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authorNarumol Prangnawarat <narumol.prangnawarat@arm.com>2020-05-25 14:44:46 +0100
committerNarumol Prangnawarat <narumol.prangnawarat@arm.com>2020-05-25 14:45:00 +0100
commit92037af8bf93d57bfbb8a893c34784b877faa07a (patch)
tree871c6aee3092c3a9920d949505c80a0f1ecb3371 /src/armnnTfParser/TensorFlowSupport.md
parenteb1f9cfac44cca8e0330b5918fa907ee0b32f9bf (diff)
downloadarmnn-92037af8bf93d57bfbb8a893c34784b877faa07a.tar.gz
IVGCVSW-4792 Update armnn readme and support files
* Add TPIP used by Arm NN * Add SqueezeNet to CaffeSupport.md * Add ResNet v2 50 to TensorFlowSupport.md * Update tested networks in TensorFlowLiteSupported.md Signed-off-by: Narumol Prangnawarat <narumol.prangnawarat@arm.com> Change-Id: I15927ea600a3dfffee082933d32f235f3940730e
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@@ -189,4 +189,6 @@ Arm tests these operators with the following TensorFlow fp32 neural networks:
* inception_v3. The Arm NN SDK only supports the official inception_v3 transformed model. See the TensorFlow documentation on [preparing models for mobile deployment](https://www.tensorflow.org/mobile/prepare_models) for more information on how to transform the inception_v3 network.
+* ResNet v2 50 implementation from the [TF Slim model zoo](https://github.com/tensorflow/models/tree/master/research/slim)
+
More machine learning operators will be supported in future releases.