Merge pull request #3126 from nignatovsky/publish-models

Published 1st set of candidate models for 2022.1 Release

Author: Wovchena

demos/background_subtraction_demo/cpp_gapi/README.md

@@ -48,6 +48,10 @@ An example of using the Model Converter:
 omz_converter --list models.lst
 ```
 
+### Supported Models
+
+* instance-segmentation-person-????
+
 > **NOTE**: Refer to the tables [Intel's Pre-Trained Models Device Support](../../../models/intel/device_support.md) and [Public Pre-Trained Models Device Support](../../../models/public/device_support.md) for the details on models inference support at different devices.
 
 ## Running

demos/background_subtraction_demo/cpp_gapi/models.lst

@@ -1,3 +1,3 @@
 # This file can be used with the --list option of the model downloader.
-# TODO: instance-segmentation-person-????
+instance-segmentation-person-????
 instance-segmentation-security-????

demos/background_subtraction_demo/python/README.md

@@ -58,7 +58,7 @@ omz_converter --list models.lst
 
 ### Supported Models
 
-* instance-segmentation-person-0007
+* instance-segmentation-person-????
 * yolact-resnet50-fpn-pytorch
 
 > **NOTE**: Refer to the tables [Intel's Pre-Trained Models Device Support](../../../models/intel/device_support.md) and [Public Pre-Trained Models Device Support](../../../models/public/device_support.md) for the details on models inference support at different devices.

demos/background_subtraction_demo/python/models.lst

@@ -1,3 +1,3 @@
 # This file can be used with the --list option of the model downloader.
-# TODO: instance-segmentation-person-????
+instance-segmentation-person-????
 yolact-resnet50-fpn-pytorch

demos/bert_question_answering_demo/python/README.md

@@ -113,7 +113,7 @@ Options:
 
 ```sh
     python3 bert_question_answering_demo.py
-            --vocab=<omz_dir>/models/intel/bert-small-uncased-whole-word-masking-squad-0001/vocab.txt
+            --vocab=<models_dir>/models/intel/bert-small-uncased-whole-word-masking-squad-0001/vocab.txt
             --model=<path_to_model>/bert-small-uncased-whole-word-masking-squad-0001.xml
             --input_names="input_ids,attention_mask,token_type_ids"
             --output_names="output_s,output_e"
@@ -131,7 +131,7 @@ Exemplary command:
 
 ```sh
     python3 bert_question_answering_demo.py
-            --vocab=<omz_dir>/models/intel/bert-small-uncased-whole-word-masking-squad-0001/vocab.txt
+            --vocab=<models_dir>/models/intel/bert-small-uncased-whole-word-masking-squad-0001/vocab.txt
             --model=localhost:9000/models/bert
             --input_names="input_ids,attention_mask,token_type_ids"
             --output_names="output_s,output_e"

demos/bert_question_answering_embedding_demo/python/README.md

@@ -117,7 +117,7 @@ You can use the following command to try the demo:
 
 ```sh
     python3 bert_question_answering_embedding_demo.py
-            --vocab=<omz_dir>/models/intel/bert-small-uncased-whole-word-masking-squad-0002/vocab.txt
+            --vocab=<models_dir>/models/intel/bert-small-uncased-whole-word-masking-squad-0002/vocab.txt
             --model_emb=<path_to_model>/bert-large-uncased-whole-word-masking-squad-emb-0001.xml
             --input_names_emb="input_ids,attention_mask,token_type_ids,position_ids"
             --model_qa=<path_to_model>/bert-small-uncased-whole-word-masking-squad-0002.xml

demos/formula_recognition_demo/python/README.md

@@ -246,7 +246,7 @@ python formula_recognition_demo.py \
     -i sample.png \
     -m_encoder <path_to_models>/formula-recognition-medium-scan-0001-im2latex-encoder.xml \
     -m_decoder <path_to_models>/formula-recognition-medium-scan-0001-im2latex-decoder.xml \
-    --vocab_path <omz_dir>/models/intel/formula-recognition-medium-scan-0001/vocab.json \
+    --vocab_path <models_dir>/models/intel/formula-recognition-medium-scan-0001/formula-recognition-medium-scan-0001-im2latex-decoder/vocab.json \
     --preprocessing resize
 ```
 
@@ -256,7 +256,7 @@ python formula_recognition_demo.py \
     -i sample2.png \
     -m_encoder <path_to_models>/formula-recognition-polynomials-handwritten-0001-encoder.xml \
     -m_decoder <path_to_models>/formula-recognition-polynomials-handwritten-0001-decoder.xml \
-    --vocab_path <omz_dir>/models/intel/formula-recognition-polynomials-handwritten-0001/vocab.json \
+    --vocab_path <models_dir>/models/intel/formula-recognition-polynomials-handwritten-0001/formula-recognition-polynomials-handwritten-0001-decoder/vocab.json \
     --preprocessing crop
 ```
 

demos/handwritten_text_recognition_demo/python/models.lst

@@ -1,5 +1,4 @@
 # This file can be used with the --list option of the model downloader.
+handwritten-english-recognition-????
 handwritten-japanese-recognition-????
 handwritten-simplified-chinese-recognition-????
-# TODO
-# handwritten-english-recognition-????

demos/noise_suppression_demo/cpp/README.md

@@ -27,8 +27,8 @@ omz_converter --list models.lst
 
 ### Supported Models
 
-* noise-suppression-poconetlike-0001
 * noise-suppression-denseunet-ll-0001
+* noise-suppression-poconetlike-0001
 
 > **NOTE**: Refer to the tables [Intel's Pre-Trained Models Device Support](../../../models/intel/device_support.md) and [Public Pre-Trained Models Device Support](../../../models/public/device_support.md) for the details on models inference support at different devices.
 

demos/noise_suppression_demo/cpp/models.lst

@@ -1,4 +1,3 @@
 # This file can be used with the --list option of the model downloader.
+noise-suppression-denseunet-ll-????
 noise-suppression-poconetlike-????
-# TODO
-# noise-suppression-denseunet-ll-????