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.dev202306060509.tar.gz.

File metadata

  • Download URL: tflite-model-maker-nightly-0.4.3.dev202306060509.tar.gz
  • Upload date:
  • Size: 322.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/6.6.0 pkginfo/1.9.6 requests/2.31.0 requests-toolbelt/1.0.0 tqdm/4.65.0 CPython/3.7.16

File hashes

Hashes for tflite-model-maker-nightly-0.4.3.dev202306060509.tar.gz
Algorithm Hash digest
SHA256 2c41610b34f9e45ed0f5fc7a615f246364a6ee1b04a732e1511790f75436755c
MD5 385a12ef166869a90086852f1891a6cf
BLAKE2b-256 c764280eab77c6294cf63792729333b67345f7908b14886ac5d3ba059201ddcb

See more details on using hashes here.

Provenance

File details

Details for the file tflite_model_maker_nightly-0.4.3.dev202306060509-py3-none-any.whl.

File metadata

File hashes

Hashes for tflite_model_maker_nightly-0.4.3.dev202306060509-py3-none-any.whl
Algorithm Hash digest
SHA256 537acfd57a201afa05eabf91082b25f24fc4bde64487975ba0651fd5a3d1a8c6
MD5 b3fe72751dc6d4aacf4caa3404ba2ebf
BLAKE2b-256 2e34c198ef1739733e03e1ad0699e9cafac25457bf15913953689053b7d80c87

See more details on using hashes here.

Provenance

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