Fix a few typos and consistency issues in model docs

docs/models/.templates/models/advprop.md

@@ -1,7 +1,9 @@
-# AdvProp
+# AdvProp (EfficientNet)
 
 **AdvProp** is an adversarial training scheme which treats adversarial examples as additional examples, to prevent overfitting. Key to the method is the usage of a separate auxiliary batch norm for adversarial examples, as they have different underlying distributions to normal examples.
 
+The weights from this model were ported from [Tensorflow/TPU](https://github.com/tensorflow/tpu).
+
 {% include 'code_snippets.md' %}
 
 ## How do I train this model?

docs/models/.templates/models/csp-darknet.md

@@ -1,4 +1,4 @@
-# CSP DarkNet
+# CSP-DarkNet
 
 **CSPDarknet53** is a convolutional neural network and backbone for object detection that uses [DarkNet-53](https://paperswithcode.com/method/darknet-53). It employs a CSPNet strategy to partition the feature map of the base layer into two parts and then merges them through a cross-stage hierarchy. The use of a split and merge strategy allows for more gradient flow through the network. 
 

docs/models/.templates/models/csp-resnet.md

@@ -1,4 +1,4 @@
-# CSP ResNet
+# CSP-ResNet
 
 **CSPResNet** is a convolutional neural network where we apply the Cross Stage Partial Network (CSPNet) approach to [ResNet](https://paperswithcode.com/method/resnet). The CSPNet partitions the feature map of the base layer into two parts and then merges them through a cross-stage hierarchy. The use of a split and merge strategy allows for more gradient flow through the network.
 

docs/models/.templates/models/csp-resnext.md

@@ -1,4 +1,4 @@
-# CSP ResNeXt
+# CSP-ResNeXt
 
 **CSPResNeXt** is a convolutional neural network where we apply the Cross Stage Partial Network (CSPNet) approach to [ResNeXt](https://paperswithcode.com/method/resnext). The CSPNet partitions the feature map of the base layer into two parts and then merges them through a cross-stage hierarchy. The use of a split and merge strategy allows for more gradient flow through the network.
 

docs/models/.templates/models/ecaresnet.md

@@ -1,4 +1,4 @@
-# ECA ResNet
+# ECA-ResNet
 
 An **ECA ResNet** is a variant on a [ResNet](https://paperswithcode.com/method/resnet) that utilises an [Efficient Channel Attention module](https://paperswithcode.com/method/efficient-channel-attention). Efficient Channel Attention is an architectural unit based on [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) that reduces model complexity without dimensionality reduction. 
 

docs/models/.templates/models/ese-vovnet.md

@@ -1,4 +1,4 @@
-# ESE VoVNet
+# ESE-VoVNet
 
 **VoVNet** is a convolutional neural network that seeks to make [DenseNet](https://paperswithcode.com/method/densenet) more efficient by concatenating all features only once in the last feature map, which makes input size constant and enables enlarging new output channel. 
 

docs/models/.templates/models/gloun-inception-v3.md

@@ -1,4 +1,4 @@
-# Gluon Inception v3
+# (Gluon) Inception v3
 
 **Inception v3** is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolutions, and the use of an [auxiliary classifer](https://paperswithcode.com/method/auxiliary-classifier) to propagate label information lower down the network (along with the use of batch normalization for layers in the sidehead). The key building block is an [Inception Module](https://paperswithcode.com/method/inception-v3-module).
 

docs/models/.templates/models/gloun-resnet.md

@@ -1,4 +1,4 @@
-# Glu on ResNet
+# (Gluon) ResNet
 
 **Residual Networks**, or **ResNets**, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping. They stack [residual blocks](https://paperswithcode.com/method/residual-block) ontop of each other to form network: e.g. a ResNet-50 has fifty layers using these blocks. 
 

docs/models/.templates/models/gloun-resnext.md

@@ -1,4 +1,4 @@
-# Gluon ResNeXt
+# (Gluon) ResNeXt
 
 A **ResNeXt** repeats a [building block](https://paperswithcode.com/method/resnext-block) that aggregates a set of transformations with the same topology. Compared to a [ResNet](https://paperswithcode.com/method/resnet), it exposes a new dimension,  *cardinality* (the size of the set of transformations) $C$, as an essential factor in addition to the dimensions of depth and width. 
 

docs/models/.templates/models/gloun-senet.md

@@ -1,4 +1,4 @@
-# Summary
+# (Gluon) SENet
 
 A **SENet** is a convolutional neural network architecture that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
 

docs/models/.templates/models/gloun-seresnext.md

@@ -1,4 +1,4 @@
-# Summary
+# (Gluon) SE-ResNeXt
 
 **SE ResNeXt** is a variant of a [ResNext](https://www.paperswithcode.com/method/resnext) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
 

docs/models/.templates/models/gloun-xception.md

@@ -1,4 +1,4 @@
-# Summary
+# (Gluon) Xception
 
 **Xception** is a convolutional neural network architecture that relies solely on [depthwise separable convolution](https://paperswithcode.com/method/depthwise-separable-convolution) layers.
 

docs/models/.templates/models/inception-resnet-v2.md

@@ -1,4 +1,4 @@
-# Inception Resnet v2
+# Inception ResNet v2
 
 **Inception-ResNet-v2** is a convolutional neural architecture that builds on the Inception family of architectures but incorporates [residual connections](https://paperswithcode.com/method/residual-connection) (replacing the filter concatenation stage of the Inception architecture).
 

docs/models/.templates/models/legacy-se-resnet.md

@@ -1,4 +1,4 @@
-# (Legacy) SE ResNet
+# (Legacy) SE-ResNet
 
 **SE ResNet** is a variant of a [ResNet](https://www.paperswithcode.com/method/resnet) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
 

docs/models/.templates/models/legacy-se-resnext.md

@@ -1,4 +1,4 @@
-# (Legacy) SE ResNeXt
+# (Legacy) SE-ResNeXt
 
 **SE ResNeXt** is a variant of a [ResNeXt](https://www.paperswithcode.com/method/resnext) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
 

docs/models/.templates/models/se-resnet.md

@@ -1,4 +1,4 @@
-# SE ResNet
+# SE-ResNet
 
 **SE ResNet** is a variant of a [ResNet](https://www.paperswithcode.com/method/resnet) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
 

docs/models/.templates/models/seresnext.md

@@ -1,4 +1,4 @@
-# SE ResNeXt
+# SE-ResNeXt
 
 **SE ResNeXt** is a variant of a [ResNext](https://www.paperswithcode.com/method/resneXt) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
 

docs/models/.templates/models/skresnet.md

@@ -1,4 +1,4 @@
-# SK ResNet
+# SK-ResNet
 
 **SK ResNet** is a variant of a [ResNet](https://www.paperswithcode.com/method/resnet) that employs a [Selective Kernel](https://paperswithcode.com/method/selective-kernel) unit. In general, all the large kernel convolutions in the original bottleneck blocks in ResNet are replaced by the proposed [SK convolutions](https://paperswithcode.com/method/selective-kernel-convolution), enabling the network to choose appropriate receptive field sizes in an adaptive manner.
 

docs/models/.templates/models/skresnext.md

@@ -1,4 +1,4 @@
-# SK ResNeXt
+# SK-ResNeXt
 
 **SK ResNeXt** is a variant of a [ResNeXt](https://www.paperswithcode.com/method/resnext) that employs a [Selective Kernel](https://paperswithcode.com/method/selective-kernel) unit. In general, all the large kernel convolutions in the original bottleneck blocks in ResNext are replaced by the proposed [SK convolutions](https://paperswithcode.com/method/selective-kernel-convolution), enabling the network to choose appropriate receptive field sizes in an adaptive manner.
 

docs/models/advprop.md

@@ -1,7 +1,9 @@
-# AdvProp
+# AdvProp (EfficientNet)
 
 **AdvProp** is an adversarial training scheme which treats adversarial examples as additional examples, to prevent overfitting. Key to the method is the usage of a separate auxiliary batch norm for adversarial examples, as they have different underlying distributions to normal examples.
 
+The weights from this model were ported from [Tensorflow/TPU](https://github.com/tensorflow/tpu).
+
 ## How do I use this model on an image?
 To load a pretrained model:
 

docs/models/csp-darknet.md

@@ -1,4 +1,4 @@
-# CSP DarkNet
+# CSP-DarkNet
 
 **CSPDarknet53** is a convolutional neural network and backbone for object detection that uses [DarkNet-53](https://paperswithcode.com/method/darknet-53). It employs a CSPNet strategy to partition the feature map of the base layer into two parts and then merges them through a cross-stage hierarchy. The use of a split and merge strategy allows for more gradient flow through the network. 
 

docs/models/csp-resnet.md

@@ -1,4 +1,4 @@
-# CSP ResNet
+# CSP-ResNet
 
 **CSPResNet** is a convolutional neural network where we apply the Cross Stage Partial Network (CSPNet) approach to [ResNet](https://paperswithcode.com/method/resnet). The CSPNet partitions the feature map of the base layer into two parts and then merges them through a cross-stage hierarchy. The use of a split and merge strategy allows for more gradient flow through the network.
 

docs/models/csp-resnext.md

@@ -1,4 +1,4 @@
-# CSP ResNeXt
+# CSP-ResNeXt
 
 **CSPResNeXt** is a convolutional neural network where we apply the Cross Stage Partial Network (CSPNet) approach to [ResNeXt](https://paperswithcode.com/method/resnext). The CSPNet partitions the feature map of the base layer into two parts and then merges them through a cross-stage hierarchy. The use of a split and merge strategy allows for more gradient flow through the network.
 

docs/models/ecaresnet.md

@@ -1,4 +1,4 @@
-# ECA ResNet
+# ECA-ResNet
 
 An **ECA ResNet** is a variant on a [ResNet](https://paperswithcode.com/method/resnet) that utilises an [Efficient Channel Attention module](https://paperswithcode.com/method/efficient-channel-attention). Efficient Channel Attention is an architectural unit based on [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) that reduces model complexity without dimensionality reduction. 
 

docs/models/ese-vovnet.md

@@ -1,4 +1,4 @@
-# ESE VoVNet
+# ESE-VoVNet
 
 **VoVNet** is a convolutional neural network that seeks to make [DenseNet](https://paperswithcode.com/method/densenet) more efficient by concatenating all features only once in the last feature map, which makes input size constant and enables enlarging new output channel. 
 

docs/models/gloun-inception-v3.md

@@ -1,4 +1,4 @@
-# Gluon Inception v3
+# (Gluon) Inception v3
 
 **Inception v3** is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolutions, and the use of an [auxiliary classifer](https://paperswithcode.com/method/auxiliary-classifier) to propagate label information lower down the network (along with the use of batch normalization for layers in the sidehead). The key building block is an [Inception Module](https://paperswithcode.com/method/inception-v3-module).
 

docs/models/gloun-resnet.md

@@ -1,4 +1,4 @@
-# Glu on ResNet
+# (Gluon) ResNet
 
 **Residual Networks**, or **ResNets**, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping. They stack [residual blocks](https://paperswithcode.com/method/residual-block) ontop of each other to form network: e.g. a ResNet-50 has fifty layers using these blocks. 
 

docs/models/gloun-resnext.md

@@ -1,4 +1,4 @@
-# Gluon ResNeXt
+# (Gluon) ResNeXt
 
 A **ResNeXt** repeats a [building block](https://paperswithcode.com/method/resnext-block) that aggregates a set of transformations with the same topology. Compared to a [ResNet](https://paperswithcode.com/method/resnet), it exposes a new dimension,  *cardinality* (the size of the set of transformations) $C$, as an essential factor in addition to the dimensions of depth and width. 
 

docs/models/gloun-senet.md

@@ -1,4 +1,4 @@
-# Summary
+# (Gluon) SENet
 
 A **SENet** is a convolutional neural network architecture that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
 

docs/models/gloun-seresnext.md

@@ -1,4 +1,4 @@
-# Summary
+# (Gluon) SE-ResNeXt
 
 **SE ResNeXt** is a variant of a [ResNext](https://www.paperswithcode.com/method/resnext) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
 

docs/models/gloun-xception.md

@@ -1,4 +1,4 @@
-# Summary
+# (Gluon) Xception
 
 **Xception** is a convolutional neural network architecture that relies solely on [depthwise separable convolution](https://paperswithcode.com/method/depthwise-separable-convolution) layers.
 

docs/models/inception-resnet-v2.md

@@ -1,4 +1,4 @@
-# Inception Resnet v2
+# Inception ResNet v2
 
 **Inception-ResNet-v2** is a convolutional neural architecture that builds on the Inception family of architectures but incorporates [residual connections](https://paperswithcode.com/method/residual-connection) (replacing the filter concatenation stage of the Inception architecture).
 

docs/models/legacy-se-resnet.md

@@ -1,4 +1,4 @@
-# (Legacy) SE ResNet
+# (Legacy) SE-ResNet
 
 **SE ResNet** is a variant of a [ResNet](https://www.paperswithcode.com/method/resnet) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
 

docs/models/legacy-se-resnext.md

@@ -1,4 +1,4 @@
-# (Legacy) SE ResNeXt
+# (Legacy) SE-ResNeXt
 
 **SE ResNeXt** is a variant of a [ResNeXt](https://www.paperswithcode.com/method/resnext) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
 

docs/models/se-resnet.md

@@ -1,4 +1,4 @@
-# SE ResNet
+# SE-ResNet
 
 **SE ResNet** is a variant of a [ResNet](https://www.paperswithcode.com/method/resnet) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
 

docs/models/seresnext.md

@@ -1,4 +1,4 @@
-# SE ResNeXt
+# SE-ResNeXt
 
 **SE ResNeXt** is a variant of a [ResNext](https://www.paperswithcode.com/method/resneXt) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
 

docs/models/skresnet.md

@@ -1,4 +1,4 @@
-# SK ResNet
+# SK-ResNet
 
 **SK ResNet** is a variant of a [ResNet](https://www.paperswithcode.com/method/resnet) that employs a [Selective Kernel](https://paperswithcode.com/method/selective-kernel) unit. In general, all the large kernel convolutions in the original bottleneck blocks in ResNet are replaced by the proposed [SK convolutions](https://paperswithcode.com/method/selective-kernel-convolution), enabling the network to choose appropriate receptive field sizes in an adaptive manner.
 

docs/models/skresnext.md

@@ -1,4 +1,4 @@
-# SK ResNeXt
+# SK-ResNeXt
 
 **SK ResNeXt** is a variant of a [ResNeXt](https://www.paperswithcode.com/method/resnext) that employs a [Selective Kernel](https://paperswithcode.com/method/selective-kernel) unit. In general, all the large kernel convolutions in the original bottleneck blocks in ResNext are replaced by the proposed [SK convolutions](https://paperswithcode.com/method/selective-kernel-convolution), enabling the network to choose appropriate receptive field sizes in an adaptive manner.