# CAM

**Repository Path**: xuzuoteng/CAM

## Basic Information

- **Project Name**: CAM
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2017-07-06
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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)

### Pre-trained models:
* 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](http://places.csail.mit.edu/demo.html)):```models/deploy_alexnetplusCAM_places205.prototxt``` weights:[http://cnnlocalization.csail.mit.edu/demoCAM/models/alexnetplusCAM_places205.caffemodel]

### Usage Instructions:
* Install [caffe](https://github.com/BVLC/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](https://www.youtube.com/watch?v=fZvOy0VXWAI). The reimplementation in tensorflow is [here](https://github.com/jazzsaxmafia/Weakly_detector). The pycaffe wrapper of CAM is reimplemented at [here](https://github.com/gcucurull/CAM-Python).

### Reference:
```
@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}
}
```
### License:
The pre-trained models and the CAM technique are released for unrestricted use.

Contact [Bolei Zhou](http://people.csail.mit.edu/bzhou/) if you have questions.