Skip to main content

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.

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. 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, text classification and question answer tasks. Meanwhile, we provide demo code for each of them in demo folder.

Project details


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

File details

Details for the file tflite-model-maker-nightly-0.2.4.dev202012012228.tar.gz.

File metadata

  • Download URL: tflite-model-maker-nightly-0.2.4.dev202012012228.tar.gz
  • Upload date:
  • Size: 97.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.6.1 requests/2.25.0 setuptools/50.3.2 requests-toolbelt/0.9.1 tqdm/4.54.0 CPython/3.7.9

File hashes

Hashes for tflite-model-maker-nightly-0.2.4.dev202012012228.tar.gz
Algorithm Hash digest
SHA256 76dd1d6f883f5ff18cc95a7ecaa1f039e8986e7ed26512dc85e3c1d9ab4d790b
MD5 f04f7fab3f0e7af3400735efbbf06bd1
BLAKE2b-256 b285ea4750653105ae9189d47a9e398973145f4feaf9b0c10b64aa357a6552f0

See more details on using hashes here.

File details

Details for the file tflite_model_maker_nightly-0.2.4.dev202012012228-py3-none-any.whl.

File metadata

File hashes

Hashes for tflite_model_maker_nightly-0.2.4.dev202012012228-py3-none-any.whl
Algorithm Hash digest
SHA256 732f771883027f304cbc0af76bfcaf8ee1db081f63a834bb23a402b203821367
MD5 8bb5ccb5895fa7860bba0ef441fc0ad6
BLAKE2b-256 f4c93d3c864cc5c86d6f0220ddde75896e51f91a0f9447fddf448ce88d1a8400

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page