1# TensorFlow Lite Model Maker 2 3## Overview 4 5The TensorFlow Lite Model Maker library simplifies the process of training a 6TensorFlow Lite model using custom dataset. It uses transfer learning to reduce 7the amount of training data required and shorten the training time. 8 9## Supported Tasks 10 11The Model Maker library currently supports the following ML tasks. Click the 12links below for guides on how to train the model. 13 14Supported Tasks | Task Utility 15------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------ 16Image Classification: [tutorial](https://www.tensorflow.org/lite/tutorials/model_maker_image_classification), [api](https://www.tensorflow.org/lite/api_docs/python/tflite_model_maker/image_classifier) | Classify images into predefined categories. 17Object Detection: [tutorial](https://www.tensorflow.org/lite/tutorials/model_maker_object_detection), [api](https://www.tensorflow.org/lite/api_docs/python/tflite_model_maker/object_detector) | Detect objects in real time. 18Text Classification: [tutorial](https://www.tensorflow.org/lite/tutorials/model_maker_text_classification), [api](https://www.tensorflow.org/lite/api_docs/python/tflite_model_maker/text_classifier) | Classify text into predefined categories. 19BERT Question Answer: [tutorial](https://www.tensorflow.org/lite/tutorials/model_maker_question_answer), [api](https://www.tensorflow.org/lite/api_docs/python/tflite_model_maker/question_answer) | Find the answer in a certain context for a given question with BERT. 20Audio Classification: [tutorial](https://www.tensorflow.org/lite/tutorials/model_maker_audio_classification), [api](https://www.tensorflow.org/lite/api_docs/python/tflite_model_maker/audio_classifier) | Classify audio into predefined categories. 21Recommendation: [demo](https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/demo/recommendation_demo.py), [api](https://www.tensorflow.org/lite/api_docs/python/tflite_model_maker/recommendation) | Recommend items based on the context information for on-device scenario. 22 23If your tasks are not supported, please first use [TensorFlow](https://www.tensorflow.org/guide) 24to retrain a TensorFlow model with transfer learning (following guides like 25[images](https://www.tensorflow.org/tutorials/images/transfer_learning), 26[text](https://www.tensorflow.org/official_models/fine_tuning_bert), 27[audio](https://www.tensorflow.org/tutorials/audio/transfer_learning_audio)) or 28train it from scratch, and then [convert](https://www.tensorflow.org/lite/convert) 29it to TensorFlow Lite model. 30 31## End-to-End Example 32 33Model Maker allows you to train a TensorFlow Lite model using custom datasets in 34just a few lines of code. For example, here are the steps to train an image 35classification model. 36 37```python 38from tflite_model_maker import image_classifier 39from tflite_model_maker.image_classifier import DataLoader 40 41# Load input data specific to an on-device ML app. 42data = DataLoader.from_folder('flower_photos/') 43train_data, test_data = data.split(0.9) 44 45# Customize the TensorFlow model. 46model = image_classifier.create(train_data) 47 48# Evaluate the model. 49loss, accuracy = model.evaluate(test_data) 50 51# Export to Tensorflow Lite model and label file in `export_dir`. 52model.export(export_dir='/tmp/') 53``` 54 55For more details, see the 56[image classification guide](https://www.tensorflow.org/lite/tutorials/model_maker_image_classification). 57 58## Installation 59 60There are two ways to install Model Maker. 61 62* Install a prebuilt pip package. 63 64```shell 65pip install tflite-model-maker 66``` 67 68If you want to install nightly version, please follow the command: 69 70```shell 71pip install tflite-model-maker-nightly 72``` 73 74* Clone the source code from GitHub and install. 75 76```shell 77git clone https://github.com/tensorflow/examples 78cd examples/tensorflow_examples/lite/model_maker/pip_package 79pip install -e . 80``` 81 82TensorFlow Lite Model Maker depends on TensorFlow 83[pip package](https://www.tensorflow.org/install/pip). For GPU drivers, please 84refer to TensorFlow's [GPU guide](https://www.tensorflow.org/install/gpu) or 85[installation guide](https://www.tensorflow.org/install). 86 87## Python API Reference 88 89You can find out Model Maker's public APIs in 90[API reference](https://www.tensorflow.org/lite/api_docs/python/tflite_model_maker). 91