Remove use guide section from doxygen

* This guide has now been moved to the Quick Start section in doxygen

Signed-off-by: Nikhil Raj <nikhil.raj@arm.com>
Change-Id: I758915c43f0e9e116f7308482db34d560d7ba0d9

1 files changed, 0 insertions, 207 deletions

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-/// Copyright (c) 2021 ARM Limited and Contributors. All rights reserved.
-///
-/// SPDX-License-Identifier: MIT
-///
-
-namespace armnn
-{
-/**
-@page parsers Parsers
-
-@tableofcontents
-Execute models from different machine learning platforms efficiently with our parsers. Simply choose a parser according
-to the model you want to run e.g. If you've got a model in onnx format (<model_name>.onnx) use our onnx-parser.
-
-If you would like to run a Tensorflow Lite (TfLite) model you probably also want to take a look at our @ref delegate.
-
-All parsers are written in C++ but it is also possible to use them in python. For more information on our python
-bindings take a look into the @ref md_python_pyarmnn_README section.
-
-<br/><br/>
-
-
-
-
-@section S5_onnx_parser Arm NN Onnx Parser
-
-`armnnOnnxParser` is a library for loading neural networks defined in ONNX protobuf files into the Arm NN runtime.
-
-## ONNX operators that the Arm NN SDK supports
-
-This reference guide provides a list of ONNX operators the Arm NN SDK currently supports.
-
-The Arm NN SDK ONNX parser currently only supports fp32 operators.
-
-### Fully supported
-
-- Add
-  - See the ONNX [Add documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Add) for more information
-
-- AveragePool
-  - See the ONNX [AveragePool documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#AveragePool) for more information.
-
-- Concat
-  - See the ONNX [Concat documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Concat) for more information.
-
-- Constant
-  - See the ONNX [Constant documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Constant) for more information.
-
-- Clip
-  - See the ONNX [Clip documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Clip) for more information.
-
-- Flatten
-  - See the ONNX [Flatten documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Flatten) for more information.
-
-- Gather
-  - See the ONNX [Gather documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Gather) for more information.
-
-- GlobalAveragePool
-  - See the ONNX [GlobalAveragePool documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#GlobalAveragePool) for more information.
-
-- LeakyRelu
-  - See the ONNX [LeakyRelu documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#LeakyRelu) for more information.
-
-- MaxPool
-  - See the ONNX [max_pool documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#MaxPool) for more information.
-
-- Relu
-  - See the ONNX [Relu documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Relu) for more information.
-
-- Reshape
-  - See the ONNX [Reshape documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Reshape) for more information.
-
-- Shape
-  - See the ONNX [Shape documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Shape) for more information.
-
-- Sigmoid
-  - See the ONNX [Sigmoid documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Sigmoid) for more information.
-
-- Tanh
-  - See the ONNX [Tanh documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Tanh) for more information.
-
-- Unsqueeze
-  - See the ONNX [Unsqueeze documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Unsqueeze) for more information.
-
-### Partially supported
-
-- Conv
-  - The parser only supports 2D convolutions with a group = 1 or group = #Nb_of_channel (depthwise convolution)
-- BatchNormalization
-  - The parser does not support training mode. See the ONNX [BatchNormalization documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#BatchNormalization) for more information.
-- Gemm
-  - The parser only supports constant bias or non-constant bias where bias dimension = 1. See the ONNX [Gemm documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Gemm) for more information.
-- MatMul
-  - The parser only supports constant weights in a fully connected layer. See the ONNX [MatMul documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#MatMul) for more information.
-
-## Tested networks
-
-Arm tested these operators with the following ONNX fp32 neural networks:
-- Mobilenet_v2. See the ONNX [MobileNet documentation](https://github.com/onnx/models/tree/master/vision/classification/mobilenet) for more information.
-- Simple MNIST. This is no longer directly documented by ONNX. The model and test data may be downloaded [from the ONNX model zoo](https://onnxzoo.blob.core.windows.net/models/opset_8/mnist/mnist.tar.gz).
-
-More machine learning operators will be supported in future releases.
-<br/><br/><br/><br/>
-
-
-
-
-@section S6_tf_lite_parser Arm NN Tf Lite Parser
-
-`armnnTfLiteParser` is a library for loading neural networks defined by TensorFlow Lite FlatBuffers files
-into the Arm NN runtime.
-
-## TensorFlow Lite operators that the Arm NN SDK supports
-
-This reference guide provides a list of TensorFlow Lite operators the Arm NN SDK currently supports.
-
-### Fully supported
-The Arm NN SDK TensorFlow Lite parser currently supports the following operators:
-
-- ABS
-- ADD
-- ARG_MAX
-- ARG_MIN
-- AVERAGE_POOL_2D, Supported Fused Activation: RELU , RELU6 , TANH, NONE
-- BATCH_TO_SPACE
-- CONCATENATION, Supported Fused Activation: RELU , RELU6 , TANH, NONE
-- CONV_2D, Supported Fused Activation: RELU , RELU6 , TANH, NONE
-- CONV_3D, Supported Fused Activation: RELU , RELU6 , TANH, NONE
-- DEPTH_TO_SPACE
-- DEPTHWISE_CONV_2D, Supported Fused Activation: RELU , RELU6 , TANH, NONE
-- DEQUANTIZE
-- DIV
-- ELU
-- EQUAL
-- EXP
-- EXPAND_DIMS
-- FULLY_CONNECTED, Supported Fused Activation: RELU , RELU6 , TANH, NONE
-- GATHER
-- GREATER
-- GREATER_EQUAL
-- HARD_SWISH
-- LEAKY_RELU
-- LESS
-- LESS_EQUAL
-- LOGICAL_NOT
-- LOGISTIC
-- L2_NORMALIZATION
-- MAX_POOL_2D, Supported Fused Activation: RELU , RELU6 , TANH, NONE
-- MAXIMUM
-- MEAN
-- MINIMUM
-- MIRROR_PAD
-- MUL
-- NEG
-- NOT_EQUAL
-- PACK
-- PAD
-- PRELU
-- QUANTIZE
-- RELU
-- RELU6
-- REDUCE_MAX
-- REDUCE_MIN
-- REDUCE_PROD
-- RESHAPE
-- RESIZE_BILINEAR
-- RESIZE_NEAREST_NEIGHBOR
-- RSQRT
-- SHAPE
-- SLICE
-- SOFTMAX
-- SPACE_TO_BATCH
-- SPLIT
-- SPLIT_V
-- SQUEEZE
-- STRIDED_SLICE
-- SUB
-- SUM
-- TANH
-- TRANSPOSE
-- TRANSPOSE_CONV
-- UNPACK
-
-### Custom Operator
-- TFLite_Detection_PostProcess
-
-## Tested networks
-Arm tested these operators with the following TensorFlow Lite neural network:
-- [Quantized MobileNet](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224_quant.tgz)
-- [Quantized SSD MobileNet](http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_quantized_300x300_coco14_sync_2018_07_18.tar.gz)
-- DeepSpeech v1 converted from [TensorFlow model](https://github.com/mozilla/DeepSpeech/releases/tag/v0.4.1)
-- DeepSpeaker
-- [DeepLab v3+](https://www.tensorflow.org/lite/models/segmentation/overview)
-- FSRCNN
-- EfficientNet-lite
-- RDN converted from [TensorFlow model](https://github.com/hengchuan/RDN-TensorFlow)
-- Quantized RDN (CpuRef)
-- [Quantized Inception v3](http://download.tensorflow.org/models/tflite_11_05_08/inception_v3_quant.tgz)
-- [Quantized Inception v4](http://download.tensorflow.org/models/inception_v4_299_quant_20181026.tgz) (CpuRef)
-- Quantized ResNet v2 50 (CpuRef)
-- Quantized Yolo v3 (CpuRef)
-
-More machine learning operators will be supported in future releases.
-
-**/
-}
-