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

Two alternative methods to install Model Maker library with its dependencies.

  • Install directly.
pip install git+https://github.com/tensorflow/examples.git#egg=tensorflow-examples[model_maker]
  • Clone the repo from the HEAD, and then install with pip.
git clone https://github.com/tensorflow/examples
cd examples
pip install .[model_maker]

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. Load input data specific to an on-device ML app.
data = ImageClassifierDataLoader.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 and text classification tasks and provide demo code and colab for each of them in demo folder.

Release files for tflite-model-maker-nightly 0.1.0.dev202007291556

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 0.1.0.dev202007291556
File Size Uploaded
tflite-model-maker-nightly-0.1.0.dev202007291556.tar.gz 41.8 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for tflite-model-maker-nightly 0.1.0.dev202007291556
File Interpreter ABI Platform
tflite_model_maker_nightly-0.1.0.dev202007291556-py3-none-any.whl Python 3 none any Details

Total release size: 123.6 kB

Release files / tflite-model-maker-nightly-0.1.0.dev202007291556.tar.gz

Download URL tflite-model-maker-nightly-0.1.0.dev202007291556.tar.gz
Size 41.8 kB
Tags Source
SHA-256 checksum
How to use checksums
560d6c3e4ed0b544fc2b89d366c4697f84f15f3f0db381ec390bf1d8af29fe97
BLAKE2b-256 checksum
How to use checksums
604aa67819bf9b206acac98c597e3d70f7f56a9f705c9b48a26f174c5d2a7d58
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.2.0 pkginfo/1.5.0.1 requests/2.23.0 setuptools/46.1.3.post20200330 requests-toolbelt/0.9.1 tqdm/4.46.0 CPython/3.7.7

Release files / tflite_model_maker_nightly-0.1.0.dev202007291556-py3-none-any.whl

Download URL tflite_model_maker_nightly-0.1.0.dev202007291556-py3-none-any.whl
Size 81.8 kB
Tags Python 3
SHA-256 checksum
How to use checksums
6db2fb0d1c4a6b7a0ff81aa9e0781c8fbbc6fe7ac2728410113d85b83072f3d5
BLAKE2b-256 checksum
How to use checksums
545c9bc5199b4313da34559dbce6dabdb0d00a40492b5c080948a74aa988ba4e
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.2.0 pkginfo/1.5.0.1 requests/2.23.0 setuptools/46.1.3.post20200330 requests-toolbelt/0.9.1 tqdm/4.46.0 CPython/3.7.7

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