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 0.2.2.dev202010192146
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-0.2.2.dev202010192146.tar.gz | 50.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| tflite_model_maker_nightly-0.2.2.dev202010192146-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 154.4 kB
Release files / tflite-model-maker-nightly-0.2.2.dev202010192146.tar.gz
| Download URL | tflite-model-maker-nightly-0.2.2.dev202010192146.tar.gz |
|---|---|
| Size | 50.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
2d9145772c9b35d8e22a00560ae47ab7700b5bbcb544a3365ab09e272fa8ae32
|
|
BLAKE2b-256 checksum How to use checksums |
66880586094993f957d542594a75e4203d54de5da3e03d0aa68a77794b0d809c
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/50.3.2 requests-toolbelt/0.9.1 tqdm/4.50.2 CPython/3.7.9
|
Release files / tflite_model_maker_nightly-0.2.2.dev202010192146-py3-none-any.whl
| Download URL | tflite_model_maker_nightly-0.2.2.dev202010192146-py3-none-any.whl |
|---|---|
| Size | 103.5 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
ff655f05cec43f7405dfac4565dbea18b3209c81c521c25569a5b9eb13521d25
|
|
BLAKE2b-256 checksum How to use checksums |
a8caf31bb4b28022ce101656a39959ac452b200eb730eaf2e239c95bcd05fdd5
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/50.3.2 requests-toolbelt/0.9.1 tqdm/4.50.2 CPython/3.7.9
|
