The ModelAccuracyTool-Armnn is a program for measuring the Top 5 accuracy results of a model against an image dataset.

Prerequisites:

The model is in .armnn format model file. The ArmnnConverter can be used to convert a model to this format.

Build option: To build ModelAccuracyTool, pass the following options to Cmake:

-DFLATC_DIR=/path/to/flatbuffers/x86build/
-DBUILD_ACCURACY_TOOL=1
-DBUILD_ARMNN_SERIALIZER=1

Cmd:
-h	–help	Display help messages
-m	–model-path	Path to armnn format model file
-f	–model-format	The model format. Supported values: caffe, tensorflow, tflite
-i	–input-name	Identifier of the input tensors in the network separated by comma
-o	–output-name	Identifier of the output tensors in the network separated by comma
-d	–data-dir	Path to directory containing the ImageNet test data
-p	–model-output-labels	Path to model output labels file.
-v	–validation-labels-path	Path to ImageNet Validation Label file
-l	–data-layout ]	Data layout. Supported value: NHWC, NCHW. Default: NHWC
-c	–compute	Which device to run layers on by default. Possible choices: CpuRef, CpuAcc, GpuAcc. Default: CpuAcc, CpuRef
-r	–validation-range	The range of the images to be evaluated. Specified in the form <begin index>="">:<end index>="">. The index starts at 1 and the range is inclusive. By default the evaluation will be performed on all images.
-b	–blacklist-path	Path to a blacklist file where each line denotes the index of an image to be excluded from evaluation.

Example usage:
./ModelAccuracyTool -m /path/to/model/model.armnn -f tflite -i input -o output -d /path/to/test/directory/ -p /path/to/model-output-labels -v /path/to/file/val.txt -c CpuRef -r 1:100