To label objects in an image, pass theVisionImageobject to theImageLabeler'sprocessImage()method.
First, get an instance ofImageLabeler.
Swift
letlabeler=ImageLabeler.imageLabeler()// Or, to set the minimum confidence required:// let options = ImageLabelerOptions()// options.confidenceThreshold = 0.7// let labeler = ImageLabeler.imageLabeler(options: options)
Objective-C
MLKImageLabeler*labeler=[MLKImageLabelerimageLabeler];// Or, to set the minimum confidence required:// MLKImageLabelerOptions *options =// [[MLKImageLabelerOptions alloc] init];// options.confidenceThreshold = 0.7;// MLKImageLabeler *labeler =// [MLKImageLabeler imageLabelerWithOptions:options];
If image labeling succeeds, the completion handler receives an array ofImageLabelobjects. EachImageLabelobject represents something that was
labeled in the image. The base model supports400+ different labels.
You can get each label's text description, index among all labels supported by
the model, and the confidence score of the match. For example:
If you use the output of the image labeler to overlay graphics on
the input image, first get the result from ML Kit, then render the image
and overlay in a single step. By doing so, you render to the display surface
only once for each processed input frame. See theupdatePreviewOverlayViewWithLastFramein the ML Kit quickstart sample for an example.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-09-04 UTC."],[[["\u003cp\u003eML Kit's image labeling API lets you identify objects in images using a pre-trained model that recognizes over 400 labels.\u003c/p\u003e\n"],["\u003cp\u003eTo use this API, you need to include the \u003ccode\u003eGoogleMLKit/ImageLabeling\u003c/code\u003e pod, create a \u003ccode\u003eVisionImage\u003c/code\u003e object from your image, and then process it with an \u003ccode\u003eImageLabeler\u003c/code\u003e instance.\u003c/p\u003e\n"],["\u003cp\u003eResults are provided as an array of \u003ccode\u003eImageLabel\u003c/code\u003e objects, each containing the label's text, confidence score, and index.\u003c/p\u003e\n"],["\u003cp\u003eFor real-time applications, leverage the synchronous \u003ccode\u003eresults(in:)\u003c/code\u003e API and manage video frame processing efficiently to maintain optimal frame rates.\u003c/p\u003e\n"]]],["ML Kit allows image labeling using a default model with 400+ labels. To begin, include the `GoogleMLKit/ImageLabeling` pod in your Podfile and open the `.xcworkspace` in Xcode. Input images are prepared using `VisionImage` objects, created from either `UIImage` or `CMSampleBuffer`. An `ImageLabeler` instance processes the image, returning `ImageLabel` objects with text, confidence, and index. For real-time performance, use the synchronous `results(in:)` API with video frames, managing the frame rate.\n"],null,["You can use ML Kit to label objects recognized in an image. The default model provided with\nML Kit supports 400+ different labels.\n\n\u003cbr /\u003e\n\n| **Note:** ML Kit iOS APIs only run on 64-bit devices. If you build your app with 32-bit support, check the device's architecture before using this API.\n\nTry it out\n\n- Play around with [the sample app](https://github.com/googlesamples/mlkit/tree/master/ios/quickstarts/vision) to see an example usage of this API.\n\nBefore you begin\n\n1. Include the following ML Kit pods in your Podfile: \n\n ```\n pod 'GoogleMLKit/ImageLabeling', '8.0.0'\n ```\n2. After you install or update your project's Pods, open your Xcode project using its `.xcworkspace`. ML Kit is supported in Xcode version 12.4 or greater.\n\nNow you are ready to label images.\n\n1. Prepare the input image\n\nCreate a [`VisionImage`](/ml-kit/reference/swift/mlkitvision/api/reference/Classes/VisionImage) object using a `UIImage` or a\n`CMSampleBuffer`.\n\nIf you use a `UIImage`, follow these steps:\n\n- Create a [`VisionImage`](/ml-kit/reference/swift/mlkitvision/api/reference/Classes/VisionImage) object with the `UIImage`. Make sure to specify the correct `.orientation`. \n\n Swift \n\n ```text\n let image = VisionImage(image: UIImage)\n visionImage.orientation = image.imageOrientation\n ```\n\n Objective-C \n\n ```objective-c\n MLKVisionImage *visionImage = [[MLKVisionImage alloc] initWithImage:image];\n visionImage.orientation = image.imageOrientation;\n ```\n\nIf you use a `CMSampleBuffer`, follow these steps:\n\n- Specify the orientation of the image data contained in the\n `CMSampleBuffer`.\n\n To get the image orientation: \n\n Swift \n\n ```gdscript\n func imageOrientation(\n deviceOrientation: UIDeviceOrientation,\n cameraPosition: AVCaptureDevice.Position\n ) -\u003e UIImage.Orientation {\n switch deviceOrientation {\n case .portrait:\n return cameraPosition == .front ? .leftMirrored : .right\n case .landscapeLeft:\n return cameraPosition == .front ? .downMirrored : .up\n case .portraitUpsideDown:\n return cameraPosition == .front ? .rightMirrored : .left\n case .landscapeRight:\n return cameraPosition == .front ? .upMirrored : .down\n case .faceDown, .faceUp, .unknown:\n return .up\n }\n }\n \n ```\n\n Objective-C \n\n ```css+lasso\n - (UIImageOrientation)\n imageOrientationFromDeviceOrientation:(UIDeviceOrientation)deviceOrientation\n cameraPosition:(AVCaptureDevicePosition)cameraPosition {\n switch (deviceOrientation) {\n case UIDeviceOrientationPortrait:\n return cameraPosition == AVCaptureDevicePositionFront ? UIImageOrientationLeftMirrored\n : UIImageOrientationRight;\n\n case UIDeviceOrientationLandscapeLeft:\n return cameraPosition == AVCaptureDevicePositionFront ? UIImageOrientationDownMirrored\n : UIImageOrientationUp;\n case UIDeviceOrientationPortraitUpsideDown:\n return cameraPosition == AVCaptureDevicePositionFront ? UIImageOrientationRightMirrored\n : UIImageOrientationLeft;\n case UIDeviceOrientationLandscapeRight:\n return cameraPosition == AVCaptureDevicePositionFront ? UIImageOrientationUpMirrored\n : UIImageOrientationDown;\n case UIDeviceOrientationUnknown:\n case UIDeviceOrientationFaceUp:\n case UIDeviceOrientationFaceDown:\n return UIImageOrientationUp;\n }\n }\n \n ```\n- Create a [`VisionImage`](/ml-kit/reference/swift/mlkitvision/api/reference/Classes/VisionImage) object using the `CMSampleBuffer` object and orientation: \n\n Swift \n\n ```povray\n let image = VisionImage(buffer: sampleBuffer)\n image.orientation = imageOrientation(\n deviceOrientation: UIDevice.current.orientation,\n cameraPosition: cameraPosition)\n ```\n\n Objective-C \n\n ```objective-c\n MLKVisionImage *image = [[MLKVisionImage alloc] initWithBuffer:sampleBuffer];\n image.orientation =\n [self imageOrientationFromDeviceOrientation:UIDevice.currentDevice.orientation\n cameraPosition:cameraPosition];\n ```\n\n2. Configure and run the image labeler To label objects in an image, pass the `VisionImage` object to the `ImageLabeler`'s `processImage()` method.\n\n\u003cbr /\u003e\n\n1. First, get an instance of `ImageLabeler`.\n\nSwift \n\n```swift\nlet labeler = ImageLabeler.imageLabeler()\n\n// Or, to set the minimum confidence required:\n// let options = ImageLabelerOptions()\n// options.confidenceThreshold = 0.7\n// let labeler = ImageLabeler.imageLabeler(options: options)\n```\n\nObjective-C \n\n```objective-c\nMLKImageLabeler *labeler = [MLKImageLabeler imageLabeler];\n\n// Or, to set the minimum confidence required:\n// MLKImageLabelerOptions *options =\n// [[MLKImageLabelerOptions alloc] init];\n// options.confidenceThreshold = 0.7;\n// MLKImageLabeler *labeler =\n// [MLKImageLabeler imageLabelerWithOptions:options];\n```\n\n1. Then, pass the image to the `processImage()` method:\n\nSwift \n\n```swift\nlabeler.process(image) { labels, error in\n guard error == nil, let labels = labels else { return }\n\n // Task succeeded.\n // ...\n}\n```\n\nObjective-C \n\n```objective-c\n[labeler processImage:image\ncompletion:^(NSArray *_Nullable labels,\n NSError *_Nullable error) {\n if (error != nil) { return; }\n\n // Task succeeded.\n // ...\n}];\n```\n\n3. Get information about labeled objects\n\nIf image labeling succeeds, the completion handler receives an array of\n`ImageLabel` objects. Each `ImageLabel` object represents something that was\nlabeled in the image. The base model supports [400+ different labels](/ml-kit/vision/image-labeling/label-map).\nYou can get each label's text description, index among all labels supported by\nthe model, and the confidence score of the match. For example: \n\nSwift \n\n```swift\nfor label in labels {\n let labelText = label.text\n let confidence = label.confidence\n let index = label.index\n}\n```\n\nObjective-C \n\n```objective-c\nfor (MLKImageLabel *label in labels) {\n NSString *labelText = label.text;\n float confidence = label.confidence;\n NSInteger index = label.index;\n}\n```\n\nTips to improve real-time performance\n\nIf you want to label images in a real-time application, follow these\nguidelines to achieve the best framerates:\n\n- For processing video frames, use the `results(in:)` synchronous API of the image labeler. Call this method from the [`AVCaptureVideoDataOutputSampleBufferDelegate`](https://developer.apple.com/documentation/avfoundation/avcapturevideodataoutputsamplebufferdelegate)'s [`captureOutput(_, didOutput:from:)`](https://developer.apple.com/documentation/avfoundation/avcapturevideodataoutputsamplebufferdelegate/1385775-captureoutput) function to synchronously get results from the given video frame. Keep [`AVCaptureVideoDataOutput`](https://developer.apple.com/documentation/avfoundation/avcapturevideodataoutput)'s [`alwaysDiscardsLateVideoFrames`](https://developer.apple.com/documentation/avfoundation/avcapturevideodataoutput/1385780-alwaysdiscardslatevideoframes) as `true` to throttle calls to the image labeler. If a new video frame becomes available while the image labeler is running, it will be dropped.\n- If you use the output of the image labeler to overlay graphics on the input image, first get the result from ML Kit, then render the image and overlay in a single step. By doing so, you render to the display surface only once for each processed input frame. See the [updatePreviewOverlayViewWithLastFrame](https://github.com/googlesamples/mlkit/blob/master/ios/quickstarts/vision/VisionExample/CameraViewController.swift) in the ML Kit quickstart sample for an example."]]
