Move intel_models to models/intel

This creates a single hierarchy of all model documentation. It's also
the first step towards the splitting of Model Downloader config files
into per-model configs.

README.md

@@ -7,7 +7,7 @@
 This repository includes optimized deep learning models and a set of demos to expedite development of high-performance deep learning inference applications. Use these free pre-trained models instead of training your own models to speed-up the development and production deployment process.
 
 ## Repository Components:
-* [Pre-Trained Models](intel_models/index.md)
+* [Pre-Trained Models](models/intel/index.md)
 * [Model Downloader](tools/downloader/README.md) and other automation tools
 * [Demos](demos/README.md) that demonstrate models usage with Deep Learning Deployment Toolkit
 * [Accuracy Checker](tools/accuracy_checker/README.md) tool for models accuracy validation

demos/README.md

@@ -33,7 +33,7 @@ To run the demo applications, you can use images and videos from the media files
 
 > **NOTE:** Inference Engine HDDL and FPGA plugins are available in [proprietary](https://software.intel.com/en-us/openvino-toolkit) distribution only.
 
-You can download the [pre-trained models](../intel_models/index.md) using the OpenVINO [Model Downloader](../tools/downloader/README.md) or from [https://download.01.org/opencv/](https://download.01.org/opencv/).
+You can download the [pre-trained models](../models/intel/index.md) using the OpenVINO [Model Downloader](../tools/downloader/README.md) or from [https://download.01.org/opencv/](https://download.01.org/opencv/).
 The table below shows the correlation between models, demos, and supported plugins. The plugins names are exactly as they are passed to the demos with `-d` option. The correlation between the plugins and supported devices see in the [Supported Devices](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_supported_plugins_Supported_Devices.html) section.
 
 > **NOTE:** **MYRIAD** below stands for Intel® Movidius™ Neural Compute Stick, Intel® Neural Compute Stick 2, and Intel® Vision Accelerator Design with Intel® Movidius™ Vision Processing Units.

demos/crossroad_camera_demo/README.md

@@ -8,7 +8,7 @@ reports person attributes like gender, has hat, has long-sleeved clothes
 * `person-reidentification-retail-0079`, which is executed on top of the results from the first network and prints
 a vector of features for each detected person. This vector is used to conclude if it is already detected person or not.
 
-For more information about the pre-trained models, refer to the [model documentation](../../intel_models/index.md).
+For more information about the pre-trained models, refer to the [model documentation](../../models/intel/index.md).
 
 Other demo objectives are:
 * Images/Video/Camera as inputs, via OpenCV*

demos/gaze_estimation_demo/README.md

@@ -8,7 +8,7 @@ The demo also relies on the following auxiliary networks:
 * `head-pose-estimation-adas-0001`, which estimates head pose in Tait-Bryan angles, serving as an input for gaze estimation model
 * `facial-landmarks-35-adas-0002`, which estimates coordinates of facial landmarks for detected faces. The keypoints at the corners of eyes are used to locate eyes regions required for the gaze estimation model
 
-For more information about the pre-trained models, refer to the [model documentation](../../intel_models/index.md).
+For more information about the pre-trained models, refer to the [model documentation](../../models/intel/index.md).
 
 Other demo objectives are:
 * Video/Camera as inputs, via OpenCV*

demos/human_pose_estimation_demo/README.md

@@ -4,7 +4,7 @@ This demo showcases the work of multi-person 2D pose estimation algorithm. The t
 
 * `human-pose-estimation-0001`, which is a human pose estimation network, that produces two feature vectors. The algorithm uses these feature vectors to predict human poses.
 
-For more information about the pre-trained model, refer to the [model documentation](../../intel_models/index.md).
+For more information about the pre-trained model, refer to the [model documentation](../../models/intel/index.md).
 
 The input frame height is scaled to model height, frame width is scaled to preserve initial aspect ratio and padded to multiple of 8.
 

demos/interactive_face_detection_demo/README.md

@@ -10,7 +10,7 @@ This demo executes four parallel infer requests for the Age/Gender Recognition,
 * `emotions-recognition-retail-0003`, which is executed on top of the results of the first model and reports an emotion for each detected face
 * `facial-landmarks-35-adas-0002`, which is executed on top of the results of the first model and reports normed coordinates of estimated facial landmarks
 
-For more information about the pre-trained models, refer to the [model documentation](../../intel_models/index.md).
+For more information about the pre-trained models, refer to the [model documentation](../../models/intel/index.md).
 
 Other demo objectives are:
 

demos/multichannel_demo/fd/README.md

@@ -3,7 +3,7 @@
 This demo provides an inference pipeline for multi-channel face detection. The demo uses Face Detection network. You can use the following pre-trained model with the demo:
 * `face-detection-retail-0004`, which is a primary detection network for finding faces
 
-For more information about the pre-trained models, refer to the [model documentation](../../../intel_models/index.md).
+For more information about the pre-trained models, refer to the [model documentation](../../../models/intel/index.md).
 
 Other demo objectives are:
 

demos/multichannel_demo/hpe/README.md

@@ -3,7 +3,7 @@
 This demo provides an inference pipeline for Multi-Channel Human Pose Estimation. The demo uses Human Pose Estimation network. You can use the following pre-trained model with the demos:
 * `human-pose-estimation-0001`
 
-For more information about the pre-trained models, refer to the [model documentation](../../../intel_models/index.md).
+For more information about the pre-trained models, refer to the [model documentation](../../../models/intel/index.md).
 
 Other demo objectives are:
 

demos/pedestrian_tracker_demo/README.md

@@ -6,7 +6,7 @@ You can use a set of the following pre-trained models with the demo:
 * _person-detection-retail-0013_, which is the primary detection network for finding pedestrians
 * _person-reidentification-retail-0031_, which is the network that is executed on top of the results from inference of the first network and makes reidentification of the pedestrians
 
-For more information about the pre-trained models, refer to the [model documentation](../../intel_models/index.md).
+For more information about the pre-trained models, refer to the [model documentation](../../models/intel/index.md).
 
 ## How It Works
 

demos/python_demos/action_recognition/README.md

@@ -6,7 +6,7 @@ The following pre-trained models are delivered with the product:
 * `driver-action-recognition-adas-0002-encoder` + `driver-action-recognition-adas-0002-decoder`, which are models for driver monitoring scenario. They recognize actions like safe driving, talking to the phone and others
 * `action-recognition-0001-encoder` + `action-recognition-0001-decoder`, which are general-purpose action recognition (400 actions) models for Kinetics-400 dataset.
 
-For more information about the pre-trained models, refer to the [model documentation](../../../intel_models/index.md).
+For more information about the pre-trained models, refer to the [model documentation](../../../models/intel/index.md).
 
 How It Works
 ------------

demos/security_barrier_camera_demo/README.md

@@ -8,7 +8,7 @@ reports general vehicle attributes, for example, vehicle type (car/van/bus/track
 * `license-plate-recognition-barrier-0001`, which is executed on top of the results from the first network
 and reports a string per recognized license plate
 
-For more information about the pre-trained models, refer to the [model documentation](../../intel_models/index.md).
+For more information about the pre-trained models, refer to the [model documentation](../../models/intel/index.md).
 
 Other demo objectives are:
 * Video/Camera as inputs, via OpenCV\*

demos/smart_classroom_demo/README.md

@@ -12,7 +12,7 @@ a vector of features for each detected face.
 * `person-detection-raisinghand-recognition-0001`, which is a detection network for finding students and simultaneously predicting their current actions (in contrast with the previous model, predicts only if a student raising hand or not).
 * `person-detection-action-recognition-teacher-0002`, which is a detection network for finding persons and simultaneously predicting their current actions.
 
-For more information about the pre-trained models, refer to the [model documentation](../../intel_models/index.md).
+For more information about the pre-trained models, refer to the [model documentation](../../models/intel/index.md).
 
 ## How It Works
 

demos/super_resolution_demo/README.md

@@ -7,7 +7,7 @@ You can use the following pre-trained model with the demo:
 * `single-image-super-resolution-1033`, which is the primary and only model that
   performs super resolution 4x upscale on a 200x200 image
 
-For more information about the pre-trained models, refer to the [model documentation](../../intel_models/index.md).
+For more information about the pre-trained models, refer to the [model documentation](../../models/intel/index.md).
 
 ## How It Works
 

demos/text_detection_demo/README.md

@@ -7,7 +7,7 @@ The demo shows an example of using neural networks to detect and recognize print
 * `text-recognition-0012`, which is a recognition network for recognizing text.
 * `handwritten-score-recognition-0001`, which is a recognition network for recognizing handwritten score marks like `<digit>` or `<digit>.<digit>`.
 
-For more information about the pre-trained models, refer to the [model documentation](../../intel_models/index.md).
+For more information about the pre-trained models, refer to the [model documentation](../../models/intel/index.md).
 
 ## How It Works