Is there way to convert a onnx to tinyml, or tensorflow lite?

 Yes, you can convert an ONNX model to TensorFlow Lite (TFLite), which can then be used for TinyML applications. The conversion process typically involves two steps:

1. Convert ONNX model to TensorFlow model: You can use the onnx-tf package, which provides a converter to transform ONNX models into TensorFlow models. First, you need to install the onnx-tf package:

Yes, you can convert an ONNX model to TensorFlow Lite (TFLite), which can then be used for TinyML applications. The conversion process typically involves two steps:

1. Convert ONNX model to TensorFlow model: You can use the onnx-tf package, which provides a converter to transform ONNX models into TensorFlow models. First, you need to install the onnx-tf package:

bash
pip install onnx-tf

Then, you can use the following Python code to convert your ONNX model to a TensorFlow model:

python
import onnx
from onnx_tf.backend import prepare

# Load the ONNX model
onnx_model = onnx.load("path/to/your/model.onnx")

# Convert the ONNX model to TensorFlow model
tf_model = prepare(onnx_model)

# Export the TensorFlow model to a protobuf file
tf_model.export_graph("path/to/output/tensorflow_model.pb")

2. Convert TensorFlow model to TensorFlow Lite model: Once you have the TensorFlow model, you can use the TensorFlow Lite converter to convert it to a TFLite model. Install TensorFlow, if you haven't already:

bash
pip install tensorflow

Then use the following Python code to convert the TensorFlow model to a TFLite model:

python
import tensorflow as tf

# Load the TensorFlow model
converter = tf.compat.v1.lite.TFLiteConverter.from_frozen_graph(
    "path/to/output/tensorflow_model.pb",
    input_arrays=["input_node_name"],
    output_arrays=["output_node_name"],
)

# Convert the TensorFlow model to a TFLite model
tflite_model = converter.convert()

# Save the TFLite model
with open("path/to/output/tflite_model.tflite", "wb") as f:
    f.write(tflite_model)

Replace "input_node_name" and "output_node_name" with the names of the input and output nodes of your model. You can use tools like Netron to visualize your model and find the input and output node names.

Once you have the TFLite model, you can deploy it to microcontrollers or other edge devices for TinyML applications. Please note that you might need to perform additional optimization, quantization, or model pruning to make the model suitable for deployment on resource-constrained devices. TensorFlow Lite provides tools and techniques for model optimization, which can be found in the official documentation.