MXNet 2

docs/MO_DG/prepare_model/Supported_Frameworks_Layers.md

@@ -50,10 +50,10 @@ In this article, you can find lists of supported framework layers, divided by fr
 | Tile |  |
 
 
-## MXNet Supported Symbols
+## Apache MXNet Supported Symbols
 
 
-| Symbol Name in MXNet| Limitations|
+| Symbol Name in Apache MXNet| Limitations|
 | :----------| :----------|
 | _Plus |  |
 | _contrib_arange_like |  |

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_MxNet.md

@@ -22,17 +22,17 @@ MXNet-specific parameters:
   --save_params_from_nd
             Enable saving built parameters file from .nd files
   --legacy_mxnet_model
-            Enable MXNet loader to make a model compatible with the latest MXNet version.
-            Use only if your model was trained with MXNet version lower than 1.0.0
+            Enable Apache MXNet loader to make a model compatible with the latest Apache MXNet version.
+            Use only if your model was trained with Apache MXNet version lower than 1.0.0
   --enable_ssd_gluoncv
             Enable transformation for converting the gluoncv ssd topologies.
             Use only if your topology is one of ssd gluoncv topologies
 ```
 
-> **NOTE**: By default, Model Optimizer does not use the MXNet loader. It transforms the topology to another format which is compatible with the latest
-> version of MXNet. However, the MXNet loader is required for models trained with lower version of MXNet. If your model was trained with an MXNet version lower than 1.0.0, specify the
-> `--legacy_mxnet_model` key to enable the MXNet loader. Note that the loader does not support models with custom layers. In this case, you must manually
-> recompile MXNet with custom layers and install it in your environment.
+> **NOTE**: By default, Model Optimizer does not use the Apache MXNet loader. It transforms the topology to another format which is compatible with the latest
+> version of Apache MXNet. However, the Apache MXNet loader is required for models trained with lower version of Apache MXNet. If your model was trained with an Apache MXNet version lower than 1.0.0, specify the
+> `--legacy_mxnet_model` key to enable the Apache MXNet loader. Note that the loader does not support models with custom layers. In this case, you must manually
+> recompile Apache MXNet with custom layers and install it in your environment.
 
 ## Custom Layer Definition
 

docs/MO_DG/prepare_model/convert_model/mxnet_specific/Convert_Style_Transfer_From_MXNet.md

@@ -110,7 +110,7 @@ cp models/13_decoder_auxs.nd nst_model
 ```
 > **NOTE**: Make sure that all the `.params` and `.json` files are in the same directory as the `.nd` files. Otherwise, the conversion process fails.
 
-3. Run the Model Optimizer for MXNet. Use the `--nd_prefix_name` option to specify the decoder prefix and `--input_shape` to specify input shapes in [N,C,W,H] order. For example:<br>
+3. Run the Model Optimizer for Apache MXNet. Use the `--nd_prefix_name` option to specify the decoder prefix and `--input_shape` to specify input shapes in [N,C,W,H] order. For example:<br>
 ```sh
 mo --input_symbol <path/to/nst_model>/nst_vgg19-symbol.json --framework mxnet --output_dir <path/to/output_dir> --input_shape [1,3,224,224] --nd_prefix_name 13_decoder --pretrained_model <path/to/nst_model>/vgg19-0000.params
 ```

docs/MO_DG/prepare_model/customize_model_optimizer/Customize_Model_Optimizer.md

@@ -736,7 +736,7 @@ sub-graph of the original graph isomorphic to the specified pattern.
 2. [Specific Operation Front Phase Transformations](#specific-operation-front-phase-transformations) triggered for the
 node with a specific `op` attribute value.
 3. [Generic Front Phase Transformations](#generic-front-phase-transformations).
-4. Manually enabled transformation, defined with a JSON configuration file (for TensorFlow, ONNX, MXNet, and PaddlePaddle models), specified using the `--transformations_config` command-line parameter:
+4. Manually enabled transformation, defined with a JSON configuration file (for TensorFlow, ONNX, Apache MXNet, and PaddlePaddle models), specified using the `--transformations_config` command-line parameter:
     1. [Node Name Pattern Front Phase Transformations](#node-name-pattern-front-phase-transformation).
     2. [Front Phase Transformations Using Start and End Points](#start-end-points-front-phase-transformations).
     3. [Generic Front Phase Transformations Enabled with Transformations Configuration File](#generic-transformations-config-front-phase-transformations).

docs/OV_Runtime_UG/network_state_intro.md

@@ -216,7 +216,7 @@ If the original framework does not have a special API for working with states, a
 **ONNX and frameworks supported via ONNX format:** *LSTM, RNN, GRU* original layers are converted to the TensorIterator operation. TensorIterator body contains LSTM/RNN/GRU Cell. Peepholes, InputForget modifications are not supported, sequence_lengths optional input is supported.
 *ONNX Loop* layer is converted to the OpenVINO Loop operation.
 
-**MXNet:** *LSTM, RNN, GRU* original layers are converted to TensorIterator operation, TensorIterator body contains LSTM/RNN/GRU Cell operations.
+**Apache MXNet:** *LSTM, RNN, GRU* original layers are converted to TensorIterator operation, TensorIterator body contains LSTM/RNN/GRU Cell operations.
 
 **TensorFlow:** *BlockLSTM* is converted to TensorIterator operation, TensorIterator body contains LSTM Cell operation, Peepholes, InputForget modifications are not supported.
 *While* layer is converted to TensorIterator, TensorIterator body can contain any supported operations, but dynamic cases, when count of iterations cannot be calculated in shape inference (ModelOptimizer conversion) time, are not supported.

docs/OV_Runtime_UG/supported_plugins/VPU.md

@@ -20,7 +20,7 @@ This chapter provides information on the OpenVINO Runtime plugins that enable in
 
 ## Supported Networks
 
-**Caffe\***:
+**Caffe**:
 * AlexNet
 * CaffeNet
 * GoogleNet (Inception) v1, v2, v4
@@ -32,7 +32,7 @@ This chapter provides information on the OpenVINO Runtime plugins that enable in
 * DenseNet family (121,161,169,201)
 * SSD-300, SSD-512, SSD-MobileNet, SSD-GoogleNet, SSD-SqueezeNet
 
-**TensorFlow\***:
+**TensorFlow**:
 * AlexNet
 * Inception v1, v2, v3, v4
 * Inception ResNet v2
@@ -46,7 +46,7 @@ This chapter provides information on the OpenVINO Runtime plugins that enable in
 * ssd_mobilenet_v1
 * DeepLab-v3+
 
-**MXNet\***:
+**Apache MXNet**:
 * AlexNet and CaffeNet
 * DenseNet family (121,161,169,201)
 * SqueezeNet v1.1

docs/ops/detection/DeformablePSROIPooling_1.md

@@ -13,7 +13,7 @@ If only two inputs are provided, position sensitive pooling with regular ROI bin
 If third input is provided, each bin position is transformed by adding corresponding offset to the bin left top corner coordinates. Third input values are usually calculated by regular position sensitive pooling layer, so non-deformable mode (DeformablePSROIPooling with two inputs).
 The ROI coordinates are specified as five element tuples: `[batch_id, x_1, y_1, x_2, y_2]` in absolute values.
 
-This operation is compatible with [MXNet DeformablePSROIPooling](https://mxnet.apache.org/versions/1.7.0/api/python/docs/api/contrib/symbol/index.html#mxnet.contrib.symbol.DeformablePSROIPooling) cases where `group_size` is equal to `pooled_size`.
+This operation is compatible with [Apache MXNet DeformablePSROIPooling](https://mxnet.apache.org/versions/1.7.0/api/python/docs/api/contrib/symbol/index.html#mxnet.contrib.symbol.DeformablePSROIPooling) cases where `group_size` is equal to `pooled_size`.
 
 **Attributes**
 

docs/ops/detection/DetectionOutput_1.md

@@ -101,8 +101,8 @@ At each feature map cell, *DetectionOutput* predicts the offsets relative to the
 
   * **Description**: *decrease_label_id* flag that denotes how to perform NMS.
   * **Range of values**:
-    * false - perform NMS like in Caffe\*.
-    * true - perform NMS like in MxNet\*.
+    * false - perform NMS like in Caffe.
+    * true - perform NMS like in Apache MxNet.
   * **Type**: boolean
   * **Default value**: false
   * **Required**: *no*

docs/ops/detection/DetectionOutput_8.md

@@ -108,8 +108,8 @@ it is necessary to adjust the predicted offset accordingly.
 
   * **Description**: *decrease_label_id* flag that denotes how to perform NMS.
   * **Range of values**:
-    * false - perform NMS like in Caffe\*.
-    * true - perform NMS like in MxNet\*.
+    * false - perform NMS like in Caffe.
+    * true - perform NMS like in Apache MxNet.
   * **Type**: boolean
   * **Default value**: false
   * **Required**: *no*