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.

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 .

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.

    1. Import the required modules.
from tflite_model_maker import image_classifier
from tflite_model_maker.image_classifier import DataLoader
    1. Load input data specific to an on-device ML app.
data = DataLoader.from_folder('flower_photos/')
    1. Customize the TensorFlow model.
model = image_classifier.create(data)
    1. Evaluate the model.
loss, accuracy = model.evaluate()
    1. 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 0.3.0.dev202105062252

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 0.3.0.dev202105062252
File Size Uploaded
tflite-model-maker-nightly-0.3.0.dev202105062252.tar.gz 336.4 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for tflite-model-maker-nightly 0.3.0.dev202105062252
File Interpreter ABI Platform
tflite_model_maker_nightly-0.3.0.dev202105062252-py3-none-any.whl Python 3 none any Details

Total release size: 918.6 kB

Release files / tflite-model-maker-nightly-0.3.0.dev202105062252.tar.gz

Download URL tflite-model-maker-nightly-0.3.0.dev202105062252.tar.gz
Size 336.4 kB
Tags Source
SHA-256 checksum
How to use checksums
7ec52fe13752e2411689899af403720362f9a32d30c984bbb20523d16c0b4166
BLAKE2b-256 checksum
How to use checksums
b63046fb3186adc34c264dff584ae3694a2458f165f6b92881fb1422d83819cc
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.7.10

Release files / tflite_model_maker_nightly-0.3.0.dev202105062252-py3-none-any.whl

Download URL tflite_model_maker_nightly-0.3.0.dev202105062252-py3-none-any.whl
Size 582.2 kB
Tags Python 3
SHA-256 checksum
How to use checksums
9c7144bf4635479fd3ec7c9d946e348e6a35dbc87f18aa51481871dbc0a80f7e
BLAKE2b-256 checksum
How to use checksums
d2873b1d3292a14674f4550e124d43532d2f3a6c7d717ec9eef085ac40ba1d7e
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.7.10

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