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 0.2.5.dev202101252242
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-0.2.5.dev202101252242.tar.gz | 291.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| tflite_model_maker_nightly-0.2.5.dev202101252242-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 788.6 kB
Release files / tflite-model-maker-nightly-0.2.5.dev202101252242.tar.gz
| Download URL | tflite-model-maker-nightly-0.2.5.dev202101252242.tar.gz |
|---|---|
| Size | 291.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
14c2a6698f500aa5bc5d6fbaefddba26e4d072420bc0732ff2e34a3844db05ae
|
|
BLAKE2b-256 checksum How to use checksums |
9c15439636f93999680b180c5396e14982d21f30c5ec222e6c9336a693b40c62
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.3.0 pkginfo/1.7.0 requests/2.25.1 setuptools/52.0.0 requests-toolbelt/0.9.1 tqdm/4.56.0 CPython/3.7.9
|
Release files / tflite_model_maker_nightly-0.2.5.dev202101252242-py3-none-any.whl
| Download URL | tflite_model_maker_nightly-0.2.5.dev202101252242-py3-none-any.whl |
|---|---|
| Size | 496.9 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
84c4af3ea4444b13b394e68674327ff5926e75856009d9b1f8d35fc14394c23f
|
|
BLAKE2b-256 checksum How to use checksums |
e4b0acac22e1389adc2b98ead3df543cf3af5c2f9f9a8ee8ecd19e6d4c7d822e
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.3.0 pkginfo/1.7.0 requests/2.25.1 setuptools/52.0.0 requests-toolbelt/0.9.1 tqdm/4.56.0 CPython/3.7.9
|
