# Convert to QNN for Linux Host on CPU Backend

Note

This is **Part 3** of the Convert to QNN tutorial for Linux host machines. If you have not completed Part 2, please do so [here](https://docs.qualcomm.com/doc/80-63442-10/topic/tutorial_convert_execute_cnn_model.html).

## Transferring over all relevant files

1. On the target device, open a terminal and make a destination folder by running:

mount -o remount,rw /
        mkdir -p /data/local/tmp
        cd /data/local/tmp
        ln -s /etc/ /data/local/tmp
        chmod -R 777 /data/local/tmp
        mkdir -p "/data/local/tmp/qnn_tutorial"
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2. On the host device, use `scp` to transfer `libQnnCpu.so` from your host machine to `/data/local/tmp/qnn_tutorial` on the target device.

scp "${QNN_SDK_ROOT}/lib/${QNN_TARGET_ARCH}/libQnnCpu.so" "${TARGET_USER}@${TARGET_IP}:/data/local/tmp/qnn_tutorial"
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3. Use `scp` to transfer the example built model.

    1. Update the `x64` folder below to the proper folder for your built model. The folder name depends on your host machine’s architecture.

scp "${QNN_MODEL_PATH}" "${TARGET_USER}@${TARGET_IP}:/data/local/tmp/qnn_tutorial"
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4. Transfer the input data, input list, and script from the QNN SDK examples folder into `/data/local/tmp/qnn_tutorial` on the target device using `scp` in a similar way:

scp -r "${QNN_SDK_ROOT}/examples/Models/InceptionV3/data/cropped"  "${TARGET_USER}@${TARGET_IP}:/data/local/tmp/qnn_tutorial"
        scp "${QNN_SDK_ROOT}/examples/Models/InceptionV3/data/target_raw_list.txt"  "${TARGET_USER}@${TARGET_IP}:/data/local/tmp/qnn_tutorial"
        scp "${QNN_SDK_ROOT}/examples/Models/InceptionV3/data/imagenet_slim_labels"  "${TARGET_USER}@${TARGET_IP}:/data/local/tmp/qnn_tutorial"
        scp "${QNN_SDK_ROOT}/examples/Models/InceptionV3/scripts/show_inceptionv3_classifications.py"  "${TARGET_USER}@${TARGET_IP}:/data/local/tmp/qnn_tutorial"
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5. Transfer `qnn-net-run` from `$QNN_SDK_ROOT/bin/$QNN_TARGET_ARCH/qnn-net-run` to `/data/local/tmp/qnn_tutorial` on the target device:

scp "$QNN_SDK_ROOT/bin/$QNN_TARGET_ARCH/qnn-net-run" "${TARGET_USER}@${TARGET_IP}:/data/local/tmp/qnn_tutorial"
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## Doing inferences on the target device processor

1. Open a terminal instance on the target device.

    1. Alternatively, you can `ssh` from your Linux host machine, run the following command to `ssh` into your target device.
    2. These console variables were set in the above instructions for “Transferring all relevant files”.

ssh "${TARGET_USER}@${TARGET_IP}"
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Note

You will have to log in with your target device’s login for that username.
2. Navigate to the directory containing the test files:

cd /data/local/tmp/qnn_tutorial
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3. Run the following command on the target device to execute an inference:

./qnn-net-run \
           --model "./<model_name_here>.so" \
           --input_list "./target_raw_list.txt" \
           --backend "./libQnnCpu.so" \
           --output_dir "./output"
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4. Run the following script on the target device to view the classification results:

Note

You can alternatively copy the output folder back to your host machine with `scp` and run the following script there to avoid having to install Python on your target device.

python3 ".\show_inceptionv3_classifications.py" \
            -i ".\cropped\raw_list.txt" \
            -o "output" \
            -l ".\imagenet_slim_labels.txt"
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5. Verify that the classification results in `output` match the following:

    | File Path | Expected Output |
    | --- | --- |
    | ${QNN\_SDK\_ROOT}/examples/Models/InceptionV3/data/cropped/trash\_bin.raw | 0.777344 413 ashcan |
    | ${QNN\_SDK\_ROOT}/examples/Models/InceptionV3/data/cropped/chairs.raw | 0.253906 832 studio couch |
    | ${QNN\_SDK\_ROOT}/examples/Models/InceptionV3/data/cropped/plastic\_cup.raw | 0.980469 648 measuring cup |
    | ${QNN\_SDK\_ROOT}/examples/Models/InceptionV3/data/cropped/notice\_sign.raw | 0.167969 459 brass |

Last Published: Aug 06, 2026

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