Chainer implementation of Learning Deep Features for Discriminative Localization
This implementation uses ResNet50 architecture to infer the class activations.
Class activation maps are a simple technique to get the discriminative image regions used by a CNN to identify a specific class in the image. In other words, a class activation map (CAM) lets us see which regions in the image were relevant to this class. The authors of the paper show that this also allows re-using classifiers for getting good localization results, even when training without bounding box coordinates data. This also shows how deep learning networks already have some kind of a built in attention mechanism.This should be useful for debugging the decision process in classification networks.
Trained Chainer model for ResNet50 is stored in model.
To test,
python main.py -i <path-to-image>