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.
- Note that you might also need to install
sndfilefor Audio tasks. On Debian/Ubuntu, you can do so bysudo apt-get install libsndfile1
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 .
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.
2 maintainers
Release files for tflite-model-maker-nightly 0.3.5.dev202202020606
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-0.3.5.dev202202020606.tar.gz | 355.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| tflite_model_maker_nightly-0.3.5.dev202202020606-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 972.6 kB
Release files / tflite-model-maker-nightly-0.3.5.dev202202020606.tar.gz
| Download URL | tflite-model-maker-nightly-0.3.5.dev202202020606.tar.gz |
|---|---|
| Size | 355.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
dcf14c81472e72137ba1ff0811e3c6cd070e62c37ed069ef4bcd17e3973bc109
|
|
BLAKE2b-256 checksum How to use checksums |
9d29ab1c3e85602bdaca883b11cb336ad1f92508029d137116718b05327f9f5b
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.7.1 importlib_metadata/4.10.1 pkginfo/1.8.2 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.7.12
|
Release files / tflite_model_maker_nightly-0.3.5.dev202202020606-py3-none-any.whl
| Download URL | tflite_model_maker_nightly-0.3.5.dev202202020606-py3-none-any.whl |
|---|---|
| Size | 617.5 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
3486bfe6c05d7361cee095012ca8fe35d8c3615de8e68543db0b35ec4410cde3
|
|
BLAKE2b-256 checksum How to use checksums |
3bd2e680cd576104ba638cc1f29eb2737524a628fe51a247445c0dbdb0297e0b
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.7.1 importlib_metadata/4.10.1 pkginfo/1.8.2 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.7.12
|
