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-0.3.5.dev202112030607.tar.gz.

File metadata

  • Download URL: tflite-model-maker-nightly-0.3.5.dev202112030607.tar.gz
  • Upload date:
  • Size: 354.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.0 importlib_metadata/4.8.2 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.7.12

File hashes

Hashes for tflite-model-maker-nightly-0.3.5.dev202112030607.tar.gz
Algorithm Hash digest
SHA256 ff7665f38c0736f65db834ab1b8179ea836dd2e7e533b5d258d571e6e3ac9f2e
MD5 5419af6301fdbe649ee222c5db69d58f
BLAKE2b-256 d155a317c6cb6c912c8c50363391e2559a766380491bc277cd2b70bd85f2c790

See more details on using hashes here.

File details

Details for the file tflite_model_maker_nightly-0.3.5.dev202112030607-py3-none-any.whl.

File metadata

File hashes

Hashes for tflite_model_maker_nightly-0.3.5.dev202112030607-py3-none-any.whl
Algorithm Hash digest
SHA256 ee9a6ac0b7662e330831ec5c0dcd14b27f5496f1cddc63048476da8410975f52
MD5 2212226ffd020fd31062525fabb6da34
BLAKE2b-256 6c4d87e90704b41dd68c4d26db15e0102174c0afe5177ca3925ee7edbd84a52f

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