Rerun modelindex generate script

Author: rwightman

docs/models/adversarial-inception-v3.md

@@ -1,9 +1,11 @@
-# Summary
+# Adversarial 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).
 
 This particular model was trained for study of adversarial examples (adversarial training).
 
+The weights from this model were ported from [Tensorflow/Models](https://github.com/tensorflow/models).
+
 ## How do I use this model on an image?
 To load a pretrained model:
 

docs/models/advprop.md

@@ -1,4 +1,4 @@
-# Summary
+# AdvProp
 
 **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.
 

docs/models/big-transfer.md

@@ -1,4 +1,4 @@
-# Summary
+# Big Transfer (BiT)
 
 **Big Transfer (BiT)** is a type of pretraining recipe that pre-trains  on a large supervised source dataset, and fine-tunes the weights on the target task. Models are trained on the JFT-300M dataset. The finetuned models contained in this collection are finetuned on ImageNet.
 

docs/models/csp-darknet.md

@@ -1,4 +1,4 @@
-# Summary
+# 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 @@
-# Summary
+# 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 @@
-# Summary
+# 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/densenet.md

@@ -1,4 +1,4 @@
-# Summary
+# DenseNet
 
 **DenseNet** is a type of convolutional neural network that utilises dense connections between layers, through [Dense Blocks](http://www.paperswithcode.com/method/dense-block), where we connect *all layers* (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional inputs from all preceding layers and passes on its own feature-maps to all subsequent layers.
 

docs/models/dla.md

@@ -1,4 +1,4 @@
-# Summary
+# Deep Layer Aggregation
 
 Extending  “shallow” skip connections, **Dense Layer Aggregation (DLA)** incorporates more depth and sharing. The authors introduce two structures for deep layer aggregation (DLA): iterative deep aggregation (IDA) and hierarchical deep aggregation (HDA). These structures are expressed through an architectural framework, independent of the choice of backbone, for compatibility with current and future networks. 
 

docs/models/dpn.md

@@ -1,4 +1,4 @@
-# Summary
+# Dual Path Network (DPN)
 
 A **Dual Path Network (DPN)** is a convolutional neural network which presents a new topology of connection paths internally. The intuition is that [ResNets](https://paperswithcode.com/method/resnet) enables feature re-usage while DenseNet enables new feature exploration, and both are important for learning good representations. To enjoy the benefits from both path topologies, Dual Path Networks share common features while maintaining the flexibility to explore new features through dual path architectures. 
 

docs/models/ecaresnet.md

@@ -1,4 +1,4 @@
-# Summary
+# 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.