Instructions to use Synaptics/MobileNetV2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Synaptics/MobileNetV2 with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://Synaptics/MobileNetV2") - Notebooks
- Google Colab
- Kaggle
| license: mit | |
| language: | |
| - en | |
| library_name: keras | |
| tags: | |
| - tests | |
| - tf_model_zoo | |
| # MobileNetV2 | |
| This model - MobileNetV2 is generated from `tf.keras.applications` | |
| using [tf_model_generator.py](https://github.com/syna-astra-dev/iree-synaptics-synpu/blob/main/tests/model_generator/tf_model_generator.py). | |
| The dataset for int8 quantization is done using random data. | |
| Available formats: int16, int8, float32, float16 | |
| The `kaggle.tflite` model is the [original quantized model](https://www.kaggle.com/models/tensorflow/mobilenet-v2/tfLite/1-0-224-quantized) | |
| published by Google. This model uses v1 tflite quantization. | |