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.

  • Install a prebuilt pip package.
pip install tflite-model-maker

If you want to install nightly version, 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-0.1.3.dev202009160720.tar.gz.

File metadata

  • Download URL: tflite-model-maker-nightly-0.1.3.dev202009160720.tar.gz
  • Upload date:
  • Size: 49.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/50.3.0 requests-toolbelt/0.9.1 tqdm/4.49.0 CPython/3.7.8

File hashes

Hashes for tflite-model-maker-nightly-0.1.3.dev202009160720.tar.gz
Algorithm Hash digest
SHA256 2cd32e89cf66d45a9df6167fc6b67e616fcc67ad7a394a26e96bc1937ffe5503
MD5 dfcc087aec018af2e8c0405e2444bb8d
BLAKE2b-256 dae63eb88b617f1bfd7b9e789e78bc1826a11fd0ad10e1db2c861e3028373063

See more details on using hashes here.

File details

Details for the file tflite_model_maker_nightly-0.1.3.dev202009160720-py3-none-any.whl.

File metadata

File hashes

Hashes for tflite_model_maker_nightly-0.1.3.dev202009160720-py3-none-any.whl
Algorithm Hash digest
SHA256 ae34b4e71c2b304baa27303f54fbc36f2d47d25a2a81e80f592db8f69291fc60
MD5 1c68b7cab9cc2673b8dd2fa5ed180489
BLAKE2b-256 a985fd2faf67d20b05375421b5ee5ae1fe4adff542cebe15b3a9c96eafb7ec92

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