1 files changed, 135 insertions, 0 deletions
diff --git a/samples/ImageClassification/README.md b/samples/ImageClassification/README.md
new file mode 100644
index 0000000000..068d0c916a
--- /dev/null
+++ b/samples/ImageClassification/README.md
@@ -0,0 +1,135 @@
+# Image Classification with the Arm NN Tensorflow Lite Delegate
+
+This application demonstrates the use of the Arm NN Tensorflow Lite Delegate.
+In this application we integrate the Arm NN Tensorflow Lite Delegate into the
+TensorFlow Lite Python package.
+
+## Before You Begin
+
+This repository assumes you have built, or have downloaded the
+`libarmnnDelegate.so` and `libarmnn.so` from the GitHub releases page. You will
+also need to have built the TensorFlow Lite library from source.
+
+If you have not already installed these, please follow our guides in the ArmNN
+repository. The guide to build the delegate can be found
+[here](../../delegate/BuildGuideNative.md) and the guide to integrate the
+delegate into Python can be found
+[here](../../delegate/IntegrateDelegateIntoPython.md).
+
+
+## Getting Started
+
+Before running the application, we will first need to:
+
+- Install the required Python packages
+- Download this example
+- Download a model and corresponding label mapping
+- Download an example image
+
+1. Install required packages and Git Large File Storage (to download models
+from the Arm ML-Zoo).
+
+  ```bash
+  sudo apt-get install -y python3 python3-pip wget git git-lfs unzip
+  git lfs install
+  ```
+
+2. Clone the Arm NN repository and change directory to this example.
+
+  ```bash
+  git clone https://github.com/arm-software/armnn.git
+  cd armnn/samples/ImageClassification
+  ```
+
+3. Download your model and label mappings.
+
+  For this example we use the `MobileNetV2` model. This model can be found in
+  the Arm ML-Zoo as well as scripts to download the labels for the model.
+
+  ```bash
+  export BASEDIR=$(pwd)
+  #clone the model zoo
+  git clone https://github.com/arm-software/ml-zoo.git
+  #go to the mobilenetv2 uint8 folder
+  cd ml-zoo/models/image_classification/mobilenet_v2_1.0_224/tflite_uint8
+  #generate the labelmapping
+  ./get_class_labels.sh
+  #cd back to this project folder
+  cd BASEDIR
+  #copy your model and label mapping
+  cp ml-zoo/models/image_classification/mobilenet_v2_1.0_224/tflite_uint8/mobilenet_v2_1.0_224_quantized_1_default_1.tflite .
+  cp ml-zoo/models/image_classification/mobilenet_v2_1.0_224/tflite_uint8 labelmappings.txt .
+  ```
+
+4. Download a test image.
+
+  ```bash
+  wget -O cat.png "https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true"
+  ```
+
+5. Download the required Python packages.
+
+  ```bash
+  pip3 install -r requirements.txt
+  ```
+
+6. Copy over your `libtensorflow_lite_all.so` and `libarmnn.so` library files
+you built/downloaded before trying this application to the application
+folder. For example:
+
+  ```bash
+  cp path/to/tensorflow/directory/tensorflow/bazel-bin/libtensorflow_lite_all.so .
+  cp /path/to/armnn/binaries/libarmnn.so .
+  ```
+
+## Folder Structure
+
+You should now have the following folder structure:
+
+```
+.
+├── README.md
+├── run_classifier.py          # script for the demo
+├── libtensorflow_lite_all.so  # tflite library built from tensorflow
+├── libarmnn.so
+├── cat.png                    # downloaded example image
+├── mobilenet_v2_1.0_224_quantized_1_default_1.tflite #tflite model from ml-zoo
+└── labelmappings.txt          # model labelmappings for output processing
+```
+
+## Run the model
+
+```bash
+python3 run_classifier.py \
+--input_image cat.png \
+--model_file mobilenet_v2_1.0_224_quantized_1_default_1.tflite \
+--label_file labelmappings.txt \
+--delegate_path /path/to/delegate/libarmnnDelegate.so.24 \
+--preferred_backends GpuAcc CpuAcc CpuRef
+```
+
+The output prediction will be printed. In this example we get:
+
+```bash
+'tabby, tabby cat'
+```
+
+## Running an inference with the Arm NN TensorFlow Lite Delegate
+
+Compared to your usual TensorFlow Lite projects, using the Arm NN TensorFlow
+Lite Delegate requires one extra step when loading in your model:
+
+```python
+import tflite_runtime.interpreter as tflite
+
+armnn_delegate = tflite.load_delegate("/path/to/delegate/libarmnnDelegate.so",
+  options={
+    "backends": "GpuAcc,CpuAcc,CpuRef",
+    "logging-severity": "info"
+  }
+)
+interpreter = tflite.Interpreter(
+  model_path="mobilenet_v2_1.0_224_quantized_1_default_1.tflite",
+  experimental_delegates=[armnn_delegate]
+)
+```