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.dev202206020506.tar.gz.

File metadata

  • Download URL: tflite-model-maker-nightly-0.4.1.dev202206020506.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/1.8.2 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.64.0 CPython/3.7.13

File hashes

Hashes for tflite-model-maker-nightly-0.4.1.dev202206020506.tar.gz
Algorithm Hash digest
SHA256 fe8c550d2f26040ee027a89512a6fb3d64a3aea0a0e773c91caf16c5f24eddb7
MD5 bb34af2a2516dab81fbd32c2d996a1e7
BLAKE2b-256 af28b4f1ab8797bcf11d8a058de8f919ffce6d137a2935943f7f3aafbc6c471e

See more details on using hashes here.

File details

Details for the file tflite_model_maker_nightly-0.4.1.dev202206020506-py3-none-any.whl.

File metadata

File hashes

Hashes for tflite_model_maker_nightly-0.4.1.dev202206020506-py3-none-any.whl
Algorithm Hash digest
SHA256 531e67d2bab5c32bfb444d94089cd65051bfa1666a696f17ea40fc1d00d8fc2e
MD5 10c26681d54f84099b6684ffc27555b7
BLAKE2b-256 7291198fd8835c350e6c53124c0da004b75affcb7a9a685251d23f02f0433ff7

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