Image classification is a common use of machine learning to identify what an
image represents. For example, we might want to know what type of animal appears
in a given picture. The task of predicting what an image represents is called
*image classification* . An image classifier is trained to recognize various
classes of images. For example, a model might be trained to recognize photos
representing three different types of animals: rabbits, hamsters, and dogs. See
the [image classification
example](https://github.com/tensorflow/examples/tree/master/lite/examples/image_classification/)
for more information about image classifiers.

Use the Task Library `ImageClassifier` API to deploy your custom image
classifiers or pretrained ones into your mobile apps.

## Key features of the ImageClassifier API

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

- Region of interest of the input image.

- Label map locale.

- Score threshold to filter results.

- Top-k classification results.

- Label allowlist and denylist.

## Supported image classifier models

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

- Models created by [TensorFlow Lite Model Maker for Image
  Classification](https://developers.google.com/edge/litert/libraries/modify/image_classification).

- The [pretrained image classification models on TensorFlow
  Hub](https://www.kaggle.com/models?id=127,99,239,55,295,21,102,219,279,257,273,155,297,47,146,272,161,294&tfhub-redirect=true).

- Models created by [AutoML Vision Edge Image
  Classification](https://cloud.google.com/vision/automl/docs/edge-quickstart).

- Custom models that meet the [model compatibility
  requirements](https://developers.google.com/edge/litert/libraries/task_library/image_classifier#model-compatibility-requirements).

## Run inference in Java

See the [Image Classification reference
app](https://github.com/tensorflow/examples/blob/master/lite/examples/image_classification/android/README.md)
for an example of how to use `ImageClassifier` 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
    ImageClassifierOptions options =
        ImageClassifierOptions.builder()
            .setBaseOptions(BaseOptions.builder().useGpu().build())
            .setMaxResults(1)
            .build();
    ImageClassifier imageClassifier =
        ImageClassifier.createFromFileAndOptions(
            context, modelFile, options);

    // Run inference
    List<Classifications> results = imageClassifier.classify(image);

See the [source code and
javadoc](https://github.com/tensorflow/tflite-support/blob/master/tensorflow_lite_support/java/src/java/org/tensorflow/lite/task/vision/classifier/ImageClassifier.java)
for more options to configure `ImageClassifier`.

## 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](https://guides.cocoapods.org/using/getting-started.html#getting-started)
for instructions.

Please see the [CocoaPods
guide](https://guides.cocoapods.org/using/using-cocoapods.html) 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: "birds_V1",
                                                ofType: "tflite") else { return }

    let options = ImageClassifierOptions(modelPath: modelPath)

    // Configure any additional options:
    // options.classificationOptions.maxResults = 3

    let classifier = try ImageClassifier.classifier(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: "sparrow.jpg"), let mlImage = MLImage(image: image) else { return }

    // Run inference
    let classificationResults = try classifier.classify(mlImage: mlImage)

#### Objective-C

    // Imports
    #import <TensorFlowLiteTaskVision/TensorFlowLiteTaskVision.h>

    // Initialization
    NSString *modelPath = [[NSBundle mainBundle] pathForResource:@"birds_V1" ofType:@"tflite"];

    TFLImageClassifierOptions *options =
        [[TFLImageClassifierOptions alloc] initWithModelPath:modelPath];

    // Configure any additional options:
    // options.classificationOptions.maxResults = 3;

    TFLImageClassifier *classifier = [TFLImageClassifier imageClassifierWithOptions:options
                                                                              error:nil];

    // Convert the input image to MLImage.
    UIImage *image = [UIImage imageNamed:@"sparrow.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
    TFLClassificationResult *classificationResult =
        [classifier classifyWithGMLImage:gmlImage error:nil];

See the [source
code](https://github.com/tensorflow/tflite-support/blob/master/tensorflow_lite_support/ios/task/vision/sources/TFLImageClassifier.h)
for more options to configure `TFLImageClassifier`.

## 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)
    classification_options = processor.ClassificationOptions(max_results=2)
    options = vision.ImageClassifierOptions(base_options=base_options, classification_options=classification_options)
    classifier = vision.ImageClassifier.create_from_options(options)

    # Alternatively, you can create an image classifier in the following manner:
    # classifier = vision.ImageClassifier.create_from_file(model_path)

    # Run inference
    image = vision.TensorImage.create_from_file(image_path)
    classification_result = classifier.classify(image)

See the [source
code](https://github.com/tensorflow/tflite-support/blob/master/tensorflow_lite_support/python/task/vision/image_classifier.py)
for more options to configure `ImageClassifier`.

## Run inference in C++

    // Initialization
    ImageClassifierOptions options;
    options.mutable_base_options()->mutable_model_file()->set_file_name(model_path);
    std::unique_ptr<ImageClassifier> image_classifier = ImageClassifier::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 ClassificationResult result = image_classifier->Classify(*frame_buffer).value();

See the [source
code](https://github.com/tensorflow/tflite-support/blob/master/tensorflow_lite_support/cc/task/vision/image_classifier.h)
for more options to configure `ImageClassifier`.

## Example results

Here is an example of the classification results of a [bird
classifier](https://tfhub.dev/google/lite-model/aiy/vision/classifier/birds_V1/3).

![sparrow](https://developers.google.com/static/edge/litert/libraries/task_library/images/sparrow.jpg)

    Results:
      Rank #0:
       index       : 671
       score       : 0.91406
       class name  : /m/01bwb9
       display name: Passer domesticus
      Rank #1:
       index       : 670
       score       : 0.00391
       class name  : /m/01bwbt
       display name: Passer montanus
      Rank #2:
       index       : 495
       score       : 0.00391
       class name  : /m/0bwm6m
       display name: Passer italiae

Try out the simple [CLI demo tool for
ImageClassifier](https://github.com/tensorflow/tflite-support/tree/master/tensorflow_lite_support/examples/task/vision/desktop#image-classifier)
with your own model and test data.

## Model compatibility requirements

The `ImageClassifier` API expects a TFLite model with mandatory [TFLite Model
Metadata](https://developers.google.com/edge/litert/conversion/tensorflow/metadata). See examples of creating
metadata for image classifiers using the [TensorFlow Lite Metadata Writer
API](https://developers.google.com/edge/litert/conversion/tensorflow/metadata_writer_tutorial.ipynb#image_classifiers).

The compatible image classifier 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 score tensor (kTfLiteUInt8/kTfLiteFloat32)

  - with `N` classes and either 2 or 4 dimensions, i.e. `[1 x N]` or `[1 x 1
    x 1 x N]`
  - optional (but recommended) label map(s) as AssociatedFile-s with type TENSOR_AXIS_LABELS, containing one label per line. See the [example
    label
    file](https://github.com/tensorflow/tflite-support/blob/master/tensorflow_lite_support/metadata/python/tests/testdata/image_classifier/labels.txt). The first such AssociatedFile (if any) is used to fill the `label` field (named as `class_name` in C++) of the results. The `display_name` field is filled from the AssociatedFile (if any) whose locale matches the `display_names_locale` field of the `ImageClassifierOptions` 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.