Merge pull request #2175 from vladimir-dudnik/vd/describe-public-models-convertion

describe model load and convertion to IR

Author: Roman Donchenko

data/dataset_classes/aclnet.txt renamed to data/dataset_classes/aclnet_53cl.txt

@@ -0,0 +1,20 @@
+aeroplane
+bicycle
+bird
+boat
+bottle
+bus
+car
+cat
+chair
+cow
+diningtable
+dog
+horse
+motorbike
+person
+pottedplant
+sheep
+sofa
+train
+tvmonitor

data/dataset_classes/cityscapes19.txt renamed to data/dataset_classes/cityscapes_19cl.txt

@@ -94,9 +94,17 @@ Options:
 
 Running the application with an empty list of options yields the usage message given above and an error message.
 
-To run the demo, you can use public or pre-trained models. To download the pre-trained models, use the OpenVINO [Model Downloader](../../../tools/downloader/README.md). The list of models supported by the demo is in [models.lst](./models.lst).
+To run the demo, you can use public or Intel pre-trained models. You can download models and, if necessary, convert them into Inference Engine format using the [Model Downloader and other automation tools](../../../tools/downloader/README.md) as shown in the examples below. The models which are supported by the demo are listed in the [models.lst](./models.lst) file.
 
-> **NOTE**: Before running the demo with a trained model, make sure the model is converted to the Inference Engine format (\*.xml + \*.bin) using the [Model Optimizer tool](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html).
+An example of using the Model Downloader:
+```
+python3 <omz_dir>/tools/downloader/downloader.py --list models.lst
+```
+
+An example of using the Model Converter:
+```
+python3 <omz_dir>/tools/downloader/converter.py --list models.lst
+```
 
 For example, to do inference on a GPU with the OpenVINO&trade; toolkit pre-trained models, run the following command:
 

data/dataset_classes/voc_20cl.txt

@@ -67,9 +67,17 @@ Options:
 
 Running the application with an empty list of options yields the usage message given above and an error message.
 
-To run the demo, you can use public or pre-trained models. To download the pre-trained models, use the OpenVINO [Model Downloader](../../../tools/downloader/README.md). The list of models supported by the demo is in [models.lst](models.lst).
+To run the demo, you can use public or Intel pre-trained models. You can download models and, if necessary, convert them into Inference Engine format using the [Model Downloader and other automation tools](../../../tools/downloader/README.md) as shown in the examples below. The models which are supported by the demo are listed in the [models.lst](./models.lst) file.
 
-> **NOTE**: Before running the demo with a trained model, make sure the model is converted to the Inference Engine format (\*.xml + \*.bin) using the [Model Optimizer tool](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html).
+An example of using the Model Downloader:
+```
+python3 <omz_dir>/tools/downloader/downloader.py --list models.lst
+```
+
+An example of using the Model Converter:
+```
+python3 <omz_dir>/tools/downloader/converter.py --list models.lst
+```
 
 For example, to do inference on a GPU with the OpenVINO&trade; toolkit pre-trained models, run the following command:
 

data/dataset_classes/voc.txt renamed to data/dataset_classes/voc_20cl_bkgr.txt

@@ -6,7 +6,19 @@ A trained model of ICNet for fast semantic segmentation, trained on the CamVid\*
 
 The model input is a blob that consists of a single image of `1x3x720x960` in the BGR order. The pixel values are integers in the [0, 255] range.
 
-The model output for `icnet-camvid-ava-0001` is the predicted class index of each input pixel belonging to one of the 12 classes of the CamVid dataset.
+The model output for `icnet-camvid-ava-0001` is the predicted class index of each input pixel belonging to one of the 12 classes of the CamVid dataset:
+- Sky
+- Building
+- Pole
+- Road
+- Pavement
+- Tree
+- SignSymbol
+- Fence
+- Vehicle
+- Pedestrian
+- Bike
+- Unlabeled
 
 ## Specification
 

demos/interactive_face_detection_demo/cpp/README.md

@@ -6,7 +6,19 @@ A trained model of ICNet for fast semantic segmentation, trained on the CamVid\*
 
 The model input is a blob that consists of a single image of `1x3x720x960` in the BGR order. The pixel values are integers in the [0, 255] range.
 
-The model output for `icnet-camvid-ava-sparse-30-0001` is the predicted class index of each input pixel belonging to one of the 12 classes of the CamVid dataset.
+The model output for `icnet-camvid-ava-sparse-30-0001` is the predicted class index of each input pixel belonging to one of the 12 classes of the CamVid dataset:
+- Sky
+- Building
+- Pole
+- Road
+- Pavement
+- Tree
+- SignSymbol
+- Fence
+- Vehicle
+- Pedestrian
+- Bike
+- Unlabeled
 
 ## Specification
 

demos/interactive_face_detection_demo/cpp_gapi/README.md

@@ -6,7 +6,19 @@ A trained model of ICNet for fast semantic segmentation, trained on the CamVid\*
 
 The model input is a blob that consists of a single image of `1x3x720x960` in the BGR order. The pixel values are integers in the [0, 255] range.
 
-The model output for `icnet-camvid-ava-sparse-60-0001` is the predicted class index of each input pixel belonging to one of the 12 classes of the CamVid dataset.
+The model output for `icnet-camvid-ava-sparse-60-0001` is the predicted class index of each input pixel belonging to one of the 12 classes of the CamVid dataset:
+- Sky
+- Building
+- Pole
+- Road
+- Pavement
+- Tree
+- SignSymbol
+- Fence
+- Vehicle
+- Pedestrian
+- Bike
+- Unlabeled
 
 ## Specification
 

models/intel/icnet-camvid-ava-0001/description/icnet-camvid-ava-0001.md

@@ -6,7 +6,19 @@ This is a U-Net model that is designed to perform semantic segmentation. The mod
 
 The model input is a blob that consists of a single image of `1x3x368x480` in the BGR order. The pixel values are integers in the [0, 255] range.
 
-The model output for `unet-camvid-onnx-0001` is the per-pixel probabilities of each input pixel belonging to one of the 12 classes of the CamVid dataset.
+The model output for `unet-camvid-onnx-0001` is the per-pixel probabilities of each input pixel belonging to one of the 12 classes of the CamVid dataset:
+- Sky
+- Building
+- Pole
+- Road
+- Pavement
+- Tree
+- SignSymbol
+- Fence
+- Vehicle
+- Pedestrian
+- Bike
+- Unlabeled
 
 ## Specification