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.dev202108092302.tar.gz.

File metadata

  • Download URL: tflite-model-maker-nightly-0.3.3.dev202108092302.tar.gz
  • Upload date:
  • Size: 354.9 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.dev202108092302.tar.gz
Algorithm Hash digest
SHA256 b68b55f0a1fa0bb3c2910679364f60d246d5cdf22ce2b92315ca08e9766597c0
MD5 6f47fd48e60e176ee5e24bf4339e1e32
BLAKE2b-256 ea9309deb778f453a74200f55d147b3eef3815fa6f093f8c138a4c6f6f7cf38c

See more details on using hashes here.

File details

Details for the file tflite_model_maker_nightly-0.3.3.dev202108092302-py3-none-any.whl.

File metadata

File hashes

Hashes for tflite_model_maker_nightly-0.3.3.dev202108092302-py3-none-any.whl
Algorithm Hash digest
SHA256 686d8e65b955a91ca491df38ad498c94d6339894245a57985c39f72512bbce9e
MD5 220575d7c2f55cfda914eede6953b112
BLAKE2b-256 290e8823baa7d7b6e848ecad3530b00863a86ecf8786671c7e9645d58079b680

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