1# PyArmNN Image Classification Sample Application 2 3## Overview 4 5To further explore PyArmNN API, we provide an example for running image classification on an image. 6 7All resources are downloaded during execution, so if you do not have access to the internet, you may need to download these manually. The file `example_utils.py` contains code shared between the examples. 8 9## Prerequisites 10 11##### PyArmNN 12 13Before proceeding to the next steps, make sure that you have successfully installed the newest version of PyArmNN on your system by following the instructions in the README of the PyArmNN root directory. 14 15You can verify that PyArmNN library is installed and check PyArmNN version using: 16```bash 17$ pip show pyarmnn 18``` 19 20You can also verify it by running the following and getting output similar to below: 21```bash 22$ python -c "import pyarmnn as ann;print(ann.GetVersion())" 23'32.0.0' 24``` 25 26##### Dependencies 27 28Install the dependencies: 29 30```bash 31$ pip install -r requirements.txt 32``` 33 34## Perform Image Classification 35 36Perform inference with TFLite model by running the sample script: 37```bash 38$ python tflite_mobilenetv1_quantized.py 39``` 40 41Perform inference with ONNX model by running the sample script: 42```bash 43$ python onnx_mobilenetv2.py 44``` 45 46The output from inference will be printed as <i>Top N</i> results, listing the classes and probabilities associated with the classified image. 47