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authorGeorgios Pinitas <georgios.pinitas@arm.com>2020-09-07 13:51:53 +0100
committerGeorgios Pinitas <georgios.pinitas@arm.com>2020-09-08 14:12:11 +0000
commitbe33acd75f8b2110914a583da01c65bbc77eae09 (patch)
tree211716d14a796d7f7f62e2b3abb145f3f5468ab7 /docs
parent7d0adc602b3a3ff66184632fd388b25384a9bc99 (diff)
downloadComputeLibrary-be33acd75f8b2110914a583da01c65bbc77eae09.tar.gz
COMPMID-3645: Clarify graph examples' immutable governance
Document that no structural or data alterations have been applied to any of the provided models. Signed-off-by: Georgios Pinitas <georgios.pinitas@arm.com> Change-Id: I74e6baaec1fbb08f26b16b4d5dce4406bcf5aca8 Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/3938 Tested-by: Arm Jenkins <bsgcomp@arm.com> Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
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@@ -143,6 +143,11 @@ The arm_compute::utils::load_trained_data shows how one could load
the weights and biases into tensor from the .npy file by the help of Accessor.
@section validate_examples Validating examples
+
+Compute Library provides a list of graph examples that are used in the context of integration and performance testing.
+The provenance of each model is part of its documentation and no structural or data alterations have been applied to any
+of them unless explicitly specified otherwise in the documentation.
+
Using one of the provided scripts will generate files containing the trainable parameters.
You can validate a given graph example on a list of inputs by running: