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.dev202104290339

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.dev202104290339
File Size Uploaded
tflite-model-maker-nightly-0.3.0.dev202104290339.tar.gz 325.9 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for tflite-model-maker-nightly 0.3.0.dev202104290339
File Interpreter ABI Platform
tflite_model_maker_nightly-0.3.0.dev202104290339-py3-none-any.whl Python 3 none any Details

Total release size: 893.9 kB

Release files / tflite-model-maker-nightly-0.3.0.dev202104290339.tar.gz

Download URL tflite-model-maker-nightly-0.3.0.dev202104290339.tar.gz
Size 325.9 kB
Tags Source
SHA-256 checksum
How to use checksums
c96328ec9b809bf594fbaad41d4ffa85560ddc7c50ccf85aa98eaeee4b3f0423
BLAKE2b-256 checksum
How to use checksums
cc672885f06a3c18a9dc27b119a01c5783de2e072b1b08326da19434f8785216
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.dev202104290339-py3-none-any.whl

Download URL tflite_model_maker_nightly-0.3.0.dev202104290339-py3-none-any.whl
Size 568.0 kB
Tags Python 3
SHA-256 checksum
How to use checksums
3f585231dc7bfe403bfbbcfb662228cfd5c7c5e414b80188be0e196272fb0a48
BLAKE2b-256 checksum
How to use checksums
be891d3aaf79d59f6d22d6d6d36f1c12a4cd33356d89552a964c38a305b147a2
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