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-0.4.3.dev202303020608.tar.gz
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 1c87f947f418833f30959cccb6e6c45d184b5f10a3172d79310a835a4bf08913 |
|
| MD5 | 8c5151d42d178fe2c2aec6f1208e25ed |
|
| BLAKE2b-256 | 2428a93c5d1c21535a2f80e5c5dfa076ebdc1a7d1ace0a447e5d1f87a8ecf4f8 |
Hashes for tflite_model_maker_nightly-0.4.3.dev202303020608-py3-none-any.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | e05813b526196d57e4e107b7bdc34fa322e064ce477e279858dbe87b37cce5eb |
|
| MD5 | c6ce1fd71936e09ae31809cc31baeae7 |
|
| BLAKE2b-256 | a2cd995be62a8303167f23aec26da3321e7105df233fc67e2b3e056a7aa0bf32 |
