# Running the Inception v3 Model

Overview

The example C++ application in this tutorial is called
[snpe-net-run](https://docs.qualcomm.com/doc/80-63442-10/topic/SNPE_general_tools.html#snpe-net-run). It is a command line executable that executes
a neural network using Qualcomm® Neural Processing SDK APIs.

The required arguments to snpe-net-run are:

- A neural network model in the DLC file format
- An input list file with paths to the input data.

Optional arguments to snpe-net-run are:

- Choice of GPU, DSP or AIP runtimes (default is CPU)
- Output directory (default is ./output)
- Show help description

snpe-net-run creates and populates an output directory with the
results of executing the neural network on the input data.

![../images/neural_network.png](data:image/png;base64,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)

The Qualcomm® Neural Processing SDK provides Linux and Android binaries of
**snpe-net-run** under

- $SNPE\_ROOT/bin/x86\_64-linux-clang
- $SNPE\_ROOT/bin/aarch64-android
- $SNPE\_ROOT/bin/aarch64-oe-linux-gcc8.2
- $SNPE\_ROOT/bin/aarch64-oe-linux-gcc9.3
- $SNPE\_ROOT/bin/aarch64-ubuntu-gcc9.4
- $SNPE\_ROOT/bin/aarch64-oe-linux-gcc11.2

Prerequisites

- The Qualcomm® Neural Processing SDK has been set up following the [Qualcomm (R) Neural Processing SDK
Setup](https://docs.qualcomm.com/doc/80-63442-10/topic/SNPE_general_setup.html) chapter.
- The [Tutorials Setup](https://docs.qualcomm.com/doc/80-63442-10/topic/tutorial_setup.html) has been
completed.
- TensorFlow is installed (see [TensorFlow
Setup](https://docs.qualcomm.com/doc/80-63442-10/topic/SNPE_general_setup.html#tensorflow-setup))

Introduction

The Inception v3 Imagenet classification model is trained to
classify images with 1000 labels.

The examples below shows the steps required to execute a
pretrained **optimized** and optionally **quantized** Inception
v3 model with **snpe-net-run** to classify a set of sample
images. An optimized and quantized model is used in this
example to showcase the DSP and AIP runtimes which execute
quantized 8-bit neural network models.

The DLC for the model used in this tutorial was generated and
optimized using the TensorFlow optimizer tool, during the
[Getting Inception
v3](https://docs.qualcomm.com/doc/80-63442-10/topic/tutorial_setup.html#tutorial_setup_inception_v3) portion
of the [Tutorials Setup](https://docs.qualcomm.com/doc/80-63442-10/topic/tutorial_setup.html), by the script
$SNPE\_ROOT/examples/Models/InceptionV3/scripts/setup\_inceptionv3\_snpe.py.
Additionally, if a fixed-point runtime such as DSP or AIP was
selected when running the setup script, the model was quantized
by [snpe-dlc-quantize](https://docs.qualcomm.com/doc/80-63442-10/topic/SNPE_general_tools.html#snpe-dlc-quantize).

[Learn more about a quantized model](https://docs.qualcomm.com/doc/80-63442-10/topic/quantized_models.html).

Run on Linux Host

Go to the base location for the model and run **snpe-net-run**

cd $SNPE_ROOT/examples/Models/InceptionV3
    snpe-net-run --container dlc/inception_v3_quantized.dlc --input_list data/cropped/raw_list.txt
    Copy to clipboard

After snpe-net-run completes, the results are populated in the
$SNPE\_ROOT/examples/Models/InceptionV3/output directory. There should
be one or more .log files and several Result\_X directories,
each containing a **InceptionV3/Predictions/Reshape\_1:0.raw** file.

One of the inputs is data/cropped/notice\_sign.raw and it was
created from data/cropped/notice\_sign.jpg which looks like
the following:

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)

With this input, snpe-net-run created the output file
$SNPE\_ROOT/examples/Models/InceptionV3/output/Result\_0/InceptionV3/Predictions/Reshape\_1:0.raw.
It holds the output tensor data of 1000 probabilities for the
1000 categories. The element with the highest value represents
the top classification. A python script to interpret the
classification results is provided and can be used as follows:

python3 $SNPE_ROOT/examples/Models/InceptionV3/scripts/show_inceptionv3_classifications_snpe.py -i data/cropped/raw_list.txt \
                                                                                      -o output/ \
                                                                                      -l data/imagenet_slim_labels.txt
    Copy to clipboard

The output should look like the following, showing
classification results for all the images.

Classification results
    <input_files_dir>/notice_sign.raw 0.170401 459 brass
    <input_files_dir>/plastic_cup.raw 0.977711 648 measuring cup
    <input_files_dir>/chairs.raw      0.299139 832 studio couch
    <input_files_dir>/trash_bin.raw   0.747274 413 ashcan
    Copy to clipboard

**Note:** The &lt;input\_files\_dir&gt; above maps to a path such as
$SNPE\_ROOT/examples/Models/InceptionV3/data/cropped/

The output shows the image was classified as “brass”
(index 459 of the labels) with a probability of 0.170401. The
rest of the output can be examined to see the model’s
classification on other images.

**Binary data input**

Note that the Inception v3 image classification model does not
accept jpg files as input. The model expects its input tensor
dimension to be 299x299x3 as a float array. The
scripts/setup\_inceptionv3\_snpe.py script performs a jpg to binary
data conversion by calling scripts/create\_inceptionv3\_raws.py.
The scripts are an example of how jpg images can be
preprocessed to generate input for the Inception v3 model.

Run on Target Platform ( Android/LE/UBUN )

**Select target architecture**

Qualcomm® Neural Processing SDK provides binaries for different target platforms.
Android binaries are  compiled with clang using libc++ STL implementation.
Below are examples for aarch64-android (Android platform)  and
aarch64-oe-linux-gcc11.2 toolchain (LE platform).
Similarly other toolchains for different platforms can be set as SNPE\_TARGET\_ARCH

# For Android targets: architecture: arm64-v8a - compiler: clang - STL: libc++
    export SNPE_TARGET_ARCH=aarch64-android
    
    # Example for LE targets
    export SNPE_TARGET_ARCH=aarch64-oe-linux-gcc11.2
    Copy to clipboard

For simplicity, this tutorial sets the target binaries to
aarch64-android. Following are commands for on host and on target.

**Push libraries and binaries to target**

Push Qualcomm® Neural Processing SDK libraries and the prebuilt snpe-net-run executable to
/data/local/tmp/snpeexample on the Android target. Set SNPE\_TARGET\_DSPARCH
to the DSP architecture of the target Android device.

export SNPE_TARGET_ARCH=aarch64-android
    export SNPE_TARGET_DSPARCH=hexagon-v73
    
    adb shell "mkdir -p /data/local/tmp/snpeexample/$SNPE_TARGET_ARCH/bin"
    adb shell "mkdir -p /data/local/tmp/snpeexample/$SNPE_TARGET_ARCH/lib"
    adb shell "mkdir -p /data/local/tmp/snpeexample/dsp/lib"
    
    adb push $SNPE_ROOT/lib/$SNPE_TARGET_ARCH/*.so \
          /data/local/tmp/snpeexample/$SNPE_TARGET_ARCH/lib
    adb push $SNPE_ROOT/lib/$SNPE_TARGET_DSPARCH/unsigned/*.so \
          /data/local/tmp/snpeexample/dsp/lib
    adb push $SNPE_ROOT/bin/$SNPE_TARGET_ARCH/snpe-net-run \
          /data/local/tmp/snpeexample/$SNPE_TARGET_ARCH/bin
    Copy to clipboard

**Set up enviroment variables**

Set up the library path, the path variable, and the target
architecture in adb shell to run the executable with the -h
argument to see its description.

adb shell
    export SNPE_TARGET_ARCH=aarch64-android
    export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/data/local/tmp/snpeexample/$SNPE_TARGET_ARCH/lib
    export PATH=$PATH:/data/local/tmp/snpeexample/$SNPE_TARGET_ARCH/bin
    snpe-net-run -h
    exit
    Copy to clipboard

**Push model data to Android target**

To execute the Inception v3 classification model on Android target follow these steps:

cd $SNPE_ROOT/examples/Models/InceptionV3
    mkdir data/rawfiles && cp data/cropped/*.raw data/rawfiles/
    adb shell "mkdir -p /data/local/tmp/inception_v3"
    adb push data/rawfiles /data/local/tmp/inception_v3/cropped
    adb push data/target_raw_list.txt /data/local/tmp/inception_v3
    adb push dlc/inception_v3_quantized.dlc /data/local/tmp/inception_v3
    rm -rf data/rawfiles
    Copy to clipboard

**Note:** It may take some time to push the Inception v3 dlc
file to the target.

Running on Android using CPU Runtime

The Android C++ executable is run with the following commands:

adb shell
    export SNPE_TARGET_ARCH=aarch64-android
    export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/data/local/tmp/snpeexample/$SNPE_TARGET_ARCH/lib
    export PATH=$PATH:/data/local/tmp/snpeexample/$SNPE_TARGET_ARCH/bin
    cd /data/local/tmp/inception_v3
    snpe-net-run --container inception_v3_quantized.dlc --input_list target_raw_list.txt
    exit
    Copy to clipboard

The executable will create the results folder:
/data/local/tmp/inception\_v3/output. To pull the output:

adb pull /data/local/tmp/inception_v3/output output_android
    Copy to clipboard

Check the classification results by running the following
python script:

python3 scripts/show_inceptionv3_classifications_snpe.py -i data/target_raw_list.txt \
                                                       -o output_android/ \
                                                       -l data/imagenet_slim_labels.txt
    Copy to clipboard

The output should look like the following, showing
classification results for all the images.

Classification results
    cropped/notice_sign.raw 0.170409 459 brass
    cropped/plastic_cup.raw 0.977708 648 measuring cup
    cropped/chairs.raw      0.299145 832 studio couch
    cropped/trash_bin.raw   0.747256 413 ashcan
    Copy to clipboard

Running on Android using DSP Runtime

Try running on an Android target with the –use\_dsp option as
follows:

Note the extra environment variable ADSP\_LIBRARY\_PATH must be
set to use DSP. (See [DSP Runtime
Environment](https://docs.qualcomm.com/doc/80-63442-10/topic/dsp_runtime.html) for details.)

adb shell
    export SNPE_TARGET_ARCH=aarch64-android
    export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/data/local/tmp/snpeexample/$SNPE_TARGET_ARCH/lib
    export PATH=$PATH:/data/local/tmp/snpeexample/$SNPE_TARGET_ARCH/bin
    export ADSP_LIBRARY_PATH="/data/local/tmp/snpeexample/dsp/lib;/system/lib/rfsa/adsp;/system/vendor/lib/rfsa/adsp;/dsp"
    cd /data/local/tmp/inception_v3
    snpe-net-run --container inception_v3_quantized.dlc --input_list target_raw_list.txt --use_dsp
    exit
    Copy to clipboard

Pull the output into an output\_android\_dsp directory.

adb pull /data/local/tmp/inception_v3/output output_android_dsp
    Copy to clipboard

Check the classification results by running the following
python script:

python3 scripts/show_inceptionv3_classifications_snpe.py -i data/target_raw_list.txt \
                                                       -o output_android_dsp/ \
                                                       -l data/imagenet_slim_labels.txt
    Copy to clipboard

The output should look like the following, showing
classification results for all the images.

Classification results
    cropped/notice_sign.raw 0.175781 459 brass
    cropped/plastic_cup.raw 0.976562 648 measuring cup
    cropped/chairs.raw      0.285156 832 studio couch
    cropped/trash_bin.raw   0.773438 413 ashcan
    Copy to clipboard

Classification results are identical to the run with CPU
runtime, but there are differences in the probabilities
associated with the output labels due to floating point
precision differences.

Running on Android using AIP Runtime

The AIP runtime allows you to run the Inception v3 model on
the HTA. Running the model using the AIP runtime requires setting the
–runtime argument as ‘aip’ in the script
$SNPE\_ROOT/examples/Models/InceptionV3/scripts/setup\_inceptionv3\_snpe.py
to allow HTA-specific metadata to be packed into the DLC that
is required by the AIP runtime. Refer to [Getting Inception
v3](https://docs.qualcomm.com/doc/80-63442-10/topic/tutorial_setup.html#tutorial_setup_inception_v3) for
more details.

Other than that the additional settings for AIP runtime are
quite similar to those for the DSP runtime. Note the extra
environment variable ADSP\_LIBRARY\_PATH must be set to use DSP
(See [DSP Runtime Environment](https://docs.qualcomm.com/doc/80-63442-10/topic/dsp_runtime.html) for details).
Try running on an Android target with the –use\_aip option as follows:

adb shell
    export SNPE_TARGET_ARCH=aarch64-android
    export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/data/local/tmp/snpeexample/$SNPE_TARGET_ARCH/lib
    export PATH=$PATH:/data/local/tmp/snpeexample/$SNPE_TARGET_ARCH/bin
    export ADSP_LIBRARY_PATH="/data/local/tmp/snpeexample/dsp/lib;/system/lib/rfsa/adsp;/system/vendor/lib/rfsa/adsp;/dsp"
    cd /data/local/tmp/inception_v3
    snpe-net-run --container inception_v3_quantized.dlc --input_list target_raw_list.txt --use_aip
    exit
    Copy to clipboard

Pull the output into an output\_android\_aip directory.

adb pull /data/local/tmp/inception_v3/output output_android_aip
    Copy to clipboard

Check the classification results by running the following
python script:

python3 scripts/show_inceptionv3_classifications_snpe.py -i data/target_raw_list.txt \
                                                       -o output_android_aip/ \
                                                       -l data/imagenet_slim_labels.txt
    Copy to clipboard

The output should look like the following, showing
classification results for all the images.

Classification results
    cropped/notice_sign.raw 0.175781 459 brass
    cropped/plastic_cup.raw 0.976562 648 measuring cup
    cropped/chairs.raw      0.285156 832 studio couch
    cropped/trash_bin.raw   0.773438 413 ashcan
    Copy to clipboard

Classification results are identical to the run with CPU
runtime, but there are differences in the probabilities
associated with the output labels due to floating point
precision differences.

Last Published: Jun 04, 2026

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