Skip to main content
Pre-release

This release is a pre-release and may not be stable for production use.

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 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.

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 files for tflite-model-maker-nightly 0.4.3.dev202306070508

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for tflite-model-maker-nightly 0.4.3.dev202306070508
File Size Uploaded
tflite-model-maker-nightly-0.4.3.dev202306070508.tar.gz 322.6 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for tflite-model-maker-nightly 0.4.3.dev202306070508
File Interpreter ABI Platform
tflite_model_maker_nightly-0.4.3.dev202306070508-py3-none-any.whl Python 3 none any Details

Total release size: 900.7 kB

Release files / tflite-model-maker-nightly-0.4.3.dev202306070508.tar.gz

Download URL tflite-model-maker-nightly-0.4.3.dev202306070508.tar.gz
Size 322.6 kB
Tags Source
SHA-256 checksum
How to use checksums
a3e49100bdaffd1818b588051a772f0e2ed042f4fa1daca53ef6a99e72c08385
BLAKE2b-256 checksum
How to use checksums
cfc32f1e5ba8396418f866dd32c54725411c0e3ac739d2be0deabc9e0de9f626
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.7.1 importlib_metadata/6.6.0 pkginfo/1.9.6 requests/2.31.0 requests-toolbelt/1.0.0 tqdm/4.65.0 CPython/3.7.16

Release files / tflite_model_maker_nightly-0.4.3.dev202306070508-py3-none-any.whl

Download URL tflite_model_maker_nightly-0.4.3.dev202306070508-py3-none-any.whl
Size 578.0 kB
Tags Python 3
SHA-256 checksum
How to use checksums
44ef69602bfb73c7033e6f3b408ee06d7df1ac60230fa1bd8a141b4e2ac0ad6f
BLAKE2b-256 checksum
How to use checksums
47c0110cb016ab4bb8aaa445b6d1d8578f8cf20420f8d671559720eac2b546f8
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.7.1 importlib_metadata/6.6.0 pkginfo/1.9.6 requests/2.31.0 requests-toolbelt/1.0.0 tqdm/4.65.0 CPython/3.7.16

Release history Release notifications | RSS feed

This release
Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page