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-0.3.3.dev202108032259.tar.gz.

File metadata

  • Download URL: tflite-model-maker-nightly-0.3.3.dev202108032259.tar.gz
  • Upload date:
  • Size: 354.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.6.3 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.0 CPython/3.7.10

File hashes

Hashes for tflite-model-maker-nightly-0.3.3.dev202108032259.tar.gz
Algorithm Hash digest
SHA256 b6df5f6ef56ac9c4624010f60a7bd4c6edf49f46e7de67e9757d7a4c081b3b8b
MD5 711ea6573973f3628de4001f767bd24b
BLAKE2b-256 64ca8fc3782db2e15f026be9a18ed538fa66dc219017adb5f45e8cf0906f5f5c

See more details on using hashes here.

File details

Details for the file tflite_model_maker_nightly-0.3.3.dev202108032259-py3-none-any.whl.

File metadata

File hashes

Hashes for tflite_model_maker_nightly-0.3.3.dev202108032259-py3-none-any.whl
Algorithm Hash digest
SHA256 645d64c501dd0007707fadbac8f99efb853be8b59cc0b2cb1370182e3f1a9d15
MD5 97dac4871bac98fe33f1ae85ec81d33f
BLAKE2b-256 4e11fceb787e06a960a3f0c8c3a7228f9172bb8814acef9fc5f96c061e341b05

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