Custom Models with ML Kit
Both the Image Labeling and the Object Detection & Tracking API offer support for custom image classification models. They are compatible with a selection of high-quality pre-trained models on TensorFlow Hub or your own custom model trained with TensorFlow, AutoML Vision Edge or TensorFlow Lite Model Maker.
Benefits of using ML Kit with custom models
The benefits for using a custom image classification model with ML Kit are:
- Easy-to-use high level APIs - No need to deal with low-level model input/output, handle image pre-/post-processing or building a processing pipeline.
- No need to worry about label mapping yourself, ML Kit extracts the labels from TFLite model metadata and does the mapping for you
- Supports custom models from a wide range of sources, from pre-trained models published on TensorFlow Hub to new models trained with TensorFlow, AutoML Vision Edge or TensorFlow Lite Model Maker
- Optimized for integration with Android’s Camera APIs
And, specifically for Object Detection and Tracking:
- Improve classification accuracy by locating the objects first and only run the classifier on the related image area.
- Provide a real-time interactive experience by providing your users immediate feedback on objects as they are being detected and classified.
Use a pre-trained image classification model
You can use pre-trained TensorFlow Lite models, provided they meet a set of criteria. Through TensorFlow Hub we are offering a set of vetted models - from Google or other model creators - that meet these criteria.
Use a model published on TensorFlow Hub
TensorFlow Hub offers a wide range of pre-trained image classification models - from various model creators - that can be used with the Image Labeling and Object Detection and Tracking APIs. Follow these steps.
- Pick a model from the collection of ML Kit compatible models.
- Download the .tflite model file from the model details page. Where available, pick a model format with metadata.
- Follow our guides for the Image Labeling API or Object Detection and Tracking API on how to bundle model file with your project and use it in your Android or iOS application.
Train your own image classification model
If no pre-trained image classification model fits your needs, there are various ways to train your own TensorFlow Lite model, some of which are outlined and discussed in more detail below.
|Options to train your own image classification model|
|AutoML Vision Edge||
|TensorFlow Lite Model Maker||
|Convert a TensorFlow model to TensorFlow Lite||
AutoML Vision Edge
Image classification models trained using AutoML Vision Edge, which is available through Firebase ML or Google Cloud are supported by the AutoML Image Labeling API. This API also supports download of models that are hosted with Firebase model deployment.
TensorFlow Lite Model Maker
The TFLite Model Maker library simplifies the process of adapting and converting a TensorFlow neural-network model to particular input data when deploying this model for on-device ML applications. You can follow the Colab for Image classification with TensorFlow Lite Model Maker.
To learn more about how to use a model trained with Model Maker in your Android and iOS apps, follow our guides for the Image Labeling API or the Object Detection and Tracking API, depending on your use case.
Models created using TensorFlow Lite converter
If you have an existing TensorFlow image classification model, you can convert it using the TensorFlow Lite converter. Please ensure the model created meets the compatibility requirements below.
TensorFlow Lite model compatibility
You can use any pre-trained TensorFlow Lite image classification model, provided it meets these requirements:
- The model must have only one input tensor with the following constraints:
- The data is in RGB pixel format.
- The data is UINT8 or FLOAT32 type. If the input tensor type is FLOAT32, it must specify the NormalizationOptions by attaching Metadata.
- The tensor has 4 dimensions : BxHxWxC, where:
- B is the batch size. It must be 1 (inference on larger batches is not supported).
- W and H are the input width and height.
- C is the number of expected channels. It must be 3.
- The model must have at least one output tensor with N classes and either 2
or 4 dimensions:
You can add metadata to the TensorFlow Lite file as explained in Adding metadata to TensorFlow Lite model.
To use a model with FLOAT32 input tensor, you must specify the NormalizationOptions in the metadata.
We also recommend that you attach this metadata to the output tensor TensorMetadata:
- A label map specifying the name of each output class, as an AssociatedFile with type TENSOR_AXIS_LABELS (otherwise only the numerical output class indices can be returned)
- A default score threshold below which results are considered too low-confidence to be returned, as a ProcessUnit with ScoreThresholdingOptions