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.dev202209180508.tar.gz.

File metadata

  • Download URL: tflite-model-maker-nightly-0.4.3.dev202209180508.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.dev202209180508.tar.gz
Algorithm Hash digest
SHA256 4ed7d19cb028c9bf15ff4bd81c68ba67c8a798df174213dda5db9ec52951907f
MD5 7ec08f27d62020496f2f45563705b8e1
BLAKE2b-256 f61141b99f28bdf7e3dbfd4c17458894781893de773ac7a538baf5950010fdcb

See more details on using hashes here.

File details

Details for the file tflite_model_maker_nightly-0.4.3.dev202209180508-py3-none-any.whl.

File metadata

File hashes

Hashes for tflite_model_maker_nightly-0.4.3.dev202209180508-py3-none-any.whl
Algorithm Hash digest
SHA256 e5ccfd173f7cebe6ee4ec11c1a3a1d1e3c6f0c493fc0b1c50da3015ef5263253
MD5 bf17ee472ad6bd00441ec1dc6e0d70ec
BLAKE2b-256 b3c4e035dcc092f46ad6ab660c51895ea8ae486776759821e1c3c030eda3f64f

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