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<a href="tests_2_model_accuracy_tool-_armnn_2_r_e_a_d_m_e_8md.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> # The ModelAccuracyTool-Armnn</div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> </div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> The `ModelAccuracyTool-Armnn` is a program for measuring the Top 5 accuracy results of a model against an image dataset.</div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> </div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> Prerequisites:</div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> 1. The model is in .armnn format model file. The `ArmnnConverter` can be used to convert a model to this format.</div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> </div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> Build option:</div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> To build ModelAccuracyTool, pass the following options to Cmake:</div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> * -DFLATC_DIR=/path/to/flatbuffers/x86build/</div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> * -DBUILD_ACCURACY_TOOL=1</div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> * -DBUILD_ARMNN_SERIALIZER=1</div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> </div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> |Cmd:|||</div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> | ---|---|---|</div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> | -h | --help | Display help messages |</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> | -m | --model-path | Path to armnn format model file |</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> | -f | --model-format | The model format. Supported values: caffe, tensorflow, tflite |</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> | -i | --input-name | Identifier of the input tensors in the network separated by comma |</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> | -o | --output-name | Identifier of the output tensors in the network separated by comma |</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> | -d | --data-dir | Path to directory containing the ImageNet test data |</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> | -p | --model-output-labels | Path to model output labels file.</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> | -v | --validation-labels-path | Path to ImageNet Validation Label file</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> | -l | --data-layout ] | Data layout. Supported value: NHWC, NCHW. Default: NHWC</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> | -c | --compute | Which device to run layers on by default. Possible choices: CpuRef, CpuAcc, GpuAcc. Default: CpuAcc, CpuRef |</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> | -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. |</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> | -b | --blacklist-path | Path to a blacklist file where each line denotes the index of an image to be excluded from evaluation. |</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> </div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> Example usage: <br></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <code>./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</code></div></div><!-- fragment --></div><!-- contents -->
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