Integrate object detectors

    Object detectors can identify which of a known set of objects might be present and provide information about their positions within the given image or a video stream. An object detector is trained to detect the presence and location of multiple classes of objects. For example, a model might be trained with images that contain various pieces of fruit, along with a label that specifies the class of fruit they represent (e.g. an apple, a banana, or a strawberry), and data specifying where each object appears in the image. See the object detection example for more information about object detectors.

    Use the Task Library ObjectDetector API to deploy your custom object detectors or pretrained ones into your mobile apps.

    Key features of the ObjectDetector API

    • Input image processing, including rotation, resizing, and color space conversion.

    • Label map locale.

    • Score threshold to filter results.

    • Top-k detection results.

    • Label allowlist and denylist.

    Supported object detector models

    The following models are guaranteed to be compatible with the ObjectDetector API.

    Run inference in Java

    See the Object Detection reference app for an example of how to use ObjectDetector in an Android app.

    Step 1: Import Gradle dependency and other settings

    Copy the .tflite model file to the assets directory of the Android module where the model will be run. Specify that the file should not be compressed, and add the TensorFlow Lite library to the module’s build.gradle file:

      android 
  
 { 
  
 // Other settings 
  
 // Specify tflite file should not be compressed for the app apk 
  
 aaptOptions 
  
 { 
  
 noCompress 
  
 "tflite" 
  
 } 
 } 
 dependencies 
  
 { 
  
 // Other dependencies 
  
 // Import the Task Vision Library dependency 
  
 implementation 
  
 ' 
 org 
 . 
 tensorflow 
 : 
 tensorflow 
 - 
 lite 
 - 
 task 
 - 
 vision 
 ' 
  
 // Import the GPU delegate plugin Library for GPU inference 
  
 implementation 
  
 ' 
 org 
 . 
 tensorflow 
 : 
 tensorflow 
 - 
 lite 
 - 
 gpu 
 - 
 delegate 
 - 
 plugin 
 ' 
 }

    Step 2: Using the model

      // Initialization 
 ObjectDetectorOptions 
  
 options 
  
 = 
  
 ObjectDetectorOptions 
 . 
 builder 
 () 
  
 . 
 setBaseOptions 
 ( 
 BaseOptions 
 . 
 builder 
 (). 
 useGpu 
 (). 
 build 
 ()) 
  
 . 
 setMaxResults 
 ( 
 1 
 ) 
  
 . 
 build 
 (); 
 ObjectDetector 
  
 objectDetector 
  
 = 
  
 ObjectDetector 
 . 
 createFromFileAndOptions 
 ( 
  
 context 
 , 
  
 modelFile 
 , 
  
 options 
 ); 
 // Run inference 
 List<Detection> 
  
 results 
  
 = 
  
 objectDetector 
 . 
 detect 
 ( 
 image 
 );

    See the source code and javadoc for more options to configure ObjectDetector .

    Run inference in iOS

    Step 1: Install the dependencies

    The Task Library supports installation using CocoaPods. Make sure that CocoaPods is installed on your system. Please see the CocoaPods installation guide for instructions.

    Please see the CocoaPods guide for details on adding pods to an Xcode project.

    Add the TensorFlowLiteTaskVision pod in the Podfile.

     target 'MyAppWithTaskAPI' do
  use_frameworks!
  pod 'TensorFlowLiteTaskVision'
end

    Make sure that the .tflite model you will be using for inference is present in your app bundle.

    Step 2: Using the model

    Swift

      // Imports 
 import 
  
 TensorFlowLiteTaskVision 
 // Initialization 
 guard 
  
 let 
  
 modelPath 
  
 = 
  
 Bundle 
 . 
 main 
 . 
 path 
 ( 
 forResource 
 : 
  
 "ssd_mobilenet_v1" 
 , 
  
 ofType 
 : 
  
 "tflite" 
 ) 
  
 else 
  
 { 
  
 return 
  
 } 
 let 
  
 options 
  
 = 
  
 ObjectDetectorOptions 
 ( 
 modelPath 
 : 
  
 modelPath 
 ) 
 // Configure any additional options: 
 // options.classificationOptions.maxResults = 3 
 let 
  
 detector 
  
 = 
  
 try 
  
 ObjectDetector 
 . 
 detector 
 ( 
 options 
 : 
  
 options 
 ) 
 // Convert the input image to MLImage. 
 // There are other sources for MLImage. For more details, please see: 
 // https://developers.google.com/ml-kit/reference/ios/mlimage/api/reference/Classes/GMLImage 
 guard 
  
 let 
  
 image 
  
 = 
  
 UIImage 
  
 ( 
 named 
 : 
  
 "cats_and_dogs.jpg" 
 ), 
  
 let 
  
 mlImage 
  
 = 
  
 MLImage 
 ( 
 image 
 : 
  
 image 
 ) 
  
 else 
  
 { 
  
 return 
  
 } 
 // Run inference 
 let 
  
 detectionResult 
  
 = 
  
 try 
  
 detector 
 . 
 detect 
 ( 
 mlImage 
 : 
  
 mlImage 
 )

    Objective-C

      // Imports 
 #import <TensorFlowLiteTaskVision/TensorFlowLiteTaskVision.h> 
 // Initialization 
 NSString 
  
 * 
 modelPath 
  
 = 
  
 [[ 
 NSBundle 
  
 mainBundle 
 ] 
  
 pathForResource 
 : 
 @"ssd_mobilenet_v1" 
  
 ofType 
 : 
 @"tflite" 
 ]; 
 TFLObjectDetectorOptions 
  
 * 
 options 
  
 = 
  
 [[ 
 TFLObjectDetectorOptions 
  
 alloc 
 ] 
  
 initWithModelPath 
 : 
 modelPath 
 ]; 
 // Configure any additional options: 
 // options.classificationOptions.maxResults = 3; 
 TFLObjectDetector 
  
 * 
 detector 
  
 = 
  
 [ 
 TFLObjectDetector 
  
 objectDetectorWithOptions 
 : 
 options 
  
 error 
 : 
 nil 
 ]; 
 // Convert the input image to MLImage. 
 UIImage 
  
 * 
 image 
  
 = 
  
 [ 
 UIImage 
  
 imageNamed 
 : 
 @"dogs.jpg" 
 ]; 
 // There are other sources for GMLImage. For more details, please see: 
 // https://developers.google.com/ml-kit/reference/ios/mlimage/api/reference/Classes/GMLImage 
 GMLImage 
  
 * 
 gmlImage 
  
 = 
  
 [[ 
 GMLImage 
  
 alloc 
 ] 
  
 initWithImage 
 : 
 image 
 ]; 
 // Run inference 
 TFLDetectionResult 
  
 * 
 detectionResult 
  
 = 
  
 [ 
 detector 
  
 detectWithGMLImage 
 : 
 gmlImage 
  
 error 
 : 
 nil 
 ];

    See the source code for more options to configure TFLObjectDetector .

    Run inference in Python

    Step 1: Install the pip package

     pip install tflite-support

    Step 2: Using the model

      # Imports 
 from 
  
 tflite_support.task 
  
 import 
 vision 
 from 
  
 tflite_support.task 
  
 import 
 core 
 from 
  
 tflite_support.task 
  
 import 
 processor 
 # Initialization 
 base_options 
 = 
 core 
 . 
 BaseOptions 
 ( 
 file_name 
 = 
 model_path 
 ) 
 detection_options 
 = 
 processor 
 . 
 DetectionOptions 
 ( 
 max_results 
 = 
 2 
 ) 
 options 
 = 
 vision 
 . 
 ObjectDetectorOptions 
 ( 
 base_options 
 = 
 base_options 
 , 
 detection_options 
 = 
 detection_options 
 ) 
 detector 
 = 
 vision 
 . 
 ObjectDetector 
 . 
 create_from_options 
 ( 
 options 
 ) 
 # Alternatively, you can create an object detector in the following manner: 
 # detector = vision.ObjectDetector.create_from_file(model_path) 
 # Run inference 
 image 
 = 
 vision 
 . 
 TensorImage 
 . 
 create_from_file 
 ( 
 image_path 
 ) 
 detection_result 
 = 
 detector 
 . 
 detect 
 ( 
 image 
 )

    See the source code for more options to configure ObjectDetector .

    Run inference in C++

      // Initialization 
 ObjectDetectorOptions 
  
 options 
 ; 
 options 
 . 
 mutable_base_options 
 () 
 - 
> mutable_model_file 
 () 
 - 
> set_file_name 
 ( 
 model_path 
 ); 
 std 
 :: 
 unique_ptr<ObjectDetector> 
  
 object_detector 
  
 = 
  
 ObjectDetector 
 :: 
 CreateFromOptions 
 ( 
 options 
 ). 
 value 
 (); 
 // Create input frame_buffer from your inputs, `image_data` and `image_dimension`. 
 // See more information here: tensorflow_lite_support/cc/task/vision/utils/frame_buffer_common_utils.h 
 std 
 :: 
 unique_ptr<FrameBuffer> 
  
 frame_buffer 
  
 = 
  
 CreateFromRgbRawBuffer 
 ( 
  
 image_data 
 , 
  
 image_dimension 
 ); 
 // Run inference 
 const 
  
 DetectionResult 
  
 result 
  
 = 
  
 object_detector 
 - 
> Detect 
 ( 
 * 
 frame_buffer 
 ). 
 value 
 ();

    See the source code for more options to configure ObjectDetector .

    Example results

    Here is an example of the detection results of ssd mobilenet v1 from TensorFlow Hub.

    dogs

     Results:
 Detection #0 (red):
  Box: (x: 355, y: 133, w: 190, h: 206)
  Top-1 class:
   index       : 17
   score       : 0.73828
   class name  : dog
 Detection #1 (green):
  Box: (x: 103, y: 15, w: 138, h: 369)
  Top-1 class:
   index       : 17
   score       : 0.73047
   class name  : dog

    Render the bounding boxes onto the input image:

    detection output

    Try out the simple CLI demo tool for ObjectDetector with your own model and test data.

    Model compatibility requirements

    The ObjectDetector API expects a TFLite model with mandatory TFLite Model Metadata . See examples of creating metadata for object detectors using the TensorFlow Lite Metadata Writer API .

    The compatible object detector models should meet the following requirements:

    • Input image tensor: (kTfLiteUInt8/kTfLiteFloat32)

      • image input of size [batch x height x width x channels] .
      • batch inference is not supported ( batch is required to be 1).
      • only RGB inputs are supported ( channels is required to be 3).
      • if type is kTfLiteFloat32, NormalizationOptions are required to be attached to the metadata for input normalization.

    • Output tensors must be the 4 outputs of a DetectionPostProcess op, i.e.:

      • Locations tensor (kTfLiteFloat32)
      • tensor of size [1 x num_results x 4] , the inner array representing bounding boxes in the form [top, left, right, bottom].
      • BoundingBoxProperties are required to be attached to the metadata and must specify type=BOUNDARIES and `coordinate_type=RATIO.

      • Classes tensor (kTfLiteFloat32)

      • tensor of size [1 x num_results] , each value representing the integer index of a class.

      • optional (but recommended) label map(s) can be attached as AssociatedFile-s with type TENSOR_VALUE_LABELS, containing one label per line. See the example label file . The first such AssociatedFile (if any) is used to fill the class_name field of the results. The display_name field is filled from the AssociatedFile (if any) whose locale matches the display_names_locale field of the ObjectDetectorOptions used at creation time ("en" by default, i.e. English). If none of these are available, only the index field of the results will be filled.

      • Scores tensor (kTfLiteFloat32)

      • tensor of size [1 x num_results] , each value representing the score of the detected object.

      • Number of detection tensor (kTfLiteFloat32)

      • integer num_results as a tensor of size [1] .
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