ArmNN
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The ModelAccuracyTool-Armnn
is a program for measuring the Top 5 accuracy results of a model against an image dataset.
Prerequisites:
ArmnnConverter
can be used to convert a model to this format.Build option: To build ModelAccuracyTool, pass the following options to Cmake:
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