update imagenet list

README.md

@@ -1,11 +1,4 @@
 # Sample code for the Class Activation Mapping
-We propose a simple technique to expose the implicit attention of Convolutional Neural Networks on the image. It highlights the most informative image regions relevant to the predicted class. You could get attention-based model instantly by tweaking your own CNN a little bit more. The paper is published at [CVPR'16](http://arxiv.org/pdf/1512.04150.pdf).
-
-The framework of the Class Activation Mapping is as below:
-![Framework](http://cnnlocalization.csail.mit.edu/framework.jpg)
-
-Some predicted class activation maps are:
-![Results](http://cnnlocalization.csail.mit.edu/example.jpg)
 
 ### NEW: PyTorch Demo code
 * The popular networks such as ResNet, DenseNet, SqueezeNet, Inception already have global average pooling at the end, so you could generate the heatmap directly without even modifying the network architecture. Here is a [sample script](pytorch_CAM.py) to generate CAM for the pretrained networks.
@@ -14,6 +7,16 @@ Some predicted class activation maps are:
 ```
 You also could take a look at the [unified PlacesCNN scene prediction code](https://github.com/CSAILVision/places365/blob/master/run_placesCNN_unified.py) to see how the CAM along with scene categories, scene attributes are predicted. It has been used in the [PlacesCNN scene recognition demo](http://places2.csail.mit.edu/demo.html).
 
+
+
+We propose a simple technique to expose the implicit attention of Convolutional Neural Networks on the image. It highlights the most informative image regions relevant to the predicted class. You could get attention-based model instantly by tweaking your own CNN a little bit more. The paper is published at [CVPR'16](http://arxiv.org/pdf/1512.04150.pdf).
+
+The framework of the Class Activation Mapping is as below:
+![Framework](http://cnnlocalization.csail.mit.edu/framework.jpg)
+
+Some predicted class activation maps are:
+![Results](http://cnnlocalization.csail.mit.edu/example.jpg)
+
 ### Pre-trained models in Caffe:
 * GoogLeNet-CAM model on ImageNet: ```models/deploy_googlenetCAM.prototxt``` weights:[http://cnnlocalization.csail.mit.edu/demoCAM/models/imagenet_googlenetCAM_train_iter_120000.caffemodel]
 * VGG16-CAM model on ImageNet: ```models/deploy_vgg16CAM.prototxt``` weights:[http://cnnlocalization.csail.mit.edu/demoCAM/models/vgg16CAM_train_iter_90000.caffemodel]