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.dev202011182221.tar.gz.

File metadata

  • Download URL: tflite-model-maker-nightly-0.2.4.dev202011182221.tar.gz
  • Upload date:
  • Size: 57.4 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/4.52.0 CPython/3.7.9

File hashes

Hashes for tflite-model-maker-nightly-0.2.4.dev202011182221.tar.gz
Algorithm Hash digest
SHA256 be78994cd9815dfe2fc92ca7ad27a924e20c7a1691ce3b7a47b7635c3cbeefc9
MD5 3abeb5beb0be8604993a2ebd9ba3ef34
BLAKE2b-256 fdeecca2af2ef0c87cf6ea1900843b99d46ec48310b5953d246e89605cbf0620

See more details on using hashes here.

File details

Details for the file tflite_model_maker_nightly-0.2.4.dev202011182221-py3-none-any.whl.

File metadata

File hashes

Hashes for tflite_model_maker_nightly-0.2.4.dev202011182221-py3-none-any.whl
Algorithm Hash digest
SHA256 15383d4c33f43bf8d5913b74e6ceb211f359cbf3970beccaa37f0c455d724c83
MD5 dfdad0cf2bb295257ad325cd6edefeff
BLAKE2b-256 87d570c4a61ed25f2189adbab12fda587b9ac1d4ea52d94f7129fb1d1217bd35

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