Merge branch 'openvinotoolkit:master' into master

Author: ieliz

CONTRIBUTING.md

@@ -4,7 +4,6 @@ We appreciate your intention to contribute model to the OpenVINO™ Open Mod
 
 Frameworks supported by the Open Model Zoo:
 * Caffe\*
-* Caffe2\* (via conversion to ONNX\*)
 * TensorFlow\*
 * PyTorch\* (via conversion to ONNX\*)
 * MXNet\*
@@ -113,7 +112,7 @@ For replacement operation:
 - `replacement` — Replacement string
 - `count` (*optional*)  — Exact number of replacements (if number of `pattern` occurrences less then this number, downloading will be aborted)
 
-**`conversion_to_onnx_args`** (*only for Caffe2\*, PyTorch\* models*)
+**`conversion_to_onnx_args`** (*only for PyTorch\* models*)
 
 List of ONNX\* conversion parameters, see `model_optimizer_args` for details.
 
@@ -177,15 +176,15 @@ license: https://raw.githubusercontent.com/pudae/tensorflow-densenet/master/LICE
 
 ## Model Conversion
 
-Deep Learning Inference Engine (IE) supports models in the Intermediate Representation (IR) format. A model from any supported framework can be converted to IR using the Model Optimizer tool included in the OpenVINO™ toolkit. Find more information about conversion in the [Model Optimizer Developer Guide](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html). After a successful conversion you get a model in the IR format, with the `*.xml` file representing the net graph and the `*.bin` file containing the net parameters.
+Deep Learning Inference Engine (IE) supports models in the Intermediate Representation (IR) format. A model from any supported framework can be converted to IR using the Model Optimizer tool included in the OpenVINO™ toolkit. Find more information about conversion in the [Model Optimizer Developer Guide](https://docs.openvino.ai/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html). After a successful conversion you get a model in the IR format, with the `*.xml` file representing the net graph and the `*.bin` file containing the net parameters.
 
 > **NOTE 1**: Image preprocessing parameters (mean and scale) must be built into a converted model to simplify model usage.
 
 > **NOTE 2**: If a model input is a color image, color channel order should be `BGR`.
 
 ## Demo
 
-A demo shows the main idea of how to infer a model using IE. If your model solves one of the tasks supported by the Open Model Zoo, try to find an appropriate option from [demos](demos/README.md) or [samples](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Samples_Overview.html). Otherwise, you must provide your own demo (C++ or Python).
+A demo shows the main idea of how to infer a model using IE. If your model solves one of the tasks supported by the Open Model Zoo, try to find an appropriate option from [demos](demos/README.md) or [samples](https://docs.openvino.ai/latest/_docs_IE_DG_Samples_Overview.html). Otherwise, you must provide your own demo (C++ or Python).
 
 The demo's name should end with `_demo` suffix to follow the convention of the project.
 

README.md

@@ -1,4 +1,4 @@
-# [OpenVINO™ Toolkit](https://01.org/openvinotoolkit) - Open Model Zoo repository
+# [OpenVINO™ Toolkit](https://docs.openvino.ai/latest/index.html) - Open Model Zoo repository
 [![Stable release](https://img.shields.io/badge/version-2021.4-green.svg)](https://github.com/openvinotoolkit/open_model_zoo/releases/tag/2022.1)
 [![Gitter chat](https://badges.gitter.im/gitterHQ/gitter.png)](https://gitter.im/open_model_zoo/community)
 [![Apache License Version 2.0](https://img.shields.io/badge/license-Apache_2.0-green.svg)](LICENSE)
@@ -19,12 +19,12 @@ Open Model Zoo is licensed under [Apache License Version 2.0](LICENSE).
 
 ## Online Documentation
 * [OpenVINO™ Release Notes](https://software.intel.com/en-us/articles/OpenVINO-RelNotes)
-* [Pre-Trained Models](https://docs.openvinotoolkit.org/latest/trained_models.html)
-* [Demos and Samples](https://docs.openvinotoolkit.org/latest/omz_demos.html)
+* [Pre-Trained Models](https://docs.openvino.ai/latest/model_zoo.html)
+* [Demos and Samples](https://docs.openvino.ai/latest/omz_demos.html)
 
 ## Other Usage Examples
-* [Open Visual Cloud](https://01.org/openvisualcloud)
-  * [Tutorial: Build and Run the AD Insertion Sample on public cloud or local machine](https://01.org/openvisualcloud/documents/tutorial-build-and-run-ad-insertion-sample-public-cloud-or-local-machine)
+* [Open Visual Cloud](https://www.intel.com/content/www/us/en/developer/articles/technical/open-visual-cloud.html)
+  * [Tutorial: Running AD Insertion on Public Cloud](https://github.com/OpenVisualCloud/Ad-Insertion-Sample/wiki/Tutorial:-Running-AD-Insertion-on-Public-Cloud)
   * [GitHub Repo for Ad Insertion Sample](https://github.com/OpenVisualCloud/Ad-Insertion-Sample)
 * [OpenVINO for Smart City](https://github.com/incluit/OpenVino-For-SmartCity)
 * [OpenVINO Driver Behavior](https://github.com/incluit/OpenVino-Driver-Behaviour)

ci/requirements-ac-test.txt

@@ -20,8 +20,6 @@ decorator==4.4.2
     # via networkx
 defusedxml==0.7.1
     # via -r tools/accuracy_checker/requirements-core.in
-editdistance==0.5.3
-    # via -r tools/accuracy_checker/requirements.in
 fast-ctc-decode==0.3.0
     # via -r tools/accuracy_checker/requirements.in
 filelock==3.0.12
@@ -78,6 +76,8 @@ numpy==1.19.5
     #   scipy
     #   tifffile
     #   transformers
+openvino-telemetry==2022.1.0
+    # via -r tools/accuracy_checker/requirements-core.in
 packaging==21.0
     # via
     #   huggingface-hub
@@ -136,6 +136,7 @@ regex==2021.8.28
 requests==2.26.0
     # via
     #   huggingface-hub
+    #   openvino-telemetry
     #   transformers
 sacremoses==0.0.45
     # via transformers

ci/requirements-ac.txt

@@ -16,8 +16,6 @@ decorator==4.4.2
     # via networkx
 defusedxml==0.7.1
     # via -r tools/accuracy_checker/requirements-core.in
-editdistance==0.5.3
-    # via -r tools/accuracy_checker/requirements.in
 fast-ctc-decode==0.3.0
     # via -r tools/accuracy_checker/requirements.in
 filelock==3.0.12
@@ -70,6 +68,8 @@ numpy==1.19.5
     #   scipy
     #   tifffile
     #   transformers
+openvino-telemetry==2022.1.0
+    # via -r tools/accuracy_checker/requirements-core.in
 packaging==21.0
     # via
     #   huggingface-hub
@@ -117,6 +117,7 @@ regex==2021.8.28
 requests==2.26.0
     # via
     #   huggingface-hub
+    #   openvino-telemetry
     #   transformers
 sacremoses==0.0.45
     # via transformers

ci/requirements-conversion.txt

@@ -26,8 +26,6 @@ defusedxml==0.7.1
     #   -r ${INTEL_OPENVINO_DIR}/deployment_tools/model_optimizer/requirements_tf2.txt
 flatbuffers==1.12
     # via tensorflow
-future==0.18.2
-    # via -r tools/model_tools/requirements-caffe2.in
 gast==0.3.3
     # via tensorflow
 google-auth==1.35.0
@@ -84,7 +82,6 @@ oauthlib==3.1.1
 onnx==1.10.1
     # via
     #   -r ${INTEL_OPENVINO_DIR}/deployment_tools/model_optimizer/requirements_onnx.txt
-    #   -r tools/model_tools/requirements-caffe2.in
     #   -r tools/model_tools/requirements-pytorch.in
 opt-einsum==3.3.0
     # via tensorflow
@@ -126,7 +123,6 @@ six==1.15.0
     #   google-auth
     #   google-pasta
     #   grpcio
-    #   h5py
     #   keras-preprocessing
     #   onnx
     #   protobuf
@@ -147,7 +143,6 @@ termcolor==1.1.0
     # via tensorflow
 torch==1.8.1
     # via
-    #   -r tools/model_tools/requirements-caffe2.in
     #   -r tools/model_tools/requirements-pytorch.in
     #   torchvision
 torchvision==0.9.1

ci/requirements-downloader.txt

@@ -6,11 +6,15 @@ charset-normalizer==2.0.4
     # via requests
 idna==3.2
     # via requests
+openvino-telemetry==2022.1.0
+    # via -r tools/model_tools/requirements.in
 pyrx==0.3.0
     # via -r tools/model_tools/requirements.in
 pyyaml==5.4.1
     # via -r tools/model_tools/requirements.in
 requests==2.26.0
-    # via -r tools/model_tools/requirements.in
+    # via
+    #   -r tools/model_tools/requirements.in
+    #   openvino-telemetry
 urllib3==1.26.6
     # via requests

ci/requirements-quantization.txt

@@ -20,8 +20,6 @@ defusedxml==0.7.1
     # via
     #   -r ${INTEL_OPENVINO_DIR}/deployment_tools/model_optimizer/requirements_kaldi.txt
     #   -r tools/accuracy_checker/requirements-core.in
-editdistance==0.5.3
-    # via -r tools/accuracy_checker/requirements.in
 fast-ctc-decode==0.3.0
     # via -r tools/accuracy_checker/requirements.in
 filelock==3.0.12
@@ -81,6 +79,8 @@ numpy==1.19.5
     #   scipy
     #   tifffile
     #   transformers
+openvino-telemetry==2022.1.0
+    # via -r tools/accuracy_checker/requirements-core.in
 packaging==21.0
     # via
     #   huggingface-hub
@@ -131,6 +131,7 @@ requests==2.26.0
     # via
     #   -r ${INTEL_OPENVINO_DIR}/deployment_tools/model_optimizer/requirements_kaldi.txt
     #   huggingface-hub
+    #   openvino-telemetry
     #   transformers
 sacremoses==0.0.45
     # via transformers

ci/update-requirements.py

@@ -87,7 +87,7 @@ def pc(target, *sources):
     pc('ci/requirements-check-basics.txt',
        'ci/requirements-check-basics.in', 'ci/requirements-documentation.in')
     pc('ci/requirements-conversion.txt',
-        *(f'tools/model_tools/requirements-{suffix}.in' for suffix in ['caffe2', 'pytorch', 'tensorflow']),
+        *(f'tools/model_tools/requirements-{suffix}.in' for suffix in ['pytorch', 'tensorflow']),
         *(openvino_dir / f'deployment_tools/model_optimizer/requirements_{suffix}.txt'
             for suffix in ['caffe', 'mxnet', 'onnx', 'tf2']))
     pc('ci/requirements-demos.txt',

data/datasets.md

@@ -40,7 +40,7 @@ To use this dataset with OMZ tools, make sure `<DATASET_DIR>` contains the follo
 
 ### Datasets in dataset_definitions.yml
 * `imagenet_1000_classes` used for evaluation models trained on ILSVRC 2012 dataset with 1000 classes. (model examples: [`alexnet`](../models/public/alexnet/README.md), [`vgg16`](../models/public/vgg16/README.md))
-* `imagenet_1000_classes_2015` used for evaluation models trained on ILSVRC 2015 dataset with 1000 classes. (model examples: [`se-resnet-152`](../models/public/se-resnet-152/README.md), [`se-resnext-50`](../models/public/se-resnext-50/README.md))
+* `imagenet_1000_classes_2015` used for evaluation models trained on ILSVRC 2015 dataset with 1000 classes. (model examples: [`se-resnet-50`](../models/public/se-resnet-50/README.md), [`se-resnext-50`](../models/public/se-resnext-50/README.md))
 * `imagenet_1001_classes` used for evaluation models trained on ILSVRC 2012 dataset with 1001 classes (background label + original labels). (model examples: [`googlenet-v2-tf`](../models/public/googlenet-v2-tf/README.md), [`resnet-50-tf`](../models/public/resnet-50-tf/README.md))
 
 ## [Common Objects in Context (COCO)](https://cocodataset.org/#home)
@@ -62,9 +62,9 @@ To use this dataset with OMZ tools, make sure `<DATASET_DIR>` contains the follo
 
 ### Datasets in dataset_definitions.yml
 * `ms_coco_mask_rcnn` used for evaluation models trained on COCO dataset for object detection and instance segmentation tasks. Background label + label map with 80 public available object categories are used. Annotations are saved in order of ascending image ID.
-* `ms_coco_detection_91_classes` used for evaluation models trained on COCO dataset for object detection tasks. Background label + label map with 80 public available object categories are used (original indexing to 91 categories is preserved. You can find more information about object categories labels [here](https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/)). Annotations are saved in order of ascending image ID. (model examples: [`faster_rcnn_resnet50_coco`](../models/public/faster_rcnn_resnet50_coco/README.md), [`ssd_resnet50_v1_fpn_coco`](../models/public/ssd_resnet50_v1_fpn_coco/README.md))
+* `ms_coco_detection_91_classes` used for evaluation models trained on COCO dataset for object detection tasks. Background label + label map with 80 public available object categories are used (original indexing to 91 categories is preserved. You can find more information about object categories labels [here](https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/)). Annotations are saved in order of ascending image ID. (model examples: [`faster_rcnn_resnet50_coco`](../models/public/faster_rcnn_resnet50_coco/README.md), [`ssd_mobilenet_v1_coco`](../models/public/ssd_mobilenet_v1_coco/README.md))
 * `ms_coco_detection_80_class_with_background` used for evaluation models trained on COCO dataset for object detection tasks. Background label + label map with 80 public available object categories are used. Annotations are saved in order of ascending image ID. (model examples: [`faster-rcnn-resnet101-coco-sparse-60-0001`](../models/intel/faster-rcnn-resnet101-coco-sparse-60-0001/README.md), [`ssd-resnet34-1200-onnx`](../models/public/ssd-resnet34-1200-onnx/README.md))
-* `ms_coco_detection_80_class_without_background` used for evaluation models trained on COCO dataset for object detection tasks. Label map with 80 public available object categories is used. Annotations are saved in order of ascending image ID. (model examples: [`ctdet_coco_dlav0_384`](../models/public/ctdet_coco_dlav0_384/README.md), [`yolo-v3-tf`](../models/public/yolo-v3-tf/README.md))
+* `ms_coco_detection_80_class_without_background` used for evaluation models trained on COCO dataset for object detection tasks. Label map with 80 public available object categories is used. Annotations are saved in order of ascending image ID. (model examples: [`ctdet_coco_dlav0_512`](../models/public/ctdet_coco_dlav0_512/README.md), [`yolo-v3-tf`](../models/public/yolo-v3-tf/README.md))
 * `ms_coco_keypoints` used for evaluation models trained on COCO dataset for human pose estimation tasks. Each annotation stores multiple keypoints for one image. (model examples: [`human-pose-estimation-0001`](../models/intel/human-pose-estimation-0001/README.md))
 * `ms_coco_single_keypoints` used for evaluation models trained on COCO dataset for human pose estimation tasks. Each annotation stores single keypoints for image, so several annotation can be associated to one image. (model examples: [`single-human-pose-estimation-0001`](../models/public/single-human-pose-estimation-0001/README.md))
 

demos/3d_segmentation_demo/python/README.md

@@ -123,5 +123,5 @@ The demo reports
 ## See Also
 
 * [Open Model Zoo Demos](../../README.md)
-* [Model Optimizer](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html)
+* [Model Optimizer](https://docs.openvino.ai/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html)
 * [Model Downloader](../../../tools/model_tools/README.md)