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.
  • Note that you might also need to install sndfile for Audio tasks. On Debian/Ubuntu, you can do so by sudo apt-get install libsndfile1

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 .

TensorFlow Lite Model Maker depends on TensorFlow pip package. For GPU support, please refer to TensorFlow's GPU guide or installation guide.

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.

  • Step 1. Import the required modules.
from tflite_model_maker import image_classifier
from tflite_model_maker.image_classifier import DataLoader
  • Step 2. Load input data specific to an on-device ML app.
data = DataLoader.from_folder('flower_photos/')
  • Step 3. Customize the TensorFlow model.
model = image_classifier.create(data)
  • Step 4. Evaluate the model.
loss, accuracy = model.evaluate()
  • Step 5. 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-0.4.3.dev202303190508.tar.gz.

File metadata

  • Download URL: tflite-model-maker-nightly-0.4.3.dev202303190508.tar.gz
  • Upload date:
  • Size: 322.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/6.1.0 pkginfo/1.9.6 requests/2.28.2 requests-toolbelt/0.10.1 tqdm/4.65.0 CPython/3.7.16

File hashes

Hashes for tflite-model-maker-nightly-0.4.3.dev202303190508.tar.gz
Algorithm Hash digest
SHA256 27b6a16399a684f530a5eaec4adb84540d25cc9f967cc0dc47de6d80c75bff97
MD5 d818c5fd5b74ce52ad872e7bf44921c8
BLAKE2b-256 2989e28809306f5aef8ef3f9f0fd6c1089cf52439af275eae2c7ddbe9dadb83f

See more details on using hashes here.

Provenance

File details

Details for the file tflite_model_maker_nightly-0.4.3.dev202303190508-py3-none-any.whl.

File metadata

File hashes

Hashes for tflite_model_maker_nightly-0.4.3.dev202303190508-py3-none-any.whl
Algorithm Hash digest
SHA256 4877bede8e7b381a461836a8d386664aa52dcb88d3a4111b2f028322a904e4be
MD5 1e0a97c5edce265f426c4e4fd1162d3c
BLAKE2b-256 c39510bb4009d1b43ddd61829649c6b55fcd30974d3bbbbd0e3dfefbe6de8074

See more details on using hashes here.

Provenance

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