Object Detector

The MediaPipe Object Detector task lets you detect the presence and location of multiple classes of objects within images or videos. For example, an object detector can locate dogs in an image. This task operates on image data with a machine learning (ML) model, accepting static data or a continuous video stream as input and outputting a list of detection results. Each detection result represents an object that appears within the image or video.

Open in Web Demos Edit in StackBlitz

Quickstart

Install the SDK for your platform, prepare input data, choose a running mode, and run inference.

Web

npmnpm install @mediapipe/tasks-visionWeb demo·Edit in StackBlitz·Setup guide

Image

import { FilesetResolver, ObjectDetector } from "@mediapipe/tasks-vision";

const vision = await FilesetResolver.forVisionTasks(
  "https://cdn.jsdelivr.net/npm/@mediapipe/tasks-vision@latest/wasm"
);
const objectDetector = await ObjectDetector.createFromOptions(vision, {
  baseOptions: { modelAssetPath: "efficientdet_lite0.tflite", delegate: "GPU" },
  runningMode: "IMAGE",
  scoreThreshold: 0.5,
  maxResults: 5,
});

// Synchronous single-frame call (stateless across images)
const image = document.getElementById("image") as HTMLImageElement;
const result = objectDetector.detect(image);

Video

import { FilesetResolver, ObjectDetector } from "@mediapipe/tasks-vision";

const vision = await FilesetResolver.forVisionTasks(
  "https://cdn.jsdelivr.net/npm/@mediapipe/tasks-vision@latest/wasm"
);
const objectDetector = await ObjectDetector.createFromOptions(vision, {
  baseOptions: { modelAssetPath: "efficientdet_lite0.tflite", delegate: "GPU" },
  runningMode: "VIDEO",
  scoreThreshold: 0.5,
  maxResults: 5,
});

// Synchronous across decoded frames with monotonic timestamps (never drops frames)
for (const { frame, timestampMs } of decodedVideoFrames) {
  const result = objectDetector.detectForVideo(frame, timestampMs);
}

Livestream

import { FilesetResolver, ObjectDetector } from "@mediapipe/tasks-vision";

const vision = await FilesetResolver.forVisionTasks(
  "https://cdn.jsdelivr.net/npm/@mediapipe/tasks-vision@latest/wasm"
);
// On Web, live camera loops pair runningMode: "VIDEO" with requestAnimationFrame
const objectDetector = await ObjectDetector.createFromOptions(vision, {
  baseOptions: { modelAssetPath: "efficientdet_lite0.tflite", delegate: "GPU" },
  runningMode: "VIDEO",
  scoreThreshold: 0.5,
  maxResults: 5,
});

const video = document.getElementById("webcam") as HTMLVideoElement;
video.srcObject = await navigator.mediaDevices.getUserMedia({ video: true });
await video.play();
let lastVideoTime = -1;

function renderLoop() {
  if (video.readyState >= 2 && video.currentTime !== lastVideoTime) {
    lastVideoTime = video.currentTime;
    const result = objectDetector.detectForVideo(video, performance.now());
    console.log(result.detections);
  }
  requestAnimationFrame(renderLoop);
}
renderLoop();

Android (Kotlin)

Gradleimplementation("com.google.mediapipe:tasks-vision:latest.release")Sample code·Setup guide

Image

import com.google.mediapipe.framework.image.BitmapImageBuilder
import com.google.mediapipe.framework.image.MPImage
import com.google.mediapipe.tasks.core.BaseOptions
import com.google.mediapipe.tasks.vision.core.RunningMode
import com.google.mediapipe.tasks.vision.objectdetector.ObjectDetector

val baseOptions = BaseOptions.builder()
    .setModelAssetPath("efficientdet_lite0.tflite")
    .build()
val options = ObjectDetector.ObjectDetectorOptions.builder()
    .setBaseOptions(baseOptions)
    .setRunningMode(RunningMode.IMAGE)
    .setScoreThreshold(0.5f)
    .setMaxResults(5)
    .build()

val objectDetector = ObjectDetector.createFromOptions(context, options)

// Synchronous single-image call (no temporal tracking across calls)
val mpImage = BitmapImageBuilder(bitmap).build()
val result = objectDetector.detect(mpImage)

Video

import com.google.mediapipe.framework.image.BitmapImageBuilder
import com.google.mediapipe.framework.image.MPImage
import com.google.mediapipe.tasks.core.BaseOptions
import com.google.mediapipe.tasks.vision.core.RunningMode
import com.google.mediapipe.tasks.vision.objectdetector.ObjectDetector

val baseOptions = BaseOptions.builder()
    .setModelAssetPath("efficientdet_lite0.tflite")
    .build()
val options = ObjectDetector.ObjectDetectorOptions.builder()
    .setBaseOptions(baseOptions)
    .setRunningMode(RunningMode.VIDEO)
    .setScoreThreshold(0.5f)
    .setMaxResults(5)
    .build()

val objectDetector = ObjectDetector.createFromOptions(context, options)

// Synchronous: processes every frame in order with monotonic timestamps (no frame drops)
val mpImage = BitmapImageBuilder(frameBitmap).build()
val result = objectDetector.detectForVideo(mpImage, frameTimestampMs)

Livestream

import android.os.SystemClock
import android.util.Log
import com.google.mediapipe.framework.image.BitmapImageBuilder
import com.google.mediapipe.framework.image.MPImage
import com.google.mediapipe.tasks.core.BaseOptions
import com.google.mediapipe.tasks.vision.core.RunningMode
import com.google.mediapipe.tasks.vision.objectdetector.ObjectDetector

val baseOptions = BaseOptions.builder()
    .setModelAssetPath("efficientdet_lite0.tflite")
    .build()
val options = ObjectDetector.ObjectDetectorOptions.builder()
    .setBaseOptions(baseOptions)
    .setRunningMode(RunningMode.LIVE_STREAM)
    .setScoreThreshold(0.5f)
    .setMaxResults(5)
    .setResultListener { result, _ -> val detections = result.detections() }
    .setErrorListener { error -> Log.e("MediaPipe", error.message.orEmpty()) }
    .build()

val objectDetector = ObjectDetector.createFromOptions(context, options)

// Async: automatically drops incoming camera frames when busy so results stay current
objectDetector.detectAsync(mpImage, SystemClock.uptimeMillis())

Android (Java)

Gradleimplementation 'com.google.mediapipe:tasks-vision:latest.release'Sample code·Setup guide

Image

import com.google.mediapipe.framework.image.BitmapImageBuilder;
import com.google.mediapipe.framework.image.MPImage;
import com.google.mediapipe.tasks.core.BaseOptions;
import com.google.mediapipe.tasks.vision.core.RunningMode;
import com.google.mediapipe.tasks.vision.objectdetector.ObjectDetector;
import com.google.mediapipe.tasks.vision.objectdetector.ObjectDetectorResult;

BaseOptions baseOptions = BaseOptions.builder()
    .setModelAssetPath("efficientdet_lite0.tflite")
    .build();
ObjectDetector.ObjectDetectorOptions options = ObjectDetector.ObjectDetectorOptions.builder()
    .setBaseOptions(baseOptions)
    .setRunningMode(RunningMode.IMAGE)
    .setScoreThreshold(0.5f)
    .setMaxResults(5)
    .build();

ObjectDetector objectDetector = ObjectDetector.createFromOptions(context, options);

// Synchronous single-image call (no temporal tracking across calls)
MPImage mpImage = new BitmapImageBuilder(bitmap).build();
ObjectDetectorResult result = objectDetector.detect(mpImage);

Video

import com.google.mediapipe.framework.image.BitmapImageBuilder;
import com.google.mediapipe.framework.image.MPImage;
import com.google.mediapipe.tasks.core.BaseOptions;
import com.google.mediapipe.tasks.vision.core.RunningMode;
import com.google.mediapipe.tasks.vision.objectdetector.ObjectDetector;
import com.google.mediapipe.tasks.vision.objectdetector.ObjectDetectorResult;

BaseOptions baseOptions = BaseOptions.builder()
    .setModelAssetPath("efficientdet_lite0.tflite")
    .build();
ObjectDetector.ObjectDetectorOptions options = ObjectDetector.ObjectDetectorOptions.builder()
    .setBaseOptions(baseOptions)
    .setRunningMode(RunningMode.VIDEO)
    .setScoreThreshold(0.5f)
    .setMaxResults(5)
    .build();

ObjectDetector objectDetector = ObjectDetector.createFromOptions(context, options);

// Synchronous: processes every frame in order with monotonic timestamps (no frame drops)
MPImage mpImage = new BitmapImageBuilder(frameBitmap).build();
ObjectDetectorResult result = objectDetector.detectForVideo(mpImage, frameTimestampMs);

Livestream

import android.os.SystemClock;
import android.util.Log;
import com.google.mediapipe.framework.image.BitmapImageBuilder;
import com.google.mediapipe.framework.image.MPImage;
import com.google.mediapipe.tasks.core.BaseOptions;
import com.google.mediapipe.tasks.vision.core.RunningMode;
import com.google.mediapipe.tasks.vision.objectdetector.ObjectDetector;

BaseOptions baseOptions = BaseOptions.builder()
    .setModelAssetPath("efficientdet_lite0.tflite")
    .build();
ObjectDetector.ObjectDetectorOptions options = ObjectDetector.ObjectDetectorOptions.builder()
    .setBaseOptions(baseOptions)
    .setRunningMode(RunningMode.LIVE_STREAM)
    .setScoreThreshold(0.5f)
    .setMaxResults(5)
    .setResultListener((result, inputImage) -> { var detections = result.detections(); })
    .setErrorListener(error -> Log.e("MediaPipe", String.valueOf(error.getMessage())))
    .build();

ObjectDetector objectDetector = ObjectDetector.createFromOptions(context, options);

// Async: automatically drops incoming camera frames when busy so results stay current
objectDetector.detectAsync(mpImage, SystemClock.uptimeMillis());

iOS

SwiftPMhttps://github.com/google-ai-edge/mediapipe → MediaPipeTasksVisionPackage.swift·Sample code·Setup guide

Image

import Foundation
import MediaPipeTasksVision

guard let modelPath = Bundle.main.path(forResource: "efficientdet_lite0", ofType: "tflite") else { return }

let options = ObjectDetectorOptions()
options.baseOptions.modelAssetPath = modelPath
options.runningMode = .image
options.scoreThreshold = 0.5
options.maxResults = 5

let objectDetector = try ObjectDetector(options: options)

// Synchronous single-image call (stateless across images)
let mpImage = try MPImage(uiImage: image)
let result = try objectDetector.detect(image: mpImage)

Video

import Foundation
import MediaPipeTasksVision

guard let modelPath = Bundle.main.path(forResource: "efficientdet_lite0", ofType: "tflite") else { return }

let options = ObjectDetectorOptions()
options.baseOptions.modelAssetPath = modelPath
options.runningMode = .video
options.scoreThreshold = 0.5
options.maxResults = 5

let objectDetector = try ObjectDetector(options: options)

// Synchronous: processes every frame in order with monotonic timestamps (no frame drops)
let mpImage = try MPImage(pixelBuffer: pixelBuffer)
let result = try objectDetector.detect(videoFrame: mpImage, timestampInMilliseconds: timestampMs)

Livestream

import Foundation
import MediaPipeTasksVision

final class ObjectDetectorStreamService: NSObject {
    private var objectDetector: ObjectDetector?

    func setUp() throws {
        guard let modelPath = Bundle.main.path(
            forResource: "efficientdet_lite0",
            ofType: "tflite"
        ) else { return }

        let options = ObjectDetectorOptions()
        options.baseOptions.modelAssetPath = modelPath
        options.runningMode = .liveStream
        options.scoreThreshold = 0.5
        options.maxResults = 5
        options.objectDetectorLiveStreamDelegate = self

        objectDetector = try ObjectDetector(options: options)
    }

    func detectAsync(mpImage: MPImage, timestampMs: Int) throws {
        // Async: automatically drops incoming camera frames when busy so results stay current
        try objectDetector?.detectAsync(image: mpImage, timestampInMilliseconds: timestampMs)
    }
}

extension ObjectDetectorStreamService: ObjectDetectorLiveStreamDelegate {
    func objectDetector(
        _ objectDetector: ObjectDetector,
        didFinishDetection result: ObjectDetectorResult?,
        timestampInMilliseconds: Int,
        error: (any Error)?
    ) {
        guard let result else { return }
        DispatchQueue.main.async {
            let detections = result.detections
        }
    }
}

Python

pippip install mediapipeSample code·Setup guide

Image

import mediapipe as mp

BaseOptions = mp.tasks.BaseOptions
ObjectDetector = mp.tasks.vision.ObjectDetector
ObjectDetectorOptions = mp.tasks.vision.ObjectDetectorOptions
VisionRunningMode = mp.tasks.vision.RunningMode

options = ObjectDetectorOptions(
    base_options=BaseOptions(model_asset_path="efficientdet_lite0.tflite"),
    running_mode=VisionRunningMode.IMAGE,
    score_threshold=0.5,
    max_results=5,
)

with ObjectDetector.create_from_options(options) as detector:
    # Synchronous single-image call (stateless across images)
    mp_image = mp.Image.create_from_file("input.jpg")
    result = detector.detect(mp_image)

Video

import mediapipe as mp

BaseOptions = mp.tasks.BaseOptions
ObjectDetector = mp.tasks.vision.ObjectDetector
ObjectDetectorOptions = mp.tasks.vision.ObjectDetectorOptions
VisionRunningMode = mp.tasks.vision.RunningMode

options = ObjectDetectorOptions(
    base_options=BaseOptions(model_asset_path="efficientdet_lite0.tflite"),
    running_mode=VisionRunningMode.VIDEO,
    score_threshold=0.5,
    max_results=5,
)

with ObjectDetector.create_from_options(options) as detector:
    # Synchronous: processes every frame in order with monotonic timestamps (no frame drops)
    for mp_image, timestamp_ms in decoded_video_frames:
        result = detector.detect_for_video(mp_image, timestamp_ms)

Livestream

import mediapipe as mp

BaseOptions = mp.tasks.BaseOptions
ObjectDetector = mp.tasks.vision.ObjectDetector
ObjectDetectorOptions = mp.tasks.vision.ObjectDetectorOptions
VisionRunningMode = mp.tasks.vision.RunningMode

def on_result(result, output_image: mp.Image, timestamp_ms: int):
    print(f"Detected {len(result.detections)} object(s) at {timestamp_ms} ms")

options = ObjectDetectorOptions(
    base_options=BaseOptions(model_asset_path="efficientdet_lite0.tflite"),
    running_mode=VisionRunningMode.LIVE_STREAM,
    score_threshold=0.5,
    max_results=5,
    result_callback=on_result,
)

with ObjectDetector.create_from_options(options) as detector:
    # Async: automatically drops incoming camera frames when busy so results stay current
    mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=rgb_frame)
    detector.detect_async(mp_image, timestamp_ms)

Task details

This section describes the capabilities, inputs, outputs, and configuration options of this task.

Features

Input image processing - Processing includes image rotation, resizing, normalization, and color space conversion.
Label map locale - Set the language used for display names.
Score threshold - Filter results based on prediction scores.
Top-k detection - Limit the number of detection results.
Label allowlist and denylist - Specify the categories detected.

Inputs

The Object Detector accepts an input of one of the following data types:

Still images
Decoded video frames
Live video feed

See Prepare vision input data for MPImage, mp.Image, and browser media examples.

Outputs

The Object Detector outputs the following results for detected objects:

Category of object
Probability score
Bounding box coordinates

Configuration options

This task has the following configuration options:

Web

Option Name	Description	Value Range	Default Value
`runningMode`	Sets the running mode for the task: `IMAGE`: Single image inputs. `VIDEO`: Decoded video frames and live camera streams.	{`IMAGE`, `VIDEO`}	`IMAGE`
`displayNamesLocale`	Locale code for display names provided in the model metadata, if available. You can add localized labels to custom models with the LiteRT Metadata Writer API.	Locale code	`"en"`
`maxResults`	Maximum number of top-scored detection results to return (`-1` returns all results).	Integer	`-1`
`scoreThreshold`	Prediction score threshold overriding the model metadata default. Detections lower than this score are rejected.	Float `[0.0, 1.0]`	Not set
`categoryAllowlist`	Optional list of allowed category names. Mutually exclusive with `categoryDenylist`.	List of strings	Not set
`categoryDenylist`	Optional list of excluded category names. Mutually exclusive with `categoryAllowlist`.	List of strings	Not set

Android

Option Name	Description	Value Range	Default Value
`runningMode`	Sets the running mode for the task: `IMAGE`: Single image inputs. `VIDEO`: Decoded frames of a video. `LIVE_STREAM`: Livestream of camera input data (requires asynchronous listener/delegate).	{`IMAGE`, `VIDEO`, `LIVE_STREAM`}	`IMAGE`
`displayNamesLocale`	Locale code for display names provided in the model metadata, if available. You can add localized labels to custom models with the LiteRT Metadata Writer API.	Locale code	`"en"`
`maxResults`	Maximum number of top-scored detection results to return (`-1` returns all results).	Integer	`-1`
`scoreThreshold`	Prediction score threshold overriding the model metadata default. Detections lower than this score are rejected.	Float `[0.0, 1.0]`	Not set
`categoryAllowlist`	Optional list of allowed category names. Mutually exclusive with `categoryDenylist`.	List of strings	Not set
`categoryDenylist`	Optional list of excluded category names. Mutually exclusive with `categoryAllowlist`.	List of strings	Not set
`resultListener`	Asynchronous callback for `LIVE_STREAM` mode results.	`OutputHandler.ResultListener<ObjectDetectorResult, MPImage>`	Not set
`errorListener`	Optional error listener for `LIVE_STREAM` mode.	`ErrorListener`	Not set

iOS

Option Name	Description	Value Range	Default Value
`runningMode`	Sets the running mode for the task: `IMAGE`: Single image inputs. `VIDEO`: Decoded frames of a video. `LIVE_STREAM`: Livestream of camera input data (requires asynchronous listener/delegate).	{`IMAGE`, `VIDEO`, `LIVE_STREAM`}	`IMAGE`
`displayNamesLocale`	Locale code for display names provided in the model metadata, if available. You can add localized labels to custom models with the LiteRT Metadata Writer API.	Locale code	`"en"`
`maxResults`	Maximum number of top-scored detection results to return (`-1` returns all results).	Integer	`-1`
`scoreThreshold`	Prediction score threshold overriding the model metadata default. Detections lower than this score are rejected.	Float `[0.0, 1.0]`	Not set
`categoryAllowlist`	Optional list of allowed category names. Mutually exclusive with `categoryDenylist`.	List of strings	Not set
`categoryDenylist`	Optional list of excluded category names. Mutually exclusive with `categoryAllowlist`.	List of strings	Not set
`objectDetectorLiveStreamDelegate`	Asynchronous delegate for `LIVE_STREAM` mode results (invoked on a serial background queue).	`ObjectDetectorLiveStreamDelegate`	Not set

Python

Option Name	Description	Value Range	Default Value
`running_mode`	Sets the running mode for the task: `IMAGE`: Single image inputs. `VIDEO`: Decoded frames of a video. `LIVE_STREAM`: Livestream of camera input data (requires `result_callback`).	{`IMAGE`, `VIDEO`, `LIVE_STREAM`}	`IMAGE`
`display_names_locale`	Locale code for display names provided in the model metadata, if available. You can add localized labels to custom models with the LiteRT Metadata Writer API.	Locale code	`"en"`
`max_results`	Maximum number of top-scored detection results to return (`-1` returns all results).	Integer	`-1`
`score_threshold`	Prediction score threshold overriding the model metadata default. Detections lower than this score are rejected.	Float `[0.0, 1.0]`	Not set
`category_allowlist`	Optional list of allowed category names. Mutually exclusive with `category_denylist`.	List of strings	Not set
`category_denylist`	Optional list of excluded category names. Mutually exclusive with `category_allowlist`.	List of strings	Not set
`result_callback`	Asynchronous callback function for `LIVE_STREAM` mode results.	`Callable`	Not set

Task output

Each ObjectDetectorResult contains a detections list of bounding boxes and ranked class labels:

Field	Type / Coordinate Space	Description
`boundingBox`	`BoundingBox` (pixels)	Unnormalized pixel rectangle (`originX`, `originY`, `width`, `height`) in input image coordinates (`bounding_box` in Python).
`categories`	`Category[]` (`score` in `[0.0, 1.0]`)	Ranked object class labels (`categoryName`, `displayName`, `index`, `score`).

Example `ObjectDetectorResult` payload

The following example shows two detected objects (dog) with their pixel bounding boxes and confidence scores:

{
  detections: [
    {
      boundingBox: { originX: 355, originY: 133, width: 190, height: 206 }, // pixel coordinates
      categories: [{ index: 17, score: 0.7383, categoryName: "dog" }]
    },
    {
      boundingBox: { originX: 103, originY: 15, width: 138, height: 369 },
      categories: [{ index: 17, score: 0.7305, categoryName: "dog" }]
    }
  ]
}

Models

The Object Detector requires an object detection model to be downloaded and stored in your project directory. If you don't already have a model, start with the default, recommended model. The other models presented in this section make trade-offs between latency and accuracy.

EfficientDet-Lite0 model (recommended)

The EfficientDet-Lite0 model uses an EfficientNet-Lite0 backbone with a 320x320 input size and BiFPN feature network. The model was trained with the COCO dataset, a large-scale object detection dataset that contains 1.5 million object instances and 80 object labels. See the full list of supported labels. EfficientDet-Lite0 is available as an int8, float16, or float32 model. This model is recommended because it strikes a balance between latency and accuracy. It is both accurate and lightweight enough for many use cases.

Model name	Input shape	Quantization type	Versions
EfficientDet-Lite0 (int8)	320 x 320	int8	Latest
EfficientDet-Lite0 (float 16)	320 x 320	float 16	Latest
EfficientDet-Lite0 (float 32)	320 x 320	None (float32)	Latest

EfficientDet-Lite2 model

The EfficientDet-Lite2 model uses an EfficientNet-Lite2 backbone with a 448x448 input size and BiFPN feature network. The model was trained with the COCO dataset, a large-scale object detection dataset that contains 1.5 million object instances and 80 object labels. See the full list of supported labels. EfficientDet-Lite2 is available as an int8, float16, or float32 model. This model is generally more accurate than EfficientDet-Lite0, but is also slower and more memory intensive. This model is appropriate for use cases where accuracy is a higher priority than speed and size.

Model name	Input shape	Quantization type	Versions
EfficientDet-Lite2 (int8)	448 x 448	int8	Latest
EfficientDet-Lite2 (float 16)	448 x 448	float 16	Latest
EfficientDet-Lite2 (float 32)	448 x 448	None (float32)	Latest

SSD MobileNetV2 model

The SSD MobileNetV2 model uses a MobileNetV2 backbone with a 256x256 input size and SSD feature network. The model was trained with the COCO dataset, a large-scale object detection dataset that contains 1.5 million object instances and 80 object labels. See the full list of supported labels. SSD MobileNetV2 is available as a float 16 and float 32 model. This model is faster and lighter than EfficientDet-Lite0, but is also generally less accurate. This model is appropriate for use cases that require a fast, lightweight model that sacrifices some accuracy.

Model name	Input shape	Quantization type	Versions
SSDMobileNet-V2 (float 16)	256 x 256	float 16	Latest
SSDMobileNet-V2 (float 32)	256 x 256	None (float32)	Latest

Model requirements and metadata

This section describes the requirements for custom models if you decide to build a model to use with this task. Custom models must be in LiteRT format and must include metadata describing the operating parameters of the model.

Design requirements

Input	Shape	Description
Input image	Float32 tensor of shape[1, height, width, 3]	The normalized input image.

Output	Shape	Description
`detection_boxes`	Float32 tensor of shape [1, num_boxes, 4]	Box location of each detected object.
`detection_classes`	Float32 tensor of shape [1, num_boxes]	Indexes of the class names for each detected object.
`detection_scores`	float32 tensor of shape [1, num_boxes]	Prediction scores for each detected object.
`num_boxes`	Float32 tensor of size 1	The number of detected boxes.

Metadata requirements

Parameter	Description
`input_norm_mean`	The mean value used in the input tensor normalization.
`input_norm_std`	The standard deviation used in the input tensor normalization.
`label_file_paths`	The paths to the category tensor label files. If the model does not have any label files, pass an empty list.
`score_calibration_md`	Information on the score calibration operation in the classification tensor. This parameter is not required if the model does not use score calibration.

Task benchmarks

Here are the task benchmarks for the preceding pre-trained models. The latency result is the average latency on Pixel 6 using CPU / GPU.

Model name	Device	CPU latency	GPU latency
EfficientDet-Lite0 float32 model	Pixel 6	61.30ms	27.83ms
EfficientDet-Lite0 float16 model	Pixel 6	53.97ms	27.97ms
EfficientDet-Lite0 int8 model	Pixel 6	29.31ms	-
EfficientDet-Lite2 float32 model	Pixel 6	197.98ms	41.15ms
EfficientDet-Lite2 float16 model	Pixel 6	198.77ms	47.31ms
EfficientDet-Lite2 int8 model	Pixel 6	70.91ms	-
SSD MobileNetV2 float32 model	Pixel 6	36.30ms	24.01ms
SSD MobileNetV2 float16 model	Pixel 6	37.35ms	28.16ms

Object Detector Stay organized with collections Save and categorize content based on your preferences.

Quickstart

Web

Image

Video

Livestream

Android (Kotlin)

Image

Video

Livestream

Android (Java)

Image

Video

Livestream

iOS

Image

Video

Livestream

Python

Image

Video

Livestream

Task details

Features

Configuration options

Web

Android

iOS

Python

Task output

Example ObjectDetectorResult payload

Models

EfficientDet-Lite0 model (recommended)

EfficientDet-Lite2 model

SSD MobileNetV2 model

Model requirements and metadata

Design requirements

Metadata requirements

Task benchmarks

Object Detector

Example `ObjectDetectorResult` payload