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-0.4.1.dev202206190509.tar.gz.

File metadata

  • Download URL: tflite-model-maker-nightly-0.4.1.dev202206190509.tar.gz
  • Upload date:
  • Size: 367.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.11.4 pkginfo/1.8.3 requests/2.28.0 requests-toolbelt/0.9.1 tqdm/4.64.0 CPython/3.7.13

File hashes

Hashes for tflite-model-maker-nightly-0.4.1.dev202206190509.tar.gz
Algorithm Hash digest
SHA256 ed4d32c5a7b704916b2d5943ba789ab53a4291b20ac974ae561cd7e43cad7f75
MD5 32ff15ca1fbc2cc2ff5793b9a3653d36
BLAKE2b-256 894454b2a19b18a15cb62fce146925d6b6950e75b8efd87440cda345f3bc82e0

See more details on using hashes here.

File details

Details for the file tflite_model_maker_nightly-0.4.1.dev202206190509-py3-none-any.whl.

File metadata

File hashes

Hashes for tflite_model_maker_nightly-0.4.1.dev202206190509-py3-none-any.whl
Algorithm Hash digest
SHA256 56d80f70fb80f901a6aa8cc083821c414756bd60fdedfd99604cc980400255bd
MD5 a3a08b17714002ab4ae2db7193bc4ecc
BLAKE2b-256 0ad6d454041a518c1ddb71a0ee914935f856054d91ca34d6f95f180caa1eb592

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