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.dev202209160507.tar.gz.

File metadata

  • Download URL: tflite-model-maker-nightly-0.4.3.dev202209160507.tar.gz
  • Upload date:
  • Size: 322.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.12.0 pkginfo/1.8.3 requests/2.28.1 requests-toolbelt/0.9.1 tqdm/4.64.1 CPython/3.7.13

File hashes

Hashes for tflite-model-maker-nightly-0.4.3.dev202209160507.tar.gz
Algorithm Hash digest
SHA256 7401499bc59b8619c15b04b8016a9c2aeedbc75fa2dcd39699b2e696b0489062
MD5 05b590977898b43eed88651cacd4c8b1
BLAKE2b-256 bcf4cfd99a0501a19a1699c08e45e25a0e1e5e5fbee5dd8f00fd1e9adb367fe0

See more details on using hashes here.

File details

Details for the file tflite_model_maker_nightly-0.4.3.dev202209160507-py3-none-any.whl.

File metadata

File hashes

Hashes for tflite_model_maker_nightly-0.4.3.dev202209160507-py3-none-any.whl
Algorithm Hash digest
SHA256 5b98d5804215d42b8a214a228ca32f6aac64ce0b0d0de4aa97299ddcd42d0aec
MD5 a07a407328f32659da2d4f150c0d035e
BLAKE2b-256 b84db07e733ab2b0cd913a10789547551351fc0bbbd47956ab47a6d7102d489b

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