TFLite Model Maker: a model customization library for on-device applications.
Project 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 for dependent libraries that're needed to use the library and run the demo code.
- Note that you might also need to install
sndfilefor Audio tasks. On Debian/Ubuntu, you can do so bysudo apt-get install libsndfile1
Installation
There are two ways to install Model Maker.
- Install a prebuilt pip package:
tflite-model-maker.
pip install tflite-model-maker
If you want to install nightly version
tflite-model-maker-nightly,
please follow the command:
pip install tflite-model-maker-nightly
- Clone the source code from GitHub and install.
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. For GPU support, please refer to TensorFlow's GPU guide or installation guide.
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.
- Step 1. Import the required modules.
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.
data = DataLoader.from_folder('flower_photos/')
- Step 3. Customize the TensorFlow model.
model = image_classifier.create(data)
- Step 4. Evaluate the model.
loss, accuracy = model.evaluate()
- Step 5. Export to Tensorflow Lite model and label file in
export_dir.
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.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for tflite-model-maker-nightly-0.4.1.dev202205250507.tar.gz
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 62f96b39a1a8e4fbcbaca6a5400780cf2e2d32aea7d824a2aaa265089e5683a6 |
|
| MD5 | 42f218b60c3e781b9da9e91aded3399e |
|
| BLAKE2b-256 | cdb2c01c890f3ff86de3c6407231e2fe1e6f23755fad5e396c483ba82414b22d |
Hashes for tflite_model_maker_nightly-0.4.1.dev202205250507-py3-none-any.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | d61168e918ece13dd6ce7f51657e8dd8e68dd01c2abd2b70dfc750aee83b7ae7 |
|
| MD5 | 1b67faf4cc58ffeb1c8274c00dd4b47a |
|
| BLAKE2b-256 | 3649b8d0282382f3e3d11af3312ac3dd7f6fc17bbc14957c8e8e718a74f51504 |
