I have written the files demo.m and generate_bbox.m in Python in order to be able to use the script without Matlab. In order to run it in Python one just need to run

python py_demo.py

and

python py_generate_bbox.py

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.

The framework of the Class Activation Mapping is as below:

Some predicted class activation maps are:

• GoogLeNet-CAM model on ImageNet: models/deploy_googlenetCAM.prototxt weights:[http://cnnlocalization.csail.mit.edu/demoCAM/models/imagenet_googleletCAM_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]
• GoogLeNet-CAM model on Places205: models/deploy_googlenetCAM_places205.prototxt weights:[http://cnnlocalization.csail.mit.edu/demoCAM/models/places_googleletCAM_train_iter_120000.caffemodel]
• AlexNet+-CAM on ImageNet:models/deploy_alexnetplusCAM_imagenet.prototxt weights:[http://cnnlocalization.csail.mit.edu/demoCAM/models/alexnetplusCAM_imagenet.caffemodel]
• AlexNet+-CAM on Places205 (used in the online demo):models/deploy_alexnetplusCAM_places205.prototxt weights:[http://cnnlocalization.csail.mit.edu/demoCAM/models/alexnetplusCAM_places205.caffemodel]

• Install caffe, compile the matcaffe (matlab wrapper for caffe), and make sure you could run the prediction example code classification.m.
• Clone the code from Github:

git clone https://github.com/metalbubble/CAM.git
cd CAM

• Download the pretrained network

sh models/download.sh

• Run the demo code to generate the heatmap: in matlab terminal,

demo

• Run the demo code to generate bounding boxes from the heatmap: in matlab terminal,

generate_bbox

The demo video of what the CNN is looking is here. The reimplementation in tensorflow is here.

@inproceedings{zhou2016cvpr,
    author    = {Zhou, Bolei and Khosla, Aditya and Lapedriza, Agata and Oliva, Aude and Torralba, Antonio},
    title     = {Learning Deep Features for Discriminative Localization},
    booktitle = {Computer Vision and Pattern Recognition},
    year      = {2016}
}

The pre-trained models and the CAM technique are released for unrestricted use.

Contact Bolei Zhou if you have questions.