types":["semantic segmentation: dense pixel-level mask with class labels (e.g., 0=background, 1=person, 2=car)","instance segmentation: per-instance masks with instance IDs and class labels"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mediapipe__cap_5","uri":"capability://text.generation.language.text.classification.with.custom.category.support","name":"text classification with custom category support","description":"Classifies text into predefined or custom categories using a neural network-based text encoder. Processes text through embedding and classification layers, returning predicted category labels with confidence scores. Supports fine-tuning on custom datasets via Model Maker for domain-specific classification tasks.","intents":["classify user reviews as positive/negative/neutral for sentiment analysis","categorize support tickets by topic for automated routing","detect spam or toxic content in user-generated text","build custom text classifiers for domain-specific categorization (e.g., medical document classification)"],"best_for":["content moderation teams filtering user-generated text","customer support teams automating ticket routing","e-commerce platforms analyzing product reviews","teams building domain-specific text classification without ML expertise"],"limitations":["single-label classification only — no multi-label support (text can belong to multiple categories simultaneously)","no explanation or feature attribution — black-box predictions without interpretability","pre-trained models limited to common tasks (sentiment, toxicity); custom classification requires Model Maker fine-tuning","no context window or document length limits documented; behavior on very long texts unclear"],"requires":["Android 5.0+ (API 21) or iOS 11.0+ or modern browser or Python 3.8+","MediaPipe Tasks library","for custom models: training dataset with labeled examples and access to Model Maker"],"input_types":["text (string, UTF-8 encoded)"],"output_types":["structured data: predicted category label and confidence score (0-1); optionally top-N predictions with scores"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mediapipe__cap_6","uri":"capability://text.generation.language.text.embedding.generation.for.semantic.search.and.similarity","name":"text embedding generation for semantic search and similarity","description":"Converts text into fixed-size numerical embeddings (vectors) that capture semantic meaning, enabling similarity comparisons and semantic search. Uses a pre-trained text encoder model to transform variable-length text into a dense vector representation (e.g., 512-dimensional), where similar texts produce similar embeddings.","intents":["find semantically similar documents or search results without keyword matching","build recommendation systems based on text similarity (e.g., similar articles, products)","cluster documents or user queries by semantic meaning","implement semantic search in mobile apps without cloud infrastructure"],"best_for":["search teams implementing semantic search without cloud APIs","recommendation system builders working with text-based content","teams building privacy-preserving similarity search","mobile app developers adding semantic search to offline content"],"limitations":["embeddings are model-specific — embeddings from different models are not comparable","no built-in vector storage or similarity search — requires external database (e.g., SQLite with vector extension, Faiss) for large-scale search","embedding quality depends on pre-training data; domain-specific embeddings may require fine-tuning","no multi-lingual support documented; unclear if embeddings work across languages"],"requires":["Android 5.0+ (API 21) or iOS 11.0+ or modern browser or Python 3.8+","MediaPipe Tasks library","for similarity search at scale: external vector database or similarity search library (Faiss, Annoy, etc.)"],"input_types":["text (string, UTF-8 encoded)"],"output_types":["numerical vector (fixed-size embedding, e.g., 512 dimensions); format typically float32 array"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mediapipe__cap_7","uri":"capability://text.generation.language.language.detection.for.multi.lingual.text.identification","name":"language detection for multi-lingual text identification","description":"Identifies the language of input text by classifying it into one of 100+ supported languages. Uses a lightweight neural network classifier optimized for on-device inference, returning the detected language code (e.g., 'en', 'es', 'zh') with confidence score.","intents":["automatically route user input to language-specific processing pipelines","detect language for multi-lingual applications without user selection","filter or categorize user-generated content by language","enable language-aware features in global applications"],"best_for":["global app developers supporting multiple languages","content moderation teams processing multi-lingual user input","translation platforms automating language detection","teams building language-aware features without manual user selection"],"limitations":["accuracy degrades on very short text (< 10 characters); requires sufficient text for reliable detection","no script detection or language variant support (e.g., Simplified vs Traditional Chinese treated as same)","confidence scores not documented; unclear how to interpret or threshold predictions","no support for code-switching (text mixing multiple languages)"],"requires":["Android 5.0+ (API 21) or iOS 11.0+ or modern browser or Python 3.8+","MediaPipe Tasks library"],"input_types":["text (string, UTF-8 encoded, minimum ~10 characters recommended)"],"output_types":["structured data: detected language code (ISO 639-1 or similar) and confidence score (0-1)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mediapipe__cap_8","uri":"capability://image.visual.audio.classification.for.sound.event.recognition","name":"audio classification for sound event recognition","description":"Classifies audio clips into predefined sound event categories (e.g., speech, music, applause, dog bark) using a neural network-based audio classifier. Processes audio spectrograms through a classification model, returning predicted event labels with confidence scores.","intents":["detect speech vs music vs silence for audio processing pipelines","recognize environmental sounds for accessibility features (e.g., doorbell detection for deaf users)","classify audio events for smart home automation (e.g., detect glass breaking for security)","build audio-based content moderation (e.g., detect screaming or gunshots)"],"best_for":["accessibility engineers building sound event detection for deaf/hard-of-hearing users","smart home developers implementing audio-triggered automation","security/surveillance teams detecting anomalous sounds","audio processing teams filtering or categorizing audio content"],"limitations":["single-label classification only — no multi-label support for overlapping sounds","pre-trained models limited to common sound events; domain-specific audio classification requires fine-tuning","no temporal localization — returns classification for entire audio clip, not timestamp of event within clip","audio format and sample rate requirements not documented; unclear if resampling required"],"requires":["Android 5.0+ (API 21) or iOS 11.0+ or modern browser or Python 3.8+","MediaPipe Tasks library","audio input source (microphone, audio file, or audio stream)","for custom models: labeled audio dataset and access to Model Maker"],"input_types":["audio (WAV, MP3, or raw PCM; sample rate and bit depth requirements unclear)","audio stream (real-time microphone input)"],"output_types":["structured data: predicted sound event category label and confidence score (0-1); optionally top-N predictions"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mediapipe__cap_9","uri":"capability://image.visual.interactive.segmentation.with.user.guided.mask.refinement","name":"interactive segmentation with user-guided mask refinement","description":"Enables users to refine image segmentation masks by providing interactive input (e.g., clicking to select regions, drawing strokes). Combines automated segmentation with user guidance to produce precise masks, using a neural network that accepts both image and user interaction as input.","intents":["allow users to manually refine auto-generated background removal masks for better results","enable precise object extraction in image editing tools with minimal user effort","build interactive photo editing features where users guide the segmentation process","create annotation tools for semi-automated image labeling"],"best_for":["image editing app developers building interactive background removal","content creation tools requiring precise object extraction","data annotation teams semi-automating image labeling","e-commerce platforms improving product image quality"],"limitations":["requires user interaction — not fully automated; unsuitable for batch processing","interaction modality (click, stroke, bounding box) not documented; unclear what input types supported","latency of interactive refinement not documented; real-time responsiveness unclear","no multi-step refinement guidance — unclear if users can iteratively refine masks"],"requires":["Android 5.0+ (API 21) or iOS 11.0+ or modern browser or Python 3.8+","MediaPipe Tasks library","UI framework for capturing user interaction (clicks, strokes, etc.)"],"input_types":["image (JPEG, PNG, BMP)","user interaction (click coordinates, stroke paths, or bounding box — exact format unclear)"],"output_types":["refined segmentation mask (pixel-level binary or multi-class mask)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mediapipe__headline","uri":"capability://data.processing.analysis.cross.platform.framework.for.on.device.machine.learning.pipelines","name":"cross-platform framework for on-device machine learning pipelines","description":"MediaPipe is a versatile framework for building on-device machine learning pipelines, offering pre-built solutions for tasks like face detection, hand tracking, and object detection across multiple platforms including Android, iOS, web, and Python.","intents":["best on-device ML framework","ML framework for computer vision","cross-platform ML solutions","best framework for face detection","ML pipeline for object detection"],"best_for":["developers needing real-time computer vision solutions"],"limitations":["not suitable for general-purpose language tasks"],"requires":["basic knowledge of machine learning"],"input_types":["images","video streams"],"output_types":["detected objects","tracked movements"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Android 5.0+ (API 21) with GPU support for hardware acceleration, or iOS 11.0+, or modern browser with WebGL","camera permissions on mobile platforms","MediaPipe Tasks library installed (language-specific: Java/Kotlin for Android, Swift for iOS, JavaScript for Web, Python for desktop)","Android 5.0+ (API 21) or iOS 11.0+ or modern browser (Chrome 90+, Safari 14+)","camera access with reasonable lighting conditions","MediaPipe Tasks library for target platform","Android 5.0+ (API 21) or iOS 11.0+ or modern browser or Python 3.8+","MediaPipe Tasks library","sufficient GPU/computational resources (requirements unclear)","labeled dataset in required format (format specifications unclear)"],"failure_modes":["accuracy degrades significantly for faces smaller than ~50x50 pixels or at extreme angles (>45° yaw/pitch)","no built-in face recognition or identity matching — only detection and localization","model size and latency not publicly documented; 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