This release is a pre-release and may not be stable for production use.
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.
Installation
There are two ways to install Model Maker.
- Install a prebuilt pip package:
tflite-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 .
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.
- Load input data specific to an on-device ML app.
data = ImageClassifierDataLoader.from_folder('flower_photos/')
- Customize the TensorFlow model.
model = image_classifier.create(data)
- Evaluate the model.
loss, accuracy = model.evaluate()
- 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.
2 maintainers
Release files for tflite-model-maker-nightly 0.2.4.dev202011192220
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| tflite-model-maker-nightly-0.2.4.dev202011192220.tar.gz | 57.6 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| tflite_model_maker_nightly-0.2.4.dev202011192220-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 176.7 kB
Release files / tflite-model-maker-nightly-0.2.4.dev202011192220.tar.gz
| Download URL | tflite-model-maker-nightly-0.2.4.dev202011192220.tar.gz |
|---|---|
| Size | 57.6 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
636786497e274549f57682ec0c80d151a8d7768c57675d390b7814c8dd0ccff7
|
|
BLAKE2b-256 checksum How to use checksums |
51994184059bcc354e3a497a6444c96176861b68f57400f66eefacaa90d0aa85
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.2.0 pkginfo/1.6.1 requests/2.25.0 setuptools/50.3.2 requests-toolbelt/0.9.1 tqdm/4.52.0 CPython/3.7.9
|
Release files / tflite_model_maker_nightly-0.2.4.dev202011192220-py3-none-any.whl
| Download URL | tflite_model_maker_nightly-0.2.4.dev202011192220-py3-none-any.whl |
|---|---|
| Size | 119.1 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
9386ca5c52af6b169eeb28a10126d24738dfb7962f10499b3af69ec160bae4d4
|
|
BLAKE2b-256 checksum How to use checksums |
d4b803d8241b9a6bac1d97f28cffb21beb7981f35253354e9551914fbe9cad10
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.2.0 pkginfo/1.6.1 requests/2.25.0 setuptools/50.3.2 requests-toolbelt/0.9.1 tqdm/4.52.0 CPython/3.7.9
|
