Class-Activation-Mappings
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.
Dependencies
To run
Trained Chainer model for ResNet50 is stored in model.
To test,
python main.py -i <path-to-image>