remove unused section Example

ivanvikhrev · Nov 5, 2020 · b5a0503 · b5a0503
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diff --git a/models/intel/yolo-v2-ava-0001/description/yolo-v2-ava-0001.md b/models/intel/yolo-v2-ava-0001/description/yolo-v2-ava-0001.md
@@ -4,8 +4,6 @@
 
 This is a reimplemented and retrained version of the [YOLO v2](https://arxiv.org/abs/1612.08242) object detection network trained with the VOC2012 training dataset.
 
-## Example
-
 ## Specification
 
 | Metric                          | Value                                     |

diff --git a/models/intel/yolo-v2-ava-sparse-35-0001/description/yolo-v2-ava-sparse-35-0001.md b/models/intel/yolo-v2-ava-sparse-35-0001/description/yolo-v2-ava-sparse-35-0001.md
@@ -5,8 +5,6 @@
 This is a reimplemented and retrained version of the [YOLO v2](https://arxiv.org/abs/1612.08242) object detection network trained with the VOC2012 training dataset.
 [Network weight pruning](https://arxiv.org/abs/1710.01878) is applied to sparsify convolution layers (35% of network parameters are set to zeros).
 
-## Example
-
 ## Specification
 
 | Metric                       | Value        |

diff --git a/models/intel/yolo-v2-ava-sparse-70-0001/description/yolo-v2-ava-sparse-70-0001.md b/models/intel/yolo-v2-ava-sparse-70-0001/description/yolo-v2-ava-sparse-70-0001.md
@@ -5,8 +5,6 @@
 This is a reimplemented and retrained version of the [YOLO v2](https://arxiv.org/abs/1612.08242) object detection network trained with the VOC2012 training dataset.
 [Network weight pruning](https://arxiv.org/abs/1710.01878) is applied to sparsify convolution layers (70% of network parameters are set to zeros).
 
-## Example
-
 ## Specification
 
 | Metric                       | Value        |

diff --git a/models/intel/yolo-v2-tiny-ava-0001/description/yolo-v2-tiny-ava-0001.md b/models/intel/yolo-v2-tiny-ava-0001/description/yolo-v2-tiny-ava-0001.md
@@ -4,8 +4,6 @@
 
 This is a reimplemented and retrained version of the [tiny YOLO v2](https://arxiv.org/abs/1612.08242) object detection network trained with the VOC2012 training dataset.
 
-## Example
-
 ## Specification
 
 | Metric                          | Value                                     |

diff --git a/.../yolo-v2-tiny-ava-sparse-30-0001/description/yolo-v2-tiny-ava-sparse-30-0001.md b/.../yolo-v2-tiny-ava-sparse-30-0001/description/yolo-v2-tiny-ava-sparse-30-0001.md
@@ -5,8 +5,6 @@
 This is a reimplemented and retrained version of the [tiny YOLO v2](https://arxiv.org/abs/1612.08242) object detection network trained with the VOC2012 training dataset.
 [Network weight pruning](https://arxiv.org/abs/1710.01878) is applied to sparsify convolution layers (30% of network parameters are set to zeros).
 
-## Example
-
 ## Specification
 
 | Metric                          | Value                                     |

diff --git a/.../yolo-v2-tiny-ava-sparse-60-0001/description/yolo-v2-tiny-ava-sparse-60-0001.md b/.../yolo-v2-tiny-ava-sparse-60-0001/description/yolo-v2-tiny-ava-sparse-60-0001.md
@@ -5,8 +5,6 @@
 This is a reimplemented and retrained version of the [tiny YOLO v2](https://arxiv.org/abs/1612.08242) object detection network trained with the VOC2012 training dataset.
 [Network weight pruning](https://arxiv.org/abs/1710.01878) is applied to sparsify convolution layers (60% of network parameters are set to zeros).
 
-## Example
-
 ## Specification
 
 | Metric                          | Value                                     |

diff --git a/models/public/Sphereface/Sphereface.md b/models/public/Sphereface/Sphereface.md
@@ -4,8 +4,6 @@
 
 [Deep face recognition under open-set protocol](https://arxiv.org/abs/1704.08063)
 
-## Example
-
 ## Specification
 
 | Metric            | Value         |

diff --git a/models/public/aclnet/aclnet.md b/models/public/aclnet/aclnet.md
@@ -10,8 +10,6 @@ The model input is a segment of PCM audio samples in [N, C, 1, L] format.
 
 The model output for `AclNet` is the sound classifier output for the 53 different environmental sound classes from the internal sound database.
 
-## Example
-
 ## Specification
 
 | Metric            | Value         |

diff --git a/models/public/alexnet/alexnet.md b/models/public/alexnet/alexnet.md
@@ -8,8 +8,6 @@ The model input is a blob that consists of a single image of 1x3x227x227 in BGR
 
 The model output for `alexnet` is the usual object classifier output for the 1000 different classifications matching those in the ImageNet database.
 
-## Example
-
 ## Specification
 
 | Metric            | Value         |

diff --git a/models/public/brain-tumor-segmentation-0001/brain-tumor-segmentation-0001.md b/models/public/brain-tumor-segmentation-0001/brain-tumor-segmentation-0001.md
@@ -6,8 +6,6 @@ This model was created for participation in the [Brain Tumor Segmentation Challe
 The model is based on [the corresponding paper](https://arxiv.org/abs/1810.04008), where authors present deep cascaded approach for automatic brain tumor segmentation. The paper describes modifications to 3D UNet architecture and specific augmentation strategy to efficiently handle multimodal MRI input. Besides this, the approach to enhance segmentation quality with context obtained from models of the same topology operating on downscaled data is introduced.
 Each input modality has its own encoder which are later fused together to produce single output segmentation.
 
-## Example
-
 ## Specification
 
 | Metric            | Value         |

diff --git a/models/public/brain-tumor-segmentation-0002/brain-tumor-segmentation-0002.md b/models/public/brain-tumor-segmentation-0002/brain-tumor-segmentation-0002.md
@@ -4,8 +4,6 @@
 
 This model was created for participation in the [Brain Tumor Segmentation Challenge](https://www.med.upenn.edu/cbica/brats2019/registration.html) (BraTS) 2019. It has the UNet architecture trained with residual blocks.
 
-## Example
-
 ## Specification
 
 | Metric            | Value         |

diff --git a/models/public/caffenet/caffenet.md b/models/public/caffenet/caffenet.md
@@ -4,8 +4,6 @@
 
 CaffeNet\* model is used for classification. For details see [paper](https://arxiv.org/abs/1408.5093).
 
-## Example
-
 ## Specification
 
 | Metric                          | Value                                     |

diff --git a/models/public/colorization-siggraph/colorization-siggraph.md b/models/public/colorization-siggraph/colorization-siggraph.md
@@ -9,8 +9,6 @@ For details about this family of models, check out the [repository](https://gith
 Model consumes as input L-channel of LAB-image (also user points and binary mask as optional inputs).
 Model give as output predict A- and B-channels of LAB-image.
 
-## Example
-
 ## Specification
 
 | Metric            | Value         |

diff --git a/models/public/colorization-v2/colorization-v2.md b/models/public/colorization-v2/colorization-v2.md
@@ -9,8 +9,6 @@ For details about this family of models, check out the [repository](https://gith
 Model consumes as input L-channel of LAB-image.
 Model give as output predict A- and B-channels of LAB-image.
 
-## Example
-
 ## Specification
 
 | Metric            | Value         |

diff --git a/models/public/ctdet_coco_dlav0_384/ctdet_coco_dlav0_384.md b/models/public/ctdet_coco_dlav0_384/ctdet_coco_dlav0_384.md
@@ -30,8 +30,6 @@ git apply /path/to/pytorch-onnx.patch
 python convert.py ctdet --load_model /path/to/downloaded/weights.pth --exp_id coco_dlav0_384 --arch dlav0_34 --input_res 384 --gpus -1
 ```
 
-## Example
-
 ## Specification
 
 | Metric                          | Value                                     |

diff --git a/models/public/ctdet_coco_dlav0_512/ctdet_coco_dlav0_512.md b/models/public/ctdet_coco_dlav0_512/ctdet_coco_dlav0_512.md
@@ -30,8 +30,6 @@ git apply /path/to/pytorch-onnx.patch
 python convert.py ctdet --load_model /path/to/downloaded/weights.pth --exp_id coco_dlav0_512 --arch dlav0_34 --input_res 512 --gpus -1
 ```
 
-## Example
-
 ## Specification
 
 | Metric                          | Value                                     |

diff --git a/models/public/ctpn/ctpn.md b/models/public/ctpn/ctpn.md
@@ -4,8 +4,6 @@
 
 Detecting Text in Natural Image with Connectionist Text Proposal Network. For details see [paper](https://arxiv.org/abs/1609.03605).
 
-## Example
-
 ## Specification
 
 | Metric                          | Value                                     |

diff --git a/models/public/deeplabv3/deeplabv3.md b/models/public/deeplabv3/deeplabv3.md
@@ -4,8 +4,6 @@
 
 DeepLab is a state-of-art deep learning model for semantic image segmentation. For details see [paper](https://arxiv.org/abs/1706.05587).
 
-## Example
-
 ## Specification
 
 | Metric            | Value                |

diff --git a/models/public/densenet-121-caffe2/densenet-121-caffe2.md b/models/public/densenet-121-caffe2/densenet-121-caffe2.md
@@ -8,8 +8,6 @@ was converted from Caffe\* to Caffe2\* format.
 For details see repository <https://github.com/facebookarchive/models/tree/master/densenet121>,
 paper <https://arxiv.org/abs/1608.06993>.
 
-## Example
-
 ## Specification
 
 | Metric            | Value         |

diff --git a/models/public/densenet-121-tf/densenet-121-tf.md b/models/public/densenet-121-tf/densenet-121-tf.md
@@ -5,8 +5,6 @@
 This is a TensorFlow\* version of `densenet-121` model, one of the DenseNet\*
 group of models designed to perform image classification. The weights were converted from DenseNet-Keras Models. For details, see [repository](https://github.com/pudae/tensorflow-densenet/) and [paper](https://arxiv.org/abs/1608.06993).
 
-## Example
-
 ## Specification
 
 | Metric                          | Value                                     |

diff --git a/models/public/densenet-121/densenet-121.md b/models/public/densenet-121/densenet-121.md
@@ -9,8 +9,6 @@ been pretrained on the ImageNet image database. For details about this family of
 models, check out the [repository](https://github.com/shicai/DenseNet-Caffe).
 
 
-## Example
-
 ## Specification
 
 | Metric            | Value         |

diff --git a/models/public/densenet-161-tf/densenet-161-tf.md b/models/public/densenet-161-tf/densenet-161-tf.md
@@ -5,8 +5,6 @@
 This is a TensorFlow\* version of `densenet-161` model, one of the DenseNet
 group of models designed to perform image classification. The weights were converted from DenseNet-Keras Models. For details see [repository](https://github.com/pudae/tensorflow-densenet/), [paper](https://arxiv.org/abs/1608.06993).
 
-## Example
-
 ## Specification
 
 | Metric                          | Value                                     |

diff --git a/models/public/densenet-161/densenet-161.md b/models/public/densenet-161/densenet-161.md
@@ -18,8 +18,6 @@ by 0.017.
 The model output for `densenet-161` is the typical object classifier output for
 the 1000 different classifications matching those in the ImageNet database.
 
-## Example
-
 ## Specification
 
 | Metric            | Value         |

diff --git a/models/public/densenet-169-tf/densenet-169-tf.md b/models/public/densenet-169-tf/densenet-169-tf.md
@@ -5,8 +5,6 @@
 This is a TensorFlow\* version of `densenet-169` model, one of the DenseNet
 group of models designed to perform image classification. The weights were converted from DenseNet-Keras Models. For details, see [repository](https://github.com/pudae/tensorflow-densenet/) and [paper](https://arxiv.org/abs/1608.06993).
 
-## Example
-
 ## Specification
 
 | Metric                          | Value                                     |

diff --git a/models/public/densenet-169/densenet-169.md b/models/public/densenet-169/densenet-169.md
@@ -18,8 +18,6 @@ by 0.017.
 The model output for `densenet-169` is the typical object classifier output for
 the 1000 different classifications matching those in the ImageNet database.
 
-## Example
-
 ## Specification
 
 | Metric            | Value         |

diff --git a/models/public/densenet-201/densenet-201.md b/models/public/densenet-201/densenet-201.md
@@ -18,8 +18,6 @@ by 0.017.
 The model output for `densenet-201` is the typical object classifier output for
 the 1000 different classifications matching those in the ImageNet database.
 
-## Example
-
 ## Specification
 
 | Metric            | Value         |

diff --git a/models/public/efficientnet-b0-pytorch/efficientnet-b0-pytorch.md b/models/public/efficientnet-b0-pytorch/efficientnet-b0-pytorch.md
@@ -12,8 +12,6 @@ order. Before passing the image blob to the network, do the following:
 The model output for `efficientnet-b0-pytorch` is the typical object classifier output for
 1000 different classifications matching those in the ImageNet database.
 
-## Example
-
 ## Specification
 
 | Metric            | Value         |

diff --git a/models/public/efficientnet-b0/efficientnet-b0.md b/models/public/efficientnet-b0/efficientnet-b0.md
@@ -8,8 +8,6 @@ This model was pretrained in TensorFlow\*.
 All the EfficientNet models have been pretrained on the ImageNet\* image database.
 For details about this family of models, check out the [TensorFlow Cloud TPU repository](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet).
 
-## Example
-
 ## Specification
 
 | Metric            | Value         |

diff --git a/models/public/efficientnet-b0_auto_aug/efficientnet-b0_auto_aug.md b/models/public/efficientnet-b0_auto_aug/efficientnet-b0_auto_aug.md
@@ -9,8 +9,6 @@ This model was pretrained in TensorFlow\*.
 All the EfficientNet models have been pretrained on the ImageNet\* image database.
 For details about this family of models, check out the [TensorFlow Cloud TPU repository](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet).
 
-## Example
-
 ## Specification
 
 | Metric            | Value         |

diff --git a/models/public/efficientnet-b5-pytorch/efficientnet-b5-pytorch.md b/models/public/efficientnet-b5-pytorch/efficientnet-b5-pytorch.md
@@ -14,8 +14,6 @@ order. Before passing the image blob to the network, do the following:
 The model output for `efficientnet-b5-pytorch` is the typical object classifier output for
 the 1000 different classifications matching those in the ImageNet database.
 
-## Example
-
 ## Specification
 
 | Metric            | Value         |

diff --git a/models/public/efficientnet-b5/efficientnet-b5.md b/models/public/efficientnet-b5/efficientnet-b5.md
@@ -8,8 +8,6 @@ This model was pretrained in TensorFlow\*.
 All the EfficientNet models have been pretrained on the ImageNet\* image database.
 For details about this family of models, check out the [TensorFlow Cloud TPU repository](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet).
 
-## Example
-
 ## Specification
 
 | Metric            | Value         |

diff --git a/models/public/efficientnet-b7-pytorch/efficientnet-b7-pytorch.md b/models/public/efficientnet-b7-pytorch/efficientnet-b7-pytorch.md
@@ -13,8 +13,6 @@ order. Before passing the image blob to the network, do the following:
 The model output for `efficientnet-b7-pytorch` is the typical object classifier output for
 the 1000 different classifications matching those in the ImageNet database.
 
-## Example
-
 ## Specification
 
 | Metric            | Value         |

diff --git a/models/public/efficientnet-b7_auto_aug/efficientnet-b7_auto_aug.md b/models/public/efficientnet-b7_auto_aug/efficientnet-b7_auto_aug.md
@@ -9,8 +9,6 @@ This model was pretrained in TensorFlow\*.
 All the EfficientNet models have been pretrained on the ImageNet\* image database.
 For details about this family of models, check out the [TensorFlow Cloud TPU repository](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet).
 
-## Example
-
 ## Specification
 
 | Metric            | Value         |

diff --git a/...ace-recognition-mobilefacenet-arcface/face-recognition-mobilefacenet-arcface.md b/...ace-recognition-mobilefacenet-arcface/face-recognition-mobilefacenet-arcface.md
@@ -6,8 +6,6 @@ The original name of the model is [MobileFaceNet,ArcFace@ms1m-refine-v1](https:/
 
 [Deep face recognition net with MobileFaceNet backbone and Arcface loss](https://arxiv.org/abs/1801.07698)
 
-## Example
-
 ## Specification
 
 | Metric            | Value         |

diff --git a/...public/face-recognition-resnet100-arcface/face-recognition-resnet100-arcface.md b/...public/face-recognition-resnet100-arcface/face-recognition-resnet100-arcface.md
@@ -6,8 +6,6 @@ The original name of the model is [LResNet100E-IR,ArcFace@ms1m-refine-v2](https:
 
 [Deep face recognition net with ResNet100 backbone and Arcface loss](https://arxiv.org/abs/1801.07698)
 
-## Example
-
 ## Specification
 
 | Metric            | Value         |

diff --git a/...s/public/face-recognition-resnet34-arcface/face-recognition-resnet34-arcface.md b/...s/public/face-recognition-resnet34-arcface/face-recognition-resnet34-arcface.md
@@ -6,8 +6,6 @@ The original name of the model is [LResNet34E-IR,ArcFace@ms1m-refine-v1](https:/
 
 [Deep face recognition net with ResNet34 backbone and Arcface loss](https://arxiv.org/abs/1801.07698)
 
-## Example
-
 ## Specification
 
 | Metric            | Value         |

diff --git a/...s/public/face-recognition-resnet50-arcface/face-recognition-resnet50-arcface.md b/...s/public/face-recognition-resnet50-arcface/face-recognition-resnet50-arcface.md
@@ -6,8 +6,6 @@ The original name of the model is [LResNet50E-IR,ArcFace@ms1m-refine-v1](https:/
 
 [Deep face recognition net with ResNet50 backbone and Arcface loss](https://arxiv.org/abs/1801.07698)
 
-## Example
-
 ## Specification
 
 | Metric            | Value         |

diff --git a/models/public/faceboxes-pytorch/faceboxes-pytorch.md b/models/public/faceboxes-pytorch/faceboxes-pytorch.md
@@ -5,8 +5,6 @@
 FaceBoxes: A CPU Real-time Face Detector with High Accuracy. For details see
 the [repository](https://github.com/zisianw/FaceBoxes.PyTorch), [paper](https://arxiv.org/pdf/1708.05234.pdf)
 
-## Example
-
 ## Specification
 
 | Metric                          | Value                                    |

diff --git a/models/public/facenet-20180408-102900/facenet-20180408-102900.md b/models/public/facenet-20180408-102900/facenet-20180408-102900.md
@@ -4,8 +4,6 @@
 
 FaceNet: A Unified Embedding for Face Recognition and Clustering. For details see the [repository](https://github.com/davidsandberg/facenet/), [paper](https://arxiv.org/abs/1503.03832)
 
-## Example
-
 ## Specification
 
 | Metric                          | Value                                     |

diff --git a/models/public/fast-neural-style-mosaic-onnx/fast-neural-style-mosaic-onnx.md b/models/public/fast-neural-style-mosaic-onnx/fast-neural-style-mosaic-onnx.md
@@ -10,9 +10,6 @@ Transfer and Super-Resolution](https://arxiv.org/abs/1603.08155) along with
 models are provided in the [repository](https://github.com/onnx/models).
 
 
-## Example
-
-
 ## Specification
 
 | Metric            | Value            |

diff --git a/..._inception_resnet_v2_atrous_coco/faster_rcnn_inception_resnet_v2_atrous_coco.md b/..._inception_resnet_v2_atrous_coco/faster_rcnn_inception_resnet_v2_atrous_coco.md
@@ -4,8 +4,6 @@
 
 Faster R-CNN with Inception Resnet v2 Atrous version. Used for object detection. For details see the [paper](https://arxiv.org/abs/1506.01497v3).
 
-## Example
-
 ## Specification
 
 | Metric                          | Value                                     |

diff --git a/models/public/faster_rcnn_inception_v2_coco/faster_rcnn_inception_v2_coco.md b/models/public/faster_rcnn_inception_v2_coco/faster_rcnn_inception_v2_coco.md
@@ -4,8 +4,6 @@
 
 Faster R-CNN with Inception v2. Used for object detection. For details, see the [paper](https://arxiv.org/abs/1506.01497v3).
 
-## Example
-
 ## Specification
 
 | Metric                          | Value                                     |

diff --git a/models/public/faster_rcnn_resnet101_coco/faster_rcnn_resnet101_coco.md b/models/public/faster_rcnn_resnet101_coco/faster_rcnn_resnet101_coco.md
@@ -4,8 +4,6 @@
 
 Faster R-CNN Resnet-101 model. Used for object detection. For details, see the [paper](https://arxiv.org/abs/1506.01497v3).
 
-## Example
-
 ## Specification
 
 | Metric                          | Value                                     |

diff --git a/models/public/faster_rcnn_resnet50_coco/faster_rcnn_resnet50_coco.md b/models/public/faster_rcnn_resnet50_coco/faster_rcnn_resnet50_coco.md
@@ -4,8 +4,6 @@
 
 Faster R-CNN Resnet-50 model. Used for object detection. For details, see the [paper](https://arxiv.org/abs/1506.01497v3).
 
-## Example
-
 ## Specification
 
 | Metric                          | Value                                     |