Google MediaPipe

on-device ML pipeline framework for vision, text, audio, and LLM inference. Cross-platform deployment to Android, iOS, web, desktop, edge devices, and IoT.

Overview

MediaPipe is Google's open-source framework for building on-device machine learning pipelines. It provides cross-platform APIs for vision, text, audio, and LLM inference tasks, plus a low-level graph-based pipeline framework for custom ML workloads.

Covers

• Computer vision tasks (face detection, face mesh, hand tracking, pose estimation, holistic landmarks, object detection, image classification/segmentation, gesture recognition)
• Text tasks (text classification, text embedding, language detection)
• Audio classification, (4) On-device LLM inference with MediaPipe GenAI/Tasks
• Model customization with MediaPipe Model Maker
• Visualizing/building ML pipelines with the MediaPipe Framework (graphs, calculators, packets)
• Drawing/rendering landmarks and detection results onto images/video
• Understanding MediaPipe's architecture and component ecosystem (Solutions, Tasks, Model Maker, Studio, Framework)

Architecture

MediaPipe has two layers:

1. MediaPipe Solutions (high-level) — Pre-built, ready-to-use ML tasks via cross-platform APIs. Use these for most applications.
2. MediaPipe Framework (low-level) — Graph-based pipeline builder (packets, graphs, calculators) for custom on-device ML pipelines. C++ core with Android/iOS bindings.

The Solutions layer consists of:

• MediaPipe Tasks — Cross-platform libraries (Python, Android, iOS, Web/JS, C++) wrapping pre-trained models
• MediaPipe Models — Pre-trained TFLite model bundles downloadable per task
• MediaPipe Model Maker — Fine-tune/customize models with your own data
• MediaPipe Studio — Browser-based no-code benchmarking and prototyping tool

Installation

Python

bash

pip install mediapipe

Latest version as of 2026-05: 0.10.35. The Python package bundles all tasks. Models are downloaded separately at runtime or pre-downloaded.

Android

Add to build.gradle:

text

implementation 'com.google.mediapipe:tasks-vision:0.10.35'

Replace vision with text, audio, or genai as needed.

Web / JavaScript

bash

npm install @mediapipe/tasks-vision

Available packages: @mediapipe/tasks-vision, @mediapipe/tasks-text, @mediapipe/tasks-audio, @mediapipe/tasks-genai.

iOS (CocoaPods)

text

pod 'MediaPipeTasksVision'

Quick Start — Python Examples

Face Detection

python

import mediapipe as mp
from mediapipe.tasks import python
from mediapipe.tasks.python import vision

model_path = '/absolute/path/to/blaze_face_short_range.tflite'
base_options = python.BaseOptions(model_asset_path=model_path)
options = vision.FaceDetectorOptions(base_options=base_options)
detector = vision.FaceDetector.create_from_options(options)

image = mp.Image.create_from_file('photo.jpg')
result = detector.detect(image)
for detection in result.detections:
    bbox = detection.bounding_box
    print(f"Face at x={bbox.origin_x}, y={bbox.origin_y}, "
          f"w={bbox.width}, h={bbox.height}, "
          f"score={detection.categories[0].score}")

Hand Landmark Detection

python

model_path = '/path/to/hand_landmarker.task'
options = vision.HandLandmarkerOptions(
    base_options=python.BaseOptions(model_asset_path=model_path),
    num_hands=2)
detector = vision.HandLandmarker.create_from_options(options)

image = mp.Image.create_from_file('hands.jpg')
result = detector.detect(image)
for hand_landmarks in result.hand_landmarks:
    for lm in hand_landmarks:
        print(f"Landmark: x={lm.x}, y={lm.y}, z={lm.z}")

Pose Landmark Detection

python

model_path = '/path/to/pose_landmarker_lite.task'
options = vision.PoseLandmarkerOptions(
    base_options=python.BaseOptions(model_asset_path=model_path))
detector = vision.PoseLandmarker.create_from_options(options)

image = mp.Image.create_from_file('person.jpg')
result = detector.detect(image)
# result.pose_landmarks is a list of NormalizedLandmark lists (33 landmarks each)
# result.pose_world_landmarks provides 3D world coordinates

Object Detection

python

model_path = '/path/to/efficientdet_lite0.tflite'
options = vision.ObjectDetectorOptions(
    base_options=python.BaseOptions(model_asset_path=model_path),
    max_results=5)
detector = vision.ObjectDetector.create_from_options(options)

image = mp.Image.create_from_file('scene.jpg')
result = detector.detect(image)
for detection in result.detections:
    print(f"Class: {detection.categories[0].category_name}, "
          f"BBox: {detection.bounding_box}")

Text Classification

python

from mediapipe.tasks.python import text

model_path = '/path/to/text_classifier.tflite'
options = text.TextClassifierOptions(
    base_options=python.BaseOptions(model_asset_path=model_path))
classifier = text.TextClassifier.create_from_options(options)

result = classifier.classify("I absolutely loved this movie!")
for category in result.classifications[0].categories:
    print(f"{category.category_name}: {category.score:.4f}")

Drawing Landmarks on Images

python

import cv2
import mediapipe as mp
from mediapipe.tasks.python import vision
from mediapipe.framework.formats import landmark_pb2

# ... detect landmarks ...

# Convert result landmarks to NormalizedLandmarkList
hand_landmarks_proto = landmark_pb2.NormalizedLandmarkList()
hand_landmarks_proto.landmark.extend([
    landmark_pb2.NormalizedLandmark(x=lm.x, y=lm.y, z=lm.z)
    for lm in result.hand_landmarks[0]
])

# Draw on image
annotated = mp.solutions.drawing_utils.draw_landmarks(
    image_rgb,
    hand_landmarks_proto,
    mp.solutions.hands.HAND_CONNECTIONS,
    mp.solutions.drawing_styles.get_default_hand_landmarks_style(),
    mp.solutions.drawing_styles.get_default_hand_connections_style()
)

Vision Tasks

All vision tasks support three running modes: IMAGE, VIDEO, and LIVE_STREAM.

FaceDetector

• Models: blaze_face_short_range.tflite (2m), blaze_face_full_range.tflite (5m)
• Output: bounding boxes with 6 keypoints (eyes, nose, mouth, ears)
• Options: min_detection_confidence, min_suppression_threshold

FaceLandmarker

• Models: face_landmarker.task (478 3D landmarks), face_landmarker_v2_with_blendshapes.task
• Output: 478 face mesh landmarks, 52 blendshape scores, face transformation matrix
• Options: num_faces, min_face_detection_confidence, min_tracking_confidence, output_face_blendshapes, output_facial_transformation_matrixes

HandLandmarker

• Models: hand_landmarker.task
• Output: 21 hand landmarks per hand, handedness classification (left/right), world landmarks
• Options: num_hands, min_hand_detection_confidence, min_tracking_confidence

PoseLandmarker

• Models: pose_landmarker_lite.task, pose_landmarker_full.task, pose_landmarker_heavy.task
• Output: 33 body pose landmarks, world 3D landmarks, segmentation mask
• Options: num_poses, min_pose_detection_confidence, min_tracking_confidence, output_segmentations

HolisticLandmarker

• Models: holistic_landmarker.task
• Output: Combined face (478), pose (33), and hand (21×2) landmarks simultaneously
• Options: min_face_detection_confidence, min_pose_detection_confidence, min_hand_landmarks_confidence, output_face_blendshapes

GestureRecognizer

• Models: gesture_recognizer.task
• Output: Predefined gesture categories from hand landmarks (e.g., "Thumb_Up", "Victory", "Closed_Fist", "Open_Palm", "Pointing_Up", "ILoveYou")
• Options: min_hand_detection_confidence, min_tracking_confidence, canned_gestures_classifier_options

ObjectDetector

• Models: efficientdet_lite0.tflite through efficientdet_lite2.tflite (COCO 80 classes)
• Output: Bounding boxes with category labels and scores
• Options: max_results, score_threshold, category_allowlist, category_denylist

ImageClassifier

• Models: efficientnet_lite0.tflite through efficientnet_lite4.tflite (ImageNet 1k)
• Output: Classification category list with scores
• Options: max_results, score_threshold, category_allowlist/denylist

ImageEmbedder

• Models: mobilenet_v3_small.tflite, mobilenet_v3_large.tflite
• Output: Feature embedding vectors (float or quantized) for similarity/comparison
• Options: l2_normalize, quantize

ImageSegmenter

• Models: Various segmentation models (DeepLab, selfie segmenter, hair segmenter)
• Output: Category mask and/or confidence mask
• Options: output_category_mask, output_confidence_masks

InteractiveSegmenter

• Models: magic_touch.tflite, sam.tflite
• Output: Segmentation mask for a user-specified region of interest (click/tap)
• Options: output_category_mask, output_confidence_masks

Text Tasks

TextClassifier

• Models: text_classifier.tflite (BERT-based), custom models via Model Maker
• Output: Classification categories with scores (sentiment, topic, etc.)
• Options: max_results, score_threshold, category_allowlist/denylist

TextEmbedder

• Models: universal_sentence_encoder.tflite, bert_embedder.tflite
• Output: Text embedding vectors for semantic similarity, clustering, retrieval
• Options: l2_normalize, quantize

LanguageDetector

• Models: language_detector.tflite
• Output: Detected language BCP-47 code(s) with probabilities (supports 110+ languages)

Audio Tasks

AudioClassifier

• Models: yamnet.tflite (521 audio event classes), custom models
• Output: Audio event classification with timestamps
• Input: Audio clips (mono, 16kHz sample rate) or streaming audio buffers
• Options: max_results, score_threshold, category_allowlist/denylist
• Supports AUDIO_CLIPS and AUDIO_STREAM running modes

LLM Inference (GenAI)

MediaPipe includes on-device LLM inference via MediaPipe Tasks GenAI (as of v0.10.35):

• JavaScript: @mediapipe/tasks-genai package for web-based LLM inference
• Android: Tasks GenAI for NPU-accelerated on-device LLM
• Python: LLM converter utilities for blockwise int4 quantization, weight compression
• Supports configurable quantization policies and supervised round quantization (SRQ)

MediaPipe Model Maker

Customize pre-trained models with your own data without ML expertise:

bash

pip install mediapipe-model-maker

python

from mediapipe_model_maker import text_classifier

data = text_classifier.Dataset.from_csv('reviews.csv')
model = text_classifier.create(data)
model.export_model()

Supports customization for text classification, object detection, image classification, and gesture recognition. Model Maker uses transfer learning with a few hundred examples.

MediaPipe Framework (Low-Level)

For building custom on-device ML pipelines beyond pre-built solutions:

Core Concepts

• Packets — Typed data containers (images, tensors, landmarks) that flow through the graph
• Graphs — Directed acyclic graphs of calculator nodes defining the pipeline topology
• Calculators — Processing nodes that consume input packets and produce output packets
• Streams — Named data pathways connecting calculator inputs/outputs
• Side Packets — Configuration data injected at graph initialization

Graph Configuration (.pbtxt)

protobuf

input_stream: "input_video"
output_stream: "output_video"

node {
  calculator: "ImageToTensorCalculator"
  input_stream: "IMAGE:input_video"
  output_stream: "TENSORS:image_tensor"
}

node {
  calculator: "InferenceCalculator"
  input_stream: "TENSORS:image_tensor"
  output_stream: "TENSORS:detection_tensors"
  options {
    [mediapipe.InferenceCalculatorOptions.ext] {
      model_path: "/path/to/model.tflite"
    }
  }
}

Supported Platforms for Framework

• C++ (Bazel build system)
• Android (AAR, JNI bindings)
• iOS (framework)
• Desktop (Linux, macOS, Windows via C++)

The Framework is not available for Python or web — use MediaPipe Tasks/Solutions for those platforms.

Model Management

Downloading Models

Models are hosted at https://storage.googleapis.com/mediapipe-models/. Download programmatically:

python

# Python: download helper (if available) or manual curl
# wget https://storage.googleapis.com/mediapipe-models/hand_landmarker/hand_landmarker/float16/latest/hand_landmarker.task

For production, download models ahead of time and bundle with your app.

Model Path Requirements

• Python: Must use absolute paths for model_asset_path. Relative paths or pathlib.Path objects may fail.
• Web: Pass Wasm file URLs; must be served from same origin or with CORS headers.
• Android: Place .task/.tflite files in src/main/assets/.

Common Patterns & Best Practices

Running Modes

python

# IMAGE mode — single image inference
options = vision.FaceDetectorOptions(
    base_options=python.BaseOptions(model_asset_path=model_path),
    running_mode=vision.RunningMode.IMAGE)

# VIDEO mode — frame sequence with timestamps
options = vision.FaceDetectorOptions(
    base_options=python.BaseOptions(model_asset_path=model_path),
    running_mode=vision.RunningMode.VIDEO)
# result = detector.detect_for_video(image, timestamp_ms)

# LIVE_STREAM mode — async callback-based for camera streams
def on_result(result, image, timestamp):
    pass  # handle result asynchronously

options = vision.FaceDetectorOptions(
    base_options=python.BaseOptions(model_asset_path=model_path),
    running_mode=vision.RunningMode.LIVE_STREAM,
    result_callback=on_result)

Lazy Resource Cleanup

All task objects implement context manager protocol:

python

with vision.FaceDetector.create_from_options(options) as detector:
    result = detector.detect(image)
# detector is automatically closed

Image Handling

python

import mediapipe as mp

# From file
image = mp.Image.create_from_file('photo.jpg')

# From numpy array (must be RGB, uint8)
import cv2
cv_image = cv2.imread('photo.jpg')
rgb_image = cv2.cvtColor(cv_image, cv2.COLOR_BGR2RGB)
mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=rgb_image)

GPU Delegation

python

base_options = python.BaseOptions(
    model_asset_path=model_path,
    delegate=python.BaseOptions.Delegate.GPU)  # or .CPU (default)

Error Handling

python

try:
    detector = vision.FaceDetector.create_from_options(options)
except Exception as e:
    print(f"Failed to create detector: {e}")
    # Common issues: wrong model path, incompatible model version, missing TFLite runtime

Model Versions & Compatibility

• Model .task bundles (new format) vs legacy .tflite + metadata
• Task bundle encapsulates model + metadata + pre/post processing configs
• Always check model/task version compatibility — model versions are tied to specific MediaPipe SDK versions
• Download latest models from the official model hub

Legacy Notes

• Legacy solutions (pre-2023) are deprecated as of March 2023 — use Tasks API instead
• Old mp.solutions.hands, mp.solutions.pose, mp.solutions.face_mesh APIs are legacy
• The new Tasks API (mediapipe.tasks.python.vision.HandLandmarker) supersedes legacy APIs
• Legacy code still in mediapipe.solutions.* namespace; use Tasks for new projects
• The Framework layer continues to be maintained for custom pipeline development

Key Links

• Official Docs: https://developers.google.com/mediapipe
• GitHub: https://github.com/google-ai-edge/mediapipe
• Samples: https://github.com/google-ai-edge/mediapipe-samples
• Model Hub: https://developers.google.com/mediapipe/solutions/models
• Studio: https://mediapipe-studio.web.app
• PyPI: https://pypi.org/project/mediapipe/
• Model Maker: https://developers.google.com/mediapipe/solutions/model_maker
• Paper: https://arxiv.org/abs/1906.08172