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Visualize Saliency Maps & Class Activation Maps

Implement the Guided-ReLU visualization used in the paper:

And the class activation mapping (CAM) visualization proposed in the paper:

Saliency Maps

saliency-maps.py takes an image, and produce its saliency map by running a ResNet-50 and backprop its maximum activations back to the input image space. Similar techinques can be used to visualize the concept learned by each filter in the network.

Usage:

wget http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz
tar -xzvf resnet_v1_50_2016_08_28.tar.gz
./saliency-maps.py cat.jpg

Left to right:

  • the original cat image
  • the magnitude in the saliency map
  • the magnitude blended with the original image
  • positive correlated pixels (keep original color)
  • negative correlated pixels (keep original color)

CAM

CAM-resnet.py fine-tune a Preact-ResNet to have 2x larger last-layer feature maps, then produce CAM visualizations.

Usage:

  1. Fine tune or retrain the ResNet:
./CAM-resnet.py --data /path/to/imagenet [--load ImageNet-ResNet18-Preact.npz] [--gpu 0,1,2,3]

Pretrained and fine-tuned ResNet can be downloaded in the model zoo.

  1. Generate CAM on ImageNet validation set:
./CAM-resnet.py --data /path/to/imagenet --load ImageNet-ResNet18-Preact-2xGAP.npz --cam