Rename models/**/description directories to assets

The `description` name doesn't really fit them anymore, as they no longer
contain the actual description.

Currently, the only type of asset we have is images, but I'm keeping the
name generic, since we might later add sounds, texts and who knows what
else.

models/intel/action-recognition-0001/README.md

@@ -7,7 +7,7 @@ Please refer to the [kinetics](https://deepmind.com/research/open-source/open-so
 
 ## Example
 
-![](./description/demo.png)
+![](./assets/demo.png)
 
 ## Composite model specification
 

models/intel/age-gender-recognition-retail-0013/README.md

@@ -14,9 +14,9 @@ applicable for children since their faces were not in the training set.
 
 | Input Image                                   | Result        |
 |-----------------------------------------------|---------------|
-| ![](./description/age-gender-recognition-retail-0001.jpg) | Female, 18.97 |
-| ![](./description/age-gender-recognition-retail-0002.png) | Male, 26.52   |
-| ![](./description/age-gender-recognition-retail-0003.png) | Male, 33.41   |
+| ![](./assets/age-gender-recognition-retail-0001.jpg) | Female, 18.97 |
+| ![](./assets/age-gender-recognition-retail-0002.png) | Male, 26.52   |
+| ![](./assets/age-gender-recognition-retail-0003.png) | Male, 33.41   |
 
 ## Specification
 

models/intel/asl-recognition-0004/README.md

@@ -12,7 +12,7 @@ on the input clip.
 
 ## Example
 
-![](./description/asl-recognition-0004.jpg)
+![](./assets/asl-recognition-0004.jpg)
 
 ## Specification
 

models/intel/driver-action-recognition-adas-0002/README.md

@@ -6,7 +6,7 @@ This is an action recognition composite model for the driver monitoring use case
 
 ## Example
 
-![](./description/action-recognition-kelly.png)
+![](./assets/action-recognition-kelly.png)
 
 ## Composite model specification
 

models/intel/emotions-recognition-retail-0003/README.md

@@ -14,7 +14,7 @@ only the images containing five aforementioned emotions is chosen. The total amo
 
 | Input Image                                 | Result        |
 |---------------------------------------------|---------------|
-| ![](./description/emotions-recognition-retail-0003.jpg) | Happiness     |
+| ![](./assets/emotions-recognition-retail-0003.jpg) | Happiness     |
 
 ## Specification
 

models/intel/face-detection-0200/README.md

@@ -8,7 +8,7 @@ During the training of this model, training images were resized to 256x256.
 
 ## Example
 
-![](./description/face-detection-0200.png)
+![](./assets/face-detection-0200.png)
 
 ## Specification
 

models/intel/face-detection-0202/README.md

@@ -8,7 +8,7 @@ During the training of this model, training images were resized to 384x384.
 
 ## Example
 
-![](./description/face-detection-0202.png)
+![](./assets/face-detection-0202.png)
 
 ## Specification
 

models/intel/face-detection-0204/README.md

@@ -8,7 +8,7 @@ During the training of this model, training images were resized to 448x448.
 
 ## Example
 
-![](./description/face-detection-0204.png)
+![](./assets/face-detection-0204.png)
 
 ## Specification
 

models/intel/face-detection-0205/README.md

@@ -7,7 +7,7 @@ FCOS head for indoor and outdoor scenes shot by a front-facing camera.
 
 ## Example
 
-![](./description/face-detection-0205.png)
+![](./assets/face-detection-0205.png)
 
 ## Specification
 

models/intel/face-detection-0206/README.md

@@ -7,7 +7,7 @@ ATSS head for indoor and outdoor scenes shot by a front-facing camera.
 
 ## Example
 
-![](./description/face-detection-0206.png)
+![](./assets/face-detection-0206.png)
 
 ## Specification
 

models/intel/face-detection-adas-0001/README.md

@@ -8,7 +8,7 @@ amount of computation for the 3x3 convolution block.
 
 ## Example
 
-![](./description/face-detection-adas-0001.png)
+![](./assets/face-detection-adas-0001.png)
 
 ## Specification
 

models/intel/face-detection-retail-0004/README.md

@@ -9,7 +9,7 @@ head from 1/16 scale feature map has nine clustered prior boxes.
 
 ## Example
 
-![](./description/face-detection-retail-0001.png)
+![](./assets/face-detection-retail-0001.png)
 
 ## Specification
 

models/intel/face-detection-retail-0005/README.md

@@ -8,7 +8,7 @@ head from 1/16 scale feature map has nine clustered prior boxes.
 
 ## Example
 
-![](./description/face-detection-retail-0001.png)
+![](./assets/face-detection-retail-0001.png)
 
 ## Specification
 

models/intel/facial-landmarks-35-adas-0002/README.md

@@ -6,7 +6,7 @@ This is a custom-architecture convolutional neural network for 35 facial landmar
 
 ## Example and Landmarks Definition
 
-![](./description/landmarks_illustration.png)
+![](./assets/landmarks_illustration.png)
 
 [Left Eye]
 **p0, p1**: corners of the eye, located on the boundary of the eyeball and the eyelid.
@@ -47,7 +47,7 @@ A 1000-sample random subset of a large internal dataset containing images of 300
 
 The quality of landmarks' positions prediction is evaluated through the use of Normed Error (NE). The error for the i<sup>th</sup> sample has the form:
 
-![](./description/error_formula.png)
+![](./assets/error_formula.png)
 
 where N is the number of landmarks, _p_-hat and _p_ are, correspondingly, the prediction and ground truth vectors of the k<sup>th</sup> landmark of the i<sup>th</sup> sample, and d<sub>i</sub> is the interocular distance for the i<sup>th</sup> sample.
 

models/intel/formula-recognition-medium-scan-0001/README.md

@@ -10,7 +10,7 @@ Vocabulary file is located under corresponding model configuration directory, `<
 
 ## Example of the input data
 
-![](./description/formula-recognition-medium-scan-0001.png)
+![](./assets/formula-recognition-medium-scan-0001.png)
 
 ## Example of the output
 

models/intel/formula-recognition-polynomials-handwritten-0001/README.md

@@ -12,7 +12,7 @@ Vocabulary file is located under corresponding model configuration directory, `<
 
 ## Example of the input data
 
-![](./description/formula-recognition-polynomials-handwritten-0001.png)
+![](./assets/formula-recognition-polynomials-handwritten-0001.png)
 
 ## Example of the output
 

models/intel/gaze-estimation-adas-0002/README.md

@@ -6,7 +6,7 @@ This is a custom VGG-like convolutional neural network for gaze direction estima
 
 ## Example and Gaze Vector Definition
 
-![](./description/ill_for_gaze.png)
+![](./assets/ill_for_gaze.png)
 
 The network takes three inputs: square crop of left eye image, square crop of right eye image, and three head pose angles - (yaw, pitch, and roll) (see figure). The network outputs 3-D vector corresponding to the direction of a person's gaze in a Cartesian coordinate system in which z-axis is directed from person's eyes (mid-point between left and right eyes' centers) to the camera center, y-axis is vertical, and x-axis is orthogonal to both z,y axes so that (x,y,z) constitute a right-handed coordinate system.
 

models/intel/handwritten-japanese-recognition-0001/README.md

@@ -8,7 +8,7 @@ The network is able to recognize Japanese text consisting of characters in the [
 
 ## Example
 
-![](./description/test.png) -> 菊池朋子
+![](./assets/test.png) -> 菊池朋子
 
 ## Specification
 

models/intel/handwritten-score-recognition-0003/README.md

@@ -8,7 +8,7 @@ The network is able to recognize school marks that should have format either `<d
 
 ## Example
 
-![](./description/shot_25.png) -> Mark2.5
+![](./assets/shot_25.png) -> Mark2.5
 
 ## Specification
 

models/intel/handwritten-simplified-chinese-recognition-0001/README.md

@@ -8,7 +8,7 @@ The network is able to recognize simplified Chinese text consisting of character
 
 ## Example
 
-![](./description/test.png) -> 的人不一了是他有为在责新中任自之我们
+![](./assets/test.png) -> 的人不一了是他有为在责新中任自之我们
 
 ## Specification
 

models/intel/head-pose-estimation-adas-0001/README.md

@@ -12,7 +12,7 @@ one output.
 
 ## Example
 
-![](./description/head-pose-estimation-adas-0001.png)
+![](./assets/head-pose-estimation-adas-0001.png)
 
 ## Specification
 

models/intel/horizontal-text-detection-0001/README.md

@@ -8,7 +8,7 @@ The key benefit of this model compared to the [base model](../text-detection-000
 
 ## Example
 
-![](./description/horizontal-text-detection-0001.png)
+![](./assets/horizontal-text-detection-0001.png)
 
 ## Specification
 

models/intel/human-pose-estimation-0001/README.md

@@ -8,7 +8,7 @@ The pose may contain up to 18 keypoints: ears, eyes, nose, neck, shoulders, elbo
 
 ## Example
 
-![](./description/human-pose-estimation-0001.png)
+![](./assets/human-pose-estimation-0001.png)
 
 ## Specification
 

models/intel/human-pose-estimation-0005/README.md

@@ -8,7 +8,7 @@ The pose may contain up to 17 keypoints: ears, eyes, nose, shoulders, elbows, wr
 
 ## Example
 
-![](./description/human-pose-estimation-0005.png)
+![](./assets/human-pose-estimation-0005.png)
 
 ## Specification
 

models/intel/human-pose-estimation-0006/README.md

@@ -8,7 +8,7 @@ The pose may contain up to 17 keypoints: ears, eyes, nose, shoulders, elbows, wr
 
 ## Example
 
-![](./description/human-pose-estimation-0006.png)
+![](./assets/human-pose-estimation-0006.png)
 
 ## Specification
 

models/intel/human-pose-estimation-0007/README.md

@@ -8,7 +8,7 @@ The pose may contain up to 17 keypoints: ears, eyes, nose, shoulders, elbows, wr
 
 ## Example
 
-![](./description/human-pose-estimation-0007.png)
+![](./assets/human-pose-estimation-0007.png)
 
 ## Specification
 

models/intel/image-retrieval-0001/README.md

@@ -6,7 +6,7 @@ Image retrieval model based on [MobileNetV2](https://arxiv.org/abs/1801.04381) a
 
 ## Example
 
-![](./description/image-retrieval-0001.jpg)
+![](./assets/image-retrieval-0001.jpg)
 
 ## Specification
 

models/intel/instance-segmentation-security-0002/README.md

@@ -8,7 +8,7 @@ segmentation heads.
 
 ## Example
 
-![](./description/instance-segmentation-security-0002.png)
+![](./assets/instance-segmentation-security-0002.png)
 
 ## Specification
 

models/intel/instance-segmentation-security-0091/README.md

@@ -8,7 +8,7 @@ FPN, RPN, detection and segmentation heads.
 
 ## Example
 
-![](./description/instance-segmentation-security-0091.png)
+![](./assets/instance-segmentation-security-0091.png)
 
 ## Specification
 

models/intel/instance-segmentation-security-0228/README.md

@@ -8,7 +8,7 @@ detection and segmentation heads.
 
 ## Example
 
-![](./description/instance-segmentation-security-0228.png)
+![](./assets/instance-segmentation-security-0228.png)
 
 ## Specification
 

models/intel/instance-segmentation-security-1039/README.md

@@ -8,7 +8,7 @@ detection and segmentation heads.
 
 ## Example
 
-![](./description/instance-segmentation-security-1039.png)
+![](./assets/instance-segmentation-security-1039.png)
 
 ## Specification
 

models/intel/instance-segmentation-security-1040/README.md

@@ -8,7 +8,7 @@ detection and segmentation heads.
 
 ## Example
 
-![](./description/instance-segmentation-security-1040.png)
+![](./assets/instance-segmentation-security-1040.png)
 
 ## Specification
 

models/intel/landmarks-regression-retail-0009/README.md

@@ -6,7 +6,7 @@ This is a lightweight landmarks regressor for the Smart Classroom scenario. It h
 
 ## Example
 
-![](./description/landmarks-regression-retail-0009.png)
+![](./assets/landmarks-regression-retail-0009.png)
 
 ## Specification
 
@@ -20,7 +20,7 @@ This is a lightweight landmarks regressor for the Smart Classroom scenario. It h
 
 Normed Error (NE) for i<sup>th</sup> sample has the following form:
 
-![](./description/normed_error.png)
+![](./assets/normed_error.png)
 
 where N is the number of landmarks, _p_-hat and _p_ are, correspondingly, the prediction and ground truth vectors of k<sup>th</sup> landmark of i<sup>th</sup> sample, and d<sub>i</sub> is the interocular distance for i<sup>th</sup> sample.
 

models/intel/license-plate-recognition-barrier-0001/README.md

@@ -13,7 +13,7 @@ Chinese license plates in traffic.
 
 **Note:** The license plates on the image were modified to protect the owners' privacy.
 
-![](./description/license-plate-recognition-barrier-0001.png)
+![](./assets/license-plate-recognition-barrier-0001.png)
 
 ## Specification
 

models/intel/pedestrian-and-vehicle-detector-adas-0001/README.md

@@ -6,7 +6,7 @@ Pedestrian and vehicle detection network based on MobileNet v1.0 + SSD.
 
 ## Example
 
-![](./description/pedestrian-and-vehicle-detector-adas-0001.png)
+![](./assets/pedestrian-and-vehicle-detector-adas-0001.png)
 
 ## Specification
 

models/intel/pedestrian-detection-adas-0002/README.md

@@ -6,7 +6,7 @@ Pedestrian detection network based on SSD framework with tuned MobileNet v1 as a
 
 ## Example
 
-![](./description/pedestrian-detection-adas-0002.png)
+![](./assets/pedestrian-detection-adas-0002.png)
 
 ## Specification
 

models/intel/person-attributes-recognition-crossroad-0230/README.md

@@ -6,11 +6,11 @@ This model presents a person attributes classification algorithm analysis scenar
 
 ## Examples
 
-![](./description/person-attributes-recognition-crossroad-230-1.png)
-![](./description/person-attributes-recognition-crossroad-230-2.png)
-![](./description/person-attributes-recognition-crossroad-230-3.png)
-![](./description/person-attributes-recognition-crossroad-230-4.png)
-![](./description/person-attributes-recognition-crossroad-230-5.png)
+![](./assets/person-attributes-recognition-crossroad-230-1.png)
+![](./assets/person-attributes-recognition-crossroad-230-2.png)
+![](./assets/person-attributes-recognition-crossroad-230-3.png)
+![](./assets/person-attributes-recognition-crossroad-230-4.png)
+![](./assets/person-attributes-recognition-crossroad-230-5.png)
 
 ## Specification
 

models/intel/person-detection-0106/README.md

@@ -7,7 +7,7 @@ backbone.
 
 ## Example
 
-![](./description/person-detection-0106.png)
+![](./assets/person-detection-0106.png)
 
 ## Specification
 

models/intel/person-detection-0200/README.md

@@ -8,7 +8,7 @@ prior boxes for 256x256 resolution.
 
 ## Example
 
-![](./description/person-detection-0200.png)
+![](./assets/person-detection-0200.png)
 
 ## Specification
 

models/intel/person-detection-0201/README.md

@@ -8,7 +8,7 @@ prior boxes for 384x384 resolution.
 
 ## Example
 
-![](./description/person-detection-0201.png)
+![](./assets/person-detection-0201.png)
 
 ## Specification
 

models/intel/person-detection-0202/README.md

@@ -8,7 +8,7 @@ prior boxes for 512x512 resolution.
 
 ## Example
 
-![](./description/person-detection-0202.png)
+![](./assets/person-detection-0202.png)
 
 ## Specification