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authorJan Eilers <jan.eilers@arm.com>2021-02-19 09:38:03 +0000
committerJan Eilers <jan.eilers@arm.com>2021-02-22 08:32:32 +0000
commit1121c6bf3fc4ff34b6fce2e17a2cda71be185112 (patch)
tree868a1cc73d7c8ce06da8d97eb1b7260647f7d472 /README.md
parent8548e9ccb0b8a5a8780a7d35e5abf19bb13f9dbc (diff)
downloadarmnn-1121c6bf3fc4ff34b6fce2e17a2cda71be185112.tar.gz
IVGCVSW-5695 Update supported operators
* Update supported operators for the delegate, parsers, serializer and deserializer Signed-off-by: Jan Eilers <jan.eilers@arm.com> Change-Id: I33ac99a29d894eec055cd05411014075d78b3168
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@@ -36,7 +36,7 @@ to build for a wide variety of target platforms, from a wide variety of host env
Depending on what kind of framework (Tensorflow, Caffe, ONNX) you've been using to create your model there are multiple
software tools available within Arm NN that can serve your needs.
-Generally, there is a **parser** available **for each supported framework**. Each parser allows you to run a models from
+Generally, there is a **parser** available **for each supported framework**. Each parser allows you to run models from
one framework e.g. the TfLite-Parser lets you run TfLite models. You can integrate these parsers into your own
application to load, optimize and execute your model. We also provide **python bindings** for our parsers and the Arm NN core.
We call the result **PyArmNN**. Therefore your application can be conveniently written in either C++ using the "original"