# Export the model

This section describes how to export an ONNX model from the PyTorch and TensorFlow deep learning frameworks. It also describes how to [load the model](https://docs.qualcomm.com/doc/80-99100-3/topic/index_Export-the-model.html#load-the-model) for inference.

## Export an ONNX model from PyTorch

[PyTorch](https://pytorch.org/) is a deep learning framework that provides dynamic computation graphs. In the following code, you import the necessary libraries and load a pre-trained ResNet-18 model. You then set the model to evaluation mode and define an example input tensor. Finally, you use the `torch.onnx.export` function to export the model to the ONNX format. You can replace `"model.onnx"` with the preferred output file path.

Do the following to export a PyTorch model to ONNX format:

import torch
    import torchvision.models as models
    
    # Load a pre-trained PyTorch model
    model = models.resnet18(pretrained=True)
    
    # Set the model to evaluation mode
    model.cpu().eval()
    
    # Define example input tensor
    input_tensor = torch.randn(1, 3, 224, 224).cpu()  # Replace with your own input shape
    
    # Export the model to ONNX format
    torch.onnx.export(model, input_tensor, "model.onnx", verbose=True, opset_version=13)
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## Export an ONNX model from TensorFlow

[TensorFlow](https://www.tensorflow.org/) is another deep learning framework that provides both static and dynamic computation graphs. In the following code, you import the necessary libraries and load a pre-trained ResNet-50 model using Keras. You then convert the TensorFlow model to the ONNX format using the `tf2onnx.convert.from_keras` function. Finally, you save the ONNX model to a file using the `tf2onnx.save_model` function.

Here’s how you can export a TensorFlow model to ONNX format:

import tensorflow as tf
    from tensorflow.keras.applications import ResNet50
    import tf2onnx
    
    # Load a pre-trained TensorFlow model
    model = ResNet50(weights='imagenet')
    
    # onnx_model = tf2onnx.convert.from_keras(model)
    input_tensor_spec = (tf.TensorSpec((None, 224, 224, 3), tf.float32, name="input"),)
    
    # Convert the TensorFlow model to ONNX format
    # Save the ONNX model to a file
    model_proto, _ = tf2onnx.convert.from_keras(model, input_signature=input_tensor_spec, opset_version=13, output_path="model.onnx")
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If you have a saved TensorFlow model or saved checkpoint path of TensorFlow model, you can load the model using Keras with TensorFlow or you can directly use the following commands from `tf2onnx` libraries.

# You have path to your saved TensorFlow model
    python -m tf2onnx.convert --saved-model tensorflow-model-path --opset 11 --output model.onnx
    
    # For checkpoint format:
    python -m tf2onnx.convert --checkpoint  tensorflow-model-meta-file-path --output model.onnx --inputs input0:0,input1:0 --outputs output0:0
    
    # For graphdef format:
    python -m tf2onnx.convert --graphdef  tensorflow-model-graphdef-file --output model.onnx --inputs input:0,input:0 --outputs output0:0
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You can refer this [notebook from tf2onnx](https://github.com/onnx/tensorflow-onnx/blob/main/tutorials/ConvertingSSDMobilenetToONNX.ipynb).

## Loading the ONNX model

After exporting the model from your preferred framework to ONNX, you can load it for inference using the `onnx` package. Here’s an example:

import onnx
    import onnx.checker
    
    # Load the ONNX model
    model = onnx.load("path/to/exported_model.onnx")
    
    try:
        onnx.checker.check_model(model)
    except:
    
        onnx.checker.check_model_path("path/to/exported_model.onnx")
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## Next steps

- Use the [QAic model preparator tool](https://docs.qualcomm.com/doc/80-99100-3/topic/index_Prepare-the-model.html#reference-to-model-preparator-tool) to generate optimized models for usage.
- Before compiling, see [Operator and data type support](https://docs.qualcomm.com/doc/80-99100-3/topic/index_Operator-and-Datatype-support.html) to ensure your model’s layers and precision levels are compatible.

Last Published: Aug 25, 2026

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