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-0.4.2.dev202208010509.tar.gz.

File metadata

  • Download URL: tflite-model-maker-nightly-0.4.2.dev202208010509.tar.gz
  • Upload date:
  • Size: 368.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.12.0 pkginfo/1.8.3 requests/2.28.1 requests-toolbelt/0.9.1 tqdm/4.64.0 CPython/3.7.13

File hashes

Hashes for tflite-model-maker-nightly-0.4.2.dev202208010509.tar.gz
Algorithm Hash digest
SHA256 997936b7032cfc2fd239dd551ddc1492cd21d9a9648da8aec28b967c2df9e2a0
MD5 ff8a117be9a3dfa80e2ffc066a3f5aff
BLAKE2b-256 2d653dd6b5ee2f8e5c1861db9a8b987aaf4e33fb601ce35779700568e2c2fdc0

See more details on using hashes here.

File details

Details for the file tflite_model_maker_nightly-0.4.2.dev202208010509-py3-none-any.whl.

File metadata

File hashes

Hashes for tflite_model_maker_nightly-0.4.2.dev202208010509-py3-none-any.whl
Algorithm Hash digest
SHA256 d01b2c78ae437b381588849882f71f52b28493fe62f8cd45d10b880a3bf8fca1
MD5 8d3a7306e5df4b3d80097d51589963f7
BLAKE2b-256 866480e3d4e891496afd9af75605b35d5204e94b58eb8f538f5617dd8c2e3583

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