Metadata-Version: 2.1
Name: tflite-model-maker
Version: 0.3.2
Summary: TFLite Model Maker: a model customization library for on-device applications.
Home-page: http://github.com/tensorflow/examples
Author: Google LLC
Author-email: packages@tensorflow.org
License: Apache 2.0
Download-URL: https://github.com/tensorflow/examples/tags
Description: # TFLite Model Maker
        
        ## Overview
        
        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.
        
        ## Requirements
        
        *   Refer to
            [requirements.txt](https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/requirements.txt)
            for dependent libraries that're needed to use the library and run the demo
            code.
        *   Note that you might also need to install `sndfile` for Audio tasks.
        On Debian/Ubuntu, you can do so by `sudo apt-get install libsndfile1`
        
        ## Installation
        
        There are two ways to install Model Maker.
        
        *   Install a prebuilt pip package:
            [`tflite-model-maker`](https://pypi.org/project/tflite-model-maker/).
        
        ```shell
        pip install tflite-model-maker
        ```
        
        If you want to install nightly version
        [`tflite-model-maker-nightly`](https://pypi.org/project/tflite-model-maker-nightly/),
        please follow the command:
        
        ```shell
        pip install tflite-model-maker-nightly
        ```
        
        *   Clone the source code from GitHub and install.
        
        ```shell
        git clone https://github.com/tensorflow/examples
        cd examples/tensorflow_examples/lite/model_maker/pip_package
        pip install -e .
        ```
        
        TensorFlow Lite Model Maker depends on TensorFlow
        [pip package](https://www.tensorflow.org/install/pip). For GPU support, please
        refer to TensorFlow's [GPU guide](https://www.tensorflow.org/install/gpu) or
        [installation guide](https://www.tensorflow.org/install).
        
        ## End-to-End Example
        
        For instance, it could have an end-to-end image classification example that
        utilizes this library with just 4 lines of code, each of which representing one
        step of the overall process. For more detail, you could refer to
        [Colab for image classification](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_image_classification.ipynb).
        
        *   Step 1. Import the required modules.
        
        ```python
        from tflite_model_maker import image_classifier
        from tflite_model_maker.image_classifier import DataLoader
        ```
        
        *   Step 2. Load input data specific to an on-device ML app.
        
        ```python
        data = DataLoader.from_folder('flower_photos/')
        ```
        
        *   Step 3. Customize the TensorFlow model.
        
        ```python
        model = image_classifier.create(data)
        ```
        
        *   Step 4. Evaluate the model.
        
        ```python
        loss, accuracy = model.evaluate()
        ```
        
        *   Step 5. Export to Tensorflow Lite model and label file in `export_dir`.
        
        ```python
        model.export(export_dir='/tmp/')
        ```
        
        ## Notebook
        
        Currently, we support image classification, text classification and question
        answer tasks. Meanwhile, we provide demo code for each of them in demo folder.
        
        *   [Overview for TensorFlow Lite Model Maker](https://www.tensorflow.org/lite/guide/model_maker)
        *   [Python API Reference](https://www.tensorflow.org/lite/api_docs/python/tflite_model_maker)
        *   [Colab for image classification](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_image_classification.ipynb)
        *   [Colab for text classification](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_text_classification.ipynb)
        *   [Colab for BERT question answer](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_question_answer.ipynb)
        *   [Colab for object detection](https://www.tensorflow.org/lite/tutorials/model_maker_object_detection)
        
Keywords: tensorflow,lite,model customization,transfer learning
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
