Merge branch 'master' of https://github.com/gcucurull/pytorch-image-models into gcucurull-master

Author: rwightman

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

@@ -53,12 +53,19 @@ You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-ima
 ```
 
 <!--
+Type: model-index
+Collections:
+- Name: Adversarial Inception v3
+ Paper:
+ Title: Adversarial Attacks and Defences Competition
+ URL: https://paperswithcode.com/paper/adversarial-attacks-and-defences-competition
 Models:
 - Name: adv_inception_v3
+ In Collection: Adversarial Inception v3
  Metadata:
  FLOPs: 7352418880
- Training Data:
- - ImageNet
+ Parameters: 23830000
+ File Size: 95549439
  Architecture:
  - 1x1 Convolution
  - Auxiliary Classifier
@@ -72,20 +79,20 @@ Models:
  - Max Pooling
  - ReLU
  - Softmax
- File Size: 95549439
  Tasks:
  - Image Classification
+ Training Data:
+ - ImageNet
  ID: adv_inception_v3
  Crop Pct: '0.875'
  Image Size: '299'
  Interpolation: bicubic
  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/inception_v3.py#L456
- In Collection: Adversarial Inception v3
-Collections:
-- Name: Adversarial Inception v3
- Paper:
- title: Adversarial Attacks and Defences Competition
- url: https://paperswithcode.com//paper/adversarial-attacks-and-defences-competition
- type: model-index
-Type: model-index
+ Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/adv_inception_v3-9e27bd63.pth
+ Results:
+ - Task: Image Classification
+ Dataset: ImageNet
+ Metrics:
+ Top 1 Accuracy: 77.58%
+ Top 5 Accuracy: 93.74%
 -->